JP3858379B2 - Cardiac output detection device and cardiac function diagnosis device - Google Patents

Description

【００８０】
数１においてｂは、マザー・ウエーブレットψ（ｘ）をトランスレート（平行移動）する際に用いるパラメータであり、一方、ａはスケール（伸縮）する際のパラメータである。したがって、数１においてウエーブレットψ（（ｘ−ｂ）／ａ）は、マザー・ウエーブレットψ（ｘ）をｂだけ平行移動し、ａだけ伸縮したものである。この場合、スケールパラメータａに対応してマザー・ウエーブレットψ（ｘ）の幅は伸長されるので、１／ａは周波数に対応するものとなる。なお、詳細な構成については後述する。
【００８１】
次に、２１は第１の周波数補正部であって脈波解析データＭＫＤに対して周波数補正を行う。上記した数１には周波数に対応する「１／ａ^1/2」の項があるが、異なる周波数領域間でデータを比較する場合には、この項の影響を補正する必要がある。第１の周波数補正部２１はこのために設けられたものであり、ウエーブレットデータＷＤに係数ａ^1/2を乗算して、脈波補正データＭＫＤ’を生成する。これにより、対応する各周波数に基づいて、周波数当たりのパワー密度が一定になるように補正を施すことができる。また、２３は第２の周波数補正部であって、第１の周波数補正部２１と同様に、周波数補正を施し、体動解析データＴＫＤから体動補正データＴＫＤ’を生成する。
【００８２】
ここで、第１のウエーブレット変換部２０の構成を図１１を用いて詳細に説明する。なお、第２のウエーブレット変換部２２は第１のウエーブレット変換部２０と同様に構成されているので、説明を省略する。
脈波波形ＭＨは、Ａ／Ｄ変換器によって脈波データＭＤに変換され、第１のウエーブレット変換部２０に供給されるようになっている。
この第１のウエーブレット変換部２０は、上記した数１の演算処理を行う構成であって、クロックＣＫが供給され、クロック周期で演算処理が行われるようになっており、マザー・ウエーブレットψ（ｘ）を記憶する基底関数記憶部Ｗ１、スケールパラメータａを変換するスケール変換部Ｗ２、バッファメモリＷ３、トランスレートを行う平行移動部Ｗ４および乗算部Ｗ５から構成される。なお、基底関数記憶部Ｗ１に記憶するマザー・ウエーブレットψ（ｘ）としては、ガボールウエーブレットの他、メキシカンハット、Ｈａａｒウエーブレット、Ｍｅｙｅｒウエーブレット、Ｓｈａｎｎｏｎウエーブレット等が適用できる。
【００８３】
まず、基底関数記憶部Ｗ１からマザー・ウエーブレットψ（ｘ）が読み出されると、スケール変換部Ｗ２はスケールパラメータａの変換を行う。ここで、スケールパラメータａは周期に対応するものであるから、ａが大きくなると、マザー・ウエーブレットψ（ｘ）は時間軸上で伸長される。この場合、基底関数記憶部Ｗ１に記憶されるマザー・ウエーブレットψ（ｘ）のデータ量は一定であるので、ａが大きくなると単位時間当たりのデータ量が減少してしまう。スケール変換部Ｗ２は、これを補うように補間処理を行うとともに、ａが小さくなると間引き処理を行って、関数ψ（ｘ／ａ）を生成する。このデータはバッファメモリＷ３に一旦格納される。
【００８４】
次に、平行移動部Ｗ４はバッファメモリＷ３からトランスレートパラメータｂに応じたタイミングで関数ψ（ｘ／ａ）を読み出すことにより、関数ψ（ｘ／ａ）の平行移動を行い関数ψ（ｘ−ｂ／ａ）を生成する。
【００８５】
次に、乗算部Ｗ４は、変数１／ａ^1/2、関数ψ（ｘ−ｂ／ａ）および脈波データＭＤを乗算して心拍単位でウエーブレット変換を行い、脈波解析データＭＫＤを生成する。この例において、脈波解析データＭＫＤは、０Ｈｚ〜０．５Ｈｚ、０．５Ｈｚ〜１．０Ｈｚ、１．０Ｈｚ〜１．５Ｈｚ、１．５Ｈｚ〜２．０Ｈｚ、２．０Ｈｚ〜２．５Ｈｚ、２．５Ｈｚ〜３．０Ｈｚ、３．０Ｈｚ〜３．５Ｈｚ、３．５Ｈｚ〜４．０Ｈｚといった周波数領域に分割されて出力される。
図１２は、脈波波形ＭＨの一部の期間について、脈波解析データＭＫＤを示したものである。この図において、期間ＴはピークＰ４の近傍にあり、脈波解析データＭＫＤは、期間Ｔを８分割した時間間隔で得られる。ところで、ウエーブレット変換においては、周波数分解能と時間分解能はトレードオフの関係にあるので、周波数分解能を犠牲にすれば、より短い時間間隔で脈波解析データを得ることもできる。
このようにして、生成された脈波解析データＭＫＤと体動解析データＴＫＤは、第１，第２の周波数補正部２１，２３によって周波数補正が施され、脈波補正データＭＫＤ’、体動補正データＴＫＤ’として出力される。
【００８６】
１−２．体動除去部
次に、体動除去部１１は、脈波補正データＭＫＤ’から体動補正データＴＫＤ’を減算して体動除去脈波データＭＫＤ’’を生成する。この点について、具体的に説明する。なお、以下の説明では、使用者が手でコップを持ち上げた後、これを元の位置に戻した場合を想定する。この場合、図１３（ａ）に示す脈波波形ＭＨが脈波検出用センサユニット１３０によって検出され、また、同時に図１３（ｂ）に示す体動波形ＭＨｔが波形処理部１０によって検出されたものとする。
【００８７】
ここで、体動波形ＭＨｔは、時刻Ｔ１から増加しはじめ、時刻Ｔ２で正のピークとなり、その後、次第に減少して時刻Ｔ２でレベル０を通過し、時刻Ｔ３で負のピークに達し、時刻Ｔ４でレベル０に戻っている。ところで、体動波形ＴＨは加速度センサ２１によって検出されるため、時刻Ｔ３は使用者がコップを最大に持ち上げた時刻に対応し、時刻Ｔ１は持上開始時刻に対応し、また、時刻Ｔ４は持上終了時刻に対応する。したがって、時刻Ｔ１から時刻Ｔ４までの期間が体動が存在する期間となる。なお、図１４（ｃ）は仮に体動がなかったとした場合の脈波波形ＭＨ’である。また、この例において、脈波波形ＭＨの基本波周波数は、１．３Ｈｚとなっている。
【００８８】
ここで、図１４に期間Ｔｃ（図１３参照）における脈波補正データＭＫＤ’を示し、図１５に期間Ｔｃにおける体動補正データＴＫＤ’を示す。この図から、体動波形ＴＨには、０．０Ｈｚ〜１．０Ｈｚの周波数領域において比較的大きなレベルの周波数成分が存在していることが判る。脈波補正データＭＫＤ’と体動補正データＴＫＤ’が、体動除去部１１に供給されると、体動除去部１１は、脈波補正データＭＫＤ’から体動補正データＴＫＤ’を減算して、図１６に示す体動成分が除去された体動除去脈波データＭＫＤ''を生成する。これにより、体動がある場合でもその影響をキャンセルすることが可能となる。
【００８９】
１−３．判定部
次に、判定部１１’は、体動波形ＴＨを予め定められた閾値と比較して、体動の有無を示す制御信号Ｃを生成し、これを波形処理部１０、第２のウエーブレット変換部２２および第２の周波数補正部２３に供給する。これによって、体動が無い場合には、波形処理部１０、第２のウエーブレット変換部２２および第２の周波数補正部２３の各動作が停止され、演算処理時間の低減、消費電力の低減、およびＳＮ比の向上が図られる。
【００９０】
１−４．心拍数検出部
次に、心拍数検出部１２は、体動除去脈波データＭＫＤ''に基づいて心拍数を算出する。この場合、心拍数検出部１２は、体動除去脈波データＭＫＤ''に基づいて１拍中の最大ピークＰmaxを特定する。脈波波形ＭＨ’の最大ピークＰmaxでは、高域周波数成分が大きくなるので、予め高域周波数成分に対応する閾値を定めておき、体動除去脈波データＭＫＤ''と閾値を比較して最大ピークＰmaxを特定する。そして、ある最大ピークＰmaxと次の最大ピークＰmax間の時間間隔Ｔを求め、６０／Ｔから心拍数ＨＲを算出する。
【００９１】
１−５．駆出期間検出部
次に、駆出期間検出部１３は、第１実施形態と同様に構成してもよいが、この例にあっては、体動除去脈波データＭＫＤ''に基づいて、最小ピークＰminを特定するとともに、最大ピークＰmaxの後、第２番目に現れる負のピークＰ４（ノッチ）を特定する。この場合には、最小ピークＰminに対応する周波数成分とピークＰ４に対応する周波数成分を閾値として予め記憶しておき、これらの閾値と体動除去脈波データＭＫＤ''を比較することによって、最小ピークＰminとピークＰ４を特定し、それらの間の時間間隔を駆出期間ＥＤとして算出する。
【００９２】
１−６．一回拍出量算出部
次に、一回拍出量算出部１４は、駆出期間ＥＤにおける各周波数領域の体動除去脈波データＭＫＤ''を加算して、当該期間におけるエネルギー量Ｅを求め、これに基づいて収縮期面積Ｓを演算する。
【００９３】
ところで、最小ピークＰminからピークＰ４までの脈波波形は、比較的急峻な山状の波形であることから、そこに含まれる周波数成分は高周波領域のものがほとんどである。したがって、低域周波数領域（例えば０Ｈｚ〜１Ｈｚ）の体動除去脈波データＭＫＤ''は、ノイズ成分であると考えることができる。そこで、駆出期間ＥＤにおける全ての周波数領域の体動除去脈波データＭＫＤ''を加算するのではなく、その一部を加算してエネルギー量Ｅを求めるようにしてもよい。
【００９４】
例えば、図１７に示すように、体動除去脈波データＭＫＤ''が得られたとすると、０Ｈｚ〜１Ｈｚの周波数領域にはノイズ成分が多いので、１Ｈｚ〜４Ｈｚの周波数領域にある体動除去脈波データＭＫＤ''を加算すればよい。各周波数領域の体動除去脈波データＭＫＤ''をＭｎｍで表すものとすれば、この場合のエネルギー量Ｅは次式で与えられる。
【数２】

【００９５】
次に、一回拍出量ＳＶは次式で算出される。

ただし、Ｋｅはエネルギ量Ｅと面積Ｓとの間の変換係数である。
【００９６】
このように第２実施形態にあっては、ウエーブレット変換を用いて、収縮期面積Ｓを算出したので、脈波波形のノイズ成分を除去しつつ、一回拍出量ＳＶを求めることができるので、正確な心拍出量ＣＯを算出することが可能となる。
【００９７】
Ｄ．第３実施形態
上述した第２実施形態は、ウエーブレット変換によって周波数解析を行うために、第１のウエーブレット変換部２０、第１の周波数補正部２１、第２のウエーブレット変換部２２、第２の周波数補正部２３を用いた。これに対して、第３実施形態は、第２のウエーブレット変換部２２、第２の周波数補正部２３を省略する点で、第２実施形態と相違する。
【００９８】
第３実施形態に係わる心機能診断装置の外観構成は、図３に示す第１実施形態の外観構成と同様であるのでここでは説明を省略し、その電気的構成について説明する。図１８は、第３実施形態に係わる心機能診断装置のブロック図である。図において、体動除去部１１によって体動成分が除去された体動除去脈波波形ＭＨ’を生成すると、第１のウエーブレット変換部２０は、体動除去脈波波形ＭＨ’にウエーブレット変換を施す。第１の周波数補正部２１は第１のウエーブレット変換部１６の出力に周波数補正を施して、体動除去脈波データＭＫＤ''を生成する。
【００９９】
この場合、第１の周波数補正部２１の出力は、図１０に示す体動除去部１１の出力と等価である。すなわち、ウエーブレット変換は線形であるから、処理の順番を入れ替えても良いため、体動除去をアナログ信号で行った後にウエーブレット変換することと（第３実施形態）、ウエーブレット変換された脈波補正データＭＫＤ’と体動補正データＴＫＤ’に基づいて体動除去を行うことは（第２実施形態）、等価だからである。
なお、判定部１１’は、第１実施形態と同様であり、また、心拍数検出部１２、駆出期間検出部１３、一回拍出量算出部１４、心拍出量算出部１５、評価部１６および表示部１７は、第２実施形態と同様であるから、説明を省略する。
【０１００】
このように、第３実施形態にあっては、第２のウエーブレット変換部２２、第２の周波数補正部２３を省略しても、心拍出量ＣＯを算出することができるので、より簡易な構成で心機能の状態を診断することができる。
【０１０１】
Ｅ．第４実施形態
第１〜第３実施形態においては、加速度センサ１３０によって体動波形ＴＨを検出し、脈波波形ＭＨと体動波形ＴＨとを比較して、脈波波形ＭＨの周波数成分に含まれている体動成分をキャンセルして、心拍数ＨＲと駆出期間ＥＤを算出し、これらに基づいて心機能の状態を診断した。しかし、加速度センサ１３０および波形処理部１０等が必要になるので、構成が複雑なる。第４実施形態は、この点に鑑みてなされたものであり、簡易な構成で、体動があっても正確に心機能の状態を診断することができる心機能診断装置を提供するものである。
【０１０２】
第４実施形態に係わる心機能診断装置の外観構成は、図３に示す第１実施形態の外観構成と同様であるのでここでは説明を省略し、その電気的構成について説明する。図１９は第４実施形態に係わる心機能診断装置のブロック図であり、加速度センサ１３０、波形処理部１０、第２のウエーブレット変換部２２、および第２の周波数補正部２３が省略されている点および体動除去部１１の内部構成を除いて、図１１に示す第２実施形態に係わる心機能診断装置１と同じである。以下、相違点について説明する。
体動除去部１１は、脈波補正データＭＫＤ’から体動成分を分離除去して体動分離脈波データＴＢＤを生成する。ここで、体動除去部１１は、以下に述べる体動の性質を利用している。
【０１０３】
体動は、腕の上下動や走行時の腕の振り等によって生じるが、日常生活においては、人体を瞬間的に動かすことはほとんどない。このため、日常生活では、体動波形ＴＨの周波数成分はそれほど高くなく、０Ｈｚ〜１Ｈｚの範囲にあるのが通常である。この場合、脈波波形ＭＨの基本波周波数は、１Ｈｚ〜２Ｈｚの範囲にあることが多い。したがって、日常生活において、体動波形ＴＨの周波数成分は脈波波形ＭＨの基本波周波数よりも低い周波数領域にある。
【０１０４】
一方、ジョギング等のスポーツ中にあっては、腕の振り等の影響があるため、体動波形ＴＨの周波数成分が幾分高くなるが、運動量に応じて心拍数が増加するため、脈波波形ＭＨの基本波周波数も同時に高くなる。このため、スポーツ中においても、体動波形ＴＨの周波数成分は脈波波形ＭＨの基本波周波数よりも低い周波数領域にあるのが通常である。
【０１０５】
体動除去部１１は、この点に着目して体動成分を分離するものであり、脈波波形ＭＨの基本波成分よりも低い周波数領域を無視するように構成されている。この場合には、脈波波形ＭＨの基本波成分より高い周波数領域に体動成分が存在すると心機能の検出精度が低下する。しかしながら、上述したように体動成分は脈波波形ＭＨの基本波成分よりも低い周波数領域にある確率が高いので、高い精度で心機能の状態を診断することができる。
【０１０６】
図２０は、体動除去部１１の詳細なブロック図である。波形整形部３０１は脈波波形ＭＨに波形整形を施して、脈波波形ＭＨと同期したリセットパルスを生成する。カウンタ３０２は図示せぬクロックパルスを計数し、前記リセットパルスによってカウント値がリセットされるようになっている。また、平均値算出回路３０３は、カウンタ３０２のカウント値の平均値を算出する。この場合、平均値算出回路３０３によって算出される平均値は、脈波波形ＭＨの平均周期に対応する。したがって、平均値を参照すれば、脈波波形ＭＨの基本波周波数を検知できる。
【０１０７】
次に、置換回路３０４は、前記平均値に基づいて、脈波波形ＭＨの基本波周波数を含む周波数領域を特定する。例えば、前記平均値が０．７１秒を示す場合には、基本波周波数は１．４Ｈｚとなるので、特定される周波数領域は１Ｈｚ〜１．５Ｈｚとなる。この後、置換回路３０４は、特定周波数領域未満の周波数領域について、脈波補正データＭＫＤ’を「０」に置換して体動分離脈波データＴＢＤを生成する。これにより、脈波波形ＭＨの基本波周波数より低い周波数領域の成分は、無視される。この場合、体動成分とともに脈波成分も「０」に置換されてしまうが、脈波波形ＭＨの特徴的な部分は基本波周波数よりも高域の周波数領域に存在するため、「０」に置換しても脈波波形には影響をほとんど与えない。
【０１０８】
例えば、脈波検出用センサユニット１３０によって、図１３（ａ）に示す脈波波形ＭＨ（基本波周波数１．３Ｈｚ）が検出されたものとすれば、期間Ｔｃの脈波補正データＭＫＤ’は、図１４に示すものとなる。
この場合、置換回路１９４によって特定される周波数領域は１．０Ｈｚ〜１．５Ｈｚとなるので、置換の対象となる周波数領域は、０．５Ｈｚ〜１．０Ｈｚに対応するＭａ１２〜Ｍａ８２と０Ｈｚ〜０．５Ｈｚに対応するＭａ１１〜Ｍａ８１となる。したがって、脈波補正データＭＫＤ’のデータＭａ１２〜Ｍａ８２，Ｍａ１１〜Ｍａ８１は「０」に置換され、図２１に示す体動除去脈波データＭＫＤ’’が生成される。
こうして生成された体動除去脈波データＭＫＤ’’に基づいて、図１９に示す心拍数検出部１２と駆出期間検出部１３は心拍数ＨＲと駆出期間ＥＤを各々検出する。
【０１０９】
このように第４実施形態によれば、体動成分は脈波波形ＭＨの基本波周波数成分よりも低い周波数領域に存在することが確率的に高いという体動の性質を巧みに利用して体動成分を除去した。このため、第１〜第３実施形態で必要とされた加速度センサ１３０や波形処理部１０といった構成を省略することができ、しかも体動がある場合でも正確に心機能の状態を診断することが可能となる。
【０１１０】
Ｆ．第５実施形態
第５実施形態は、第１実施形態で説明した一回拍出量算出部１４の変形例に係わるものであり、他の構成部分は、第１実施形態と同様である。第５実施形態の一回拍出量算出部１４には以下の態様がある。
【０１１１】
１．第１の態様
まず、第１の態様では、駆出期間ＥＤにおける体動除去脈波波形ＭＨ’の各ピークＰ１〜Ｐ４の血圧値とそれらの発生時刻から一回拍出量ＳＶを算出する。体動除去脈波波形ＭＨ’が図６に示すものであり、Ｐ０からＰ４までの期間を駆出期間ＥＤとするならば、一回拍出量ＳＶは次式で算出される。

この例は直線近似によって収縮期面積Ｓを算出し、これに基づいて一回拍出量ＳＶを算出する。
したがって、駆出期間ＥＤの全てのサンプルについて、体動除去脈波波形ＭＨ’を加算して面積Ｓを算出する方法と比較して、演算量を削減することができる。
【０１１２】
２．第２の態様
次に、第２の態様では、駆出期間ＥＤと心拍数ＨＲから一回拍出量ＳＶを求める。ここでは、収縮期面積法における収縮期面積Ｓを駆出期間ＥＤと心拍数ＨＲから算出する。
【０１１３】
脈波波形ＭＨには、個人差や同一個人であっても個人内差が存在するが、多数の実測データを計測することによって、ある心拍数ＨＲにおける脈波波形ＭＨのおおよその形を特定することができる。そして、脈波波形ＭＨが特定されると、駆出期間ＥＤに対応する面積Ｓを求めることができる。
【０１１４】
図２２は、第２の態様に係わる一回拍出量検出部１４のブロック図である。１４０は一回拍出量テーブルであって、そこには、心臓の駆出期間ＥＤと心拍数ＨＲとに対応付けて収縮期面積Ｓが格納されている。この一回拍出量テーブル１４０は、心拍数ＨＲ毎に設けられた複数のテーブルＴＢ１，ＴＢ２，…ＴＢｎから構成されており、各テーブルＴＢ１，ＴＢ２，…ＴＢｎには、駆出期間ＥＤに対応付けた収縮期面積Ｓが格納されている。なお、これらのテーブル内容は、多数の実測データによって生成される。また、１４１は一回拍出量テーブル１４０の後段に設けられた乗算器であって、係数Ｋｓｖと収縮期面積Ｓとを乗算して一回拍出量ＳＶを算出する。
【０１１５】
以上の構成により、駆出期間ＥＤと心拍数ＨＲが、一回拍出量テーブル１４０に供給されると、一回拍出量検出部１４０は、心拍数ＨＲに対応する１枚のテーブルＴＢを特定する。この後、このテーブルＴＢから、駆出期間ＥＤに対応する収縮期面積Ｓが読み出されると、乗算器１４１は一回拍出量ＳＶを演算する。
【０１１６】
このように、第２の態様にあっては、駆出期間ＥＤと心拍数ＨＲのみから一回拍出量ＳＶを算出することができるので、簡易な構成で短時間のうちに一回拍出量ＳＶを求めることができる。
【０１１７】
なお、上述した各テーブルＴＢ１，ＴＢ２，…ＴＢｎに、駆出期間ＥＤに対応付けた一回拍出量ＳＶを格納しておけば、乗算器１４１を省略することができる。この場合には、収縮期面積Ｓの替わりにＳ＊Ｋｓｖを各テーブルＴＢ１，ＴＢ２，…ＴＢｎに格納しておけばよい。
【０１１８】
さらに、上述した各テーブルＴＢ１，ＴＢ２，…ＴＢｎに、駆出期間ＥＤに対応付けた心拍出量ＣＯを格納しておけば、駆出期間ＥＤと心拍数ＨＲから心拍出量ＣＯを直接求めることができる。図２３は、心拍出量テーブル１４０’を示した図である。この場合には、収縮期面積Ｓの替わりにＳ＊Ｋｓｖ＊ＨＲを各テーブルＴＢ１，ＴＢ２，…ＴＢｎに格納しておけばよい。
【０１１９】
Ｇ．第６実施形態
上述した第１〜第５実施形態の心機能診断装置１は、収縮期面積法を適用して、駆出期間ＥＤにおける脈波波形の面積Ｓにある係数Ｋsvを乗じて一回拍出量ＳＶを算出した。ここで、係数Ｋsvは、各被験者によって厳密には異なるものである。このため、正確な一回拍出量ＳＶを算出するには、収縮期面積法で得られた一回拍出量ＳＶを補正することが望ましい。
そこで、第６実施形態では、図５、１０、１８および１９に示す第１〜第５実施形態の一回拍出量算出部１４と心拍出量算出部１５との間に、一回拍出量補正部２４を設けて、一回拍出量ＳＶの補正を行っている。
【０１２０】
図２４は、本実施形態に係わる一回拍出量補正部２４のブロック図である。図に示すように一回拍出量補正部２４は、補正係数ＫＨを算出する補正係数算出部２４０、補正係数ＫＨを記憶する補正係数メモリ２４１、および乗算部２４２から構成されている。
【０１２１】
補正係数算出部２４０には、熱色素希釈法等によって精密に計測される基準一回拍出量ＳＶｒが外部機器から供給されるとともに、一回拍出量算出部１４によって算出された一回拍出量ＳＶが供給されるようになっている。補正係数算出部２４０は、除算器で構成され、被験者が操作ボタンを操作して校正モードにすると、ＳＶｒ／ＳＶを補正係数ＫＨとして算出する。算出された補正係数ＫＨは補正係数メモリ２４１に記憶され、通常の測定モードにおいてそこから読み出され、使用される。乗算器２４２は、一回拍出量ＳＶと補正係数ＫＨを乗算して、補正済一回拍出量ＳＶ’を生成する。
【０１２２】
このように本実施形態によれば、校正モードにおいて補正係数ＫＨを算出し、通常の測定モードにおいて補正係数ＫＨを用いて補正済一回拍出量ＳＶ’を算出したので、より精密な心拍出量ＣＯを求めることができる。
【０１２３】
また、本実施形態の心機能診断装置１は、例えば、病院内の健康管理や、リラビリ中の健康管理に好適である。より具体的には、心疾患等の手術後に熱色素希釈法によって正確な基準一回拍出量ＳＶｒを計測すると同時に、携帯型の心機能診断装置１で一回拍出量ＳＶを計測する。そして、これらの計測結果から算出される補正係数ＫＨを記憶しておき、通常の測定モードにおいて補正係数ＫＨを用いて精密な心拍出量ＣＯを求める。これにより、患者はリハビリテーションによって健常に戻っていく過程で正確な心拍出量ＣＯに基づく心機能の診断を受けることができる。
【０１２４】
Ｈ．第７実施形態
第７実施形態は、評価指標Ｘの基準となる閾値を体表面積に応じて可変するものであり、評価部１６の構成を除いて、第１〜第６実施形態の構成と同様である。以下、相違点である評価部１６について説明する。
図２５は、第７実施形態に係わる評価部１６のブロック図である。１６０は体表面積算出部であって、そこには体重Ｗ（ｋｇ）と身長Ｈ（ｃｍ）が入力されるようになっており、これらに基づいて体表面積ＴＳが算出されるようになっている。この例では、体表面積ＴＳをデュボイス式と呼ばれる周知の実験式によって算出している。実験式を以下に示す。
ＴＳ＝Ｗ^0.425×Ｈ^0.725×７１．８４
【０１２５】
次に、１６１は閾値テーブルであって、そこには、体表面積ＴＳと心拍数ＨＲに対応付けられて、評価指標Ｘを生成するための閾値Ｒ１，Ｒ２が格納されている。この閾値テーブル１６１は、複数のテーブルＴＢ１’，ＴＢ２’，…ＴＢｎから構成されており、各テーブルには、心拍数ＨＲに対応付けられて閾値Ｒ１，Ｒ２が格納されている。そして、体表面積ＴＳが供給されると、各テーブルの中から、体表面積ＴＳに応じた１枚のテーブルが選択されるようになっている。したがって、この閾値テーブル１６１を参照すれば、体表面積ＴＳと心拍数ＨＲに応じた閾値Ｒ１，Ｒ２を得ることができる。
【０１２６】
次に、１６２は、比較部であって、閾値Ｒ１，Ｒ２と心拍出量ＣＯとを比較して、評価指標Ｘを生成する。
【０１２７】
上述したように、体表面積ＴＳによって閾値Ｒ１，Ｒ２を可変するようにしたのは、以下の理由による。一般に、体表面積ＴＳが大きい人は、体が大型で心拍出量ＣＯが多いが、逆に、体表面積ＴＳが小さい人は、体が小型で心拍出量ＣＯが少なくなる傾向にある。このため、両者の心拍出量ＣＯを同一の閾値Ｒ１，Ｒ２で評価しても、個々人に応じた心機能の評価を行うのが難しく、体表面積ＴＳに応じた評価指標Ｘを用いることによって、個々人の体型に応じた心機能の評価を行うことができるからである。
【０１２８】
また、心拍数ＨＲに応じて閾値Ｒ１，Ｒ２を可変するようにしたのは、以下の理由による。ランニング等の運動を行うと、骨格筋で大量の酸素が消費されるため、心拍数ＨＲが増加し、心拍出量ＣＯが増加する。換言すれば、運動強度に応じて心拍数ＨＲと心拍出量ＣＯは、変動するのである。したがって、心拍数ＨＲに応じた評価指標Ｘを用いることによって、被験者の運動強度が変化する際にも連続して心機能を評価することができるからである。
【０１２９】
したがって、本実施形態の心機能診断装置１を使用すれば、被験者の体型や動的に変化する心拍数ＨＲに応じて、閾値Ｒ１，Ｒ２を自動的に変更することができ、これにより、日常生活において、心機能を連続的に評価することが可能となる。
【０１３０】
Ｉ．変形例
本発明は、上述した実施形態に限定されないことは勿論であり、例えば、以下に述べる各種の変形が可能である。
（１）上述した第２〜第４実施形態では、第１の周波数補正部２１あるいは第２の周波数補正部２３を、異なる周波数領域でエネルギーを比較するために用い、補正結果を閾値と比較して最大ピークＰmax等を求めた。この場合、閾値自体を周波数補正を考慮したものにして、各周波数補正部を省略するようにしてもよい。
【０１３１】
（２）上述した第２〜第４実施形態で行ったウエーブレット変換はフィルタバンクを用いて行ってもよい。フィルタバンクの構成例を図２６に示す。図において、フィルタバンクは３段で構成されており、その基本単位は、高域フィルタ１Ａおよびデシメーションフィルタ１Ｃと、低域フィルタ１Ｂおよびデシメーションフィルタ１Ｃである。高域フィルタ１Ａと低域フィルタ１Ｂは、所定の周波数帯域を分割して、高域周波数成分と低域周波数成分を各々出力するようになっている。この例にあっては脈波データＭＤの周波数帯域として０Ｈｚ〜４Ｈｚを想定しているので、一段目の高域フィルタ１Ａの通過帯域は２Ｈｚ〜４Ｈｚに設定され、一方、一段目の低域フィルタ１Ｂの通過帯域は０Ｈｚ〜２Ｈｚに設定される。また、デシメーションフィルタ１Ｃは、１サンプルおきにデータを間引く。
こうして生成されたデータが次段に供給されると、周波数帯域の分割とデータの間引きが繰り返され、最終的には、０Ｈｚ〜４Ｈｚの周波数帯域を８分割したデータＭ１〜Ｍ８が得られる。
【０１３２】
また、高域フィルタ１Ａと低域フィルタ１Ｂとは、その内部に遅延素子(Ｄフリップフロップ)を含むトランスバーサルフィルタで構成すればよい。ところで、人の脈拍数は４０〜２００の範囲にあり、脈波波形ＭＨの基本波周波数は、生体の状態に応じて刻々と変動する。この場合、基本波周波数に同期して、分割する帯域を可変することができれば、動的な生体の状態に追従した情報を得ることができる。そこで、トランスバーサルフィルタに供給するクロックを脈波波形ＭＨとさせることによって、分割する帯域を適応的に可変してもよい。
【０１３３】
また、脈波解析データＭＫＤのうち、脈波波形ＭＨの特徴を表す代表的な周波数成分は、基本波、第２高調波および第３高調波の各周波数成分である。したがって、フィルタバンクの出力データＭ＊１〜Ｍ＊８のうち一部を用いて脈象を判定するようにしてもよい。この場合、上述したようにフィルタバンクを脈波波形ＭＨに同期するように構成すれば、高域フィルタ１Ａ、低域フィルタ１Ｂおよびデシメーションフィルタ１Ｃの一部を省略して、構成を簡易なものにすることができる。
【０１３４】
（３）上述した第１実施形態における体動除去部１１を第４実施形態で説明したウエーブレット変換で行ってもよい。この場合は、体動が除去されたウエーブレットに数３に示す逆ウエーブレットを施して波形の再合成を行い、再合成された脈波波形に基づいて、心拍数ＨＲと駆出期間ＥＤを算出すればよい。
【数３】

【０１３５】
また、逆ウエーブレットは逆フィルタバンクを用いて構成すればよい。この場合、逆ウエーブレット変換部は、図２７に示すフィルタバンクで構成してもよい。図において、フィルタバンクは３段で構成されており、その基本単位は、高域フィルタ２Ａおよび補間フィルタ２Ｃと、低域フィルタ１Ｂおよび補間フィルタ２Ｃと、加算器２Ｄである。高域フィルタ２Ａと低域フィルタ２Ｂは、所定の周波数帯域を分割して、高域周波数成分と低域周波数成分を各々出力するようになっている。また、補間フィルタ２Ｃは、２サンプル毎に１サンプルを内挿補間する。
【０１３６】
ここで、波形を再現するためには、図２６に示すフィルタバンクと図２７に示すフィルタバンクに完全再構成フィルタバンクを用いる必要がある。この場合、高域フィルタ１Ａ，２Ａおよび低域フィルタ１Ｂ，２Ｂの特性は、以下の関係があることが必要である。
Ｈ0(-Z)Ｆ0(Z)＋Ｈ1(-Z)Ｆ1(Z)＝０
Ｈ0(Z)Ｆ0(Z)＋Ｈ1(-Z)Ｆ1(Z)＝２Z^-L
【０１３７】
また、高域フィルタ２Ａと低域フィルタ２Ｂとは、その内部に遅延素子(Ｄフリップフロップ)を含むトランスバーサルフィルタで構成すればよい。なお、ウエーブレット変換部１０で使用するフィルタバンクを、脈波波形ＭＨの基本波周波数に同期して、分割する帯域を可変するため、供給するクロックを脈波波形ＭＨと同期させた場合には、このクロックを高域フィルタ２Ａと低域フィルタ２Ｂに供給してもよい。
【０１３８】
（４）また、上述した各実施形態においては、表示部１７を告知手段ｆ８の一例として説明したが、装置から人間に対して告知をするための手段としては以下説明するようなものが挙げられる。これら手段は五感を基準に分類するのが適当かと考えられる。なお、これらの手段は、単独で使用するのみならず複数の手段を組み合わせても良いことは勿論である。そして、以下説明するように、例えば視覚以外に訴える手段を用いれば、視覚障害者であっても告知内容を理解することができ、同様に、聴覚以外に訴える手段を用いれば聴覚障害者に対して告知を行うことができ、障害を持つ使用者にも優しい装置を構成できる。
【０１３９】
まず、聴覚に訴える告知手段としては、心機能の分析・診断結果などを知らせるための目的、あるいは警告の目的でなされるものなどがある。例えば、ブザーの他、圧電素子、スピーカが該当する。また、特殊な例として、告知の対象となる人間に携帯用無線呼出受信機を持たせ、告知を行う場合にはこの携帯用無線呼出受信機を装置側から呼び出すようにすることが考えられる。また、これらの機器を用いて告知を行うにあたっては、単に告知するだけではなく、何らかの情報を一緒に伝達したい場合も多々ある。そうした場合、伝えたい情報の内容に応じて、以下に示す音量等の情報のレベルを変えれば良い。例えば、音高、音量、音色、音声、音楽の種類（曲目など）である。
【０１４０】
次に、視覚に訴える告知手段が用いられるのは、装置から各種メッセージ，測定結果を知らせる目的であったり、警告をするためであったりする。そのための手段として以下のような機器が考えられる。例えば、ディスプレイ装置、ＣＲＴ（陰極線管表示装置），ＬＣＤ（液晶表示ディスプレ）、プリンタ、Ｘ−Ｙプロッタ、ランプなどがある。なお、特殊な表示装置として眼鏡型のプロジェクターがある。また、告知にあたっては以下に示すようなバリエーションが考えられる。例えば、数値の告知におけるデジタル表示，アナログ表示の別、グラフによる表示、表示色の濃淡、数値そのまま或いは数値をグレード付けして告知する場合の棒グラフ表示、円グラフ、フェイスチャート等である。フェイスチャートとしては、例えば、図２８に示すものがある。
【０１４１】
次に、触覚に訴える告知手段は、警告の目的で使用されることがあると考えられる。そのための手段として以下のようなものがある。まず、腕時計等の携帯機器の裏面から突出する形状記憶合金を設け、この形状記憶合金に通電するようにする電気的刺激がある。また、腕時計等の携帯機器の裏から突起物（例えばあまり尖っていない針など）を出し入れ可能な構造としてこの突起物によって刺激を与える機械的刺激がある。
【０１４２】
次に、嗅覚に訴える告知手段は、装置に香料等の吐出機構を設けるようにして、告知する内容と香りとを対応させておき、告知内容に応じた香料を吐出するように構成しても良い。ちなみに、香料等の吐出機構には、マイクロポンプなどが最適である。
【０１４３】
（５）上述した各実施形態においては、脈波検出手段ｆ１の一例として脈波検出用センサユニット１３０を取りあげ説明したが、本発明はこれに限定されるものではなく、脈動を検出できるものであれば、どのようなものであってもよい。
【０１４４】
例えば、脈波検出用センサユニット１３０は反射光を利用したものであったが、透過光を利用したものであってもよい。ところで、波長領域が７００ｎｍ以下の光は、指の組織を透過しにくい傾向がある。このため、透過光を利用する場合は、発光部から波長が６００ｎｍ〜１０００ｎｍの光を照射し、照射光を組織→血管→組織の順に透過させ、この透過光の光量変化を検出する。透過光は血液中のヘモグロビンの吸収を受けるので、透過光の光量変化を検出することによって、脈波波形を検出することができる。
【０１４５】
この場合、発光部には、ＩｎＧａＡｓ系（インジウム−ガリウム−砒素）やＧａＡｓ系（ガリウム−砒素）のレーザー発光ダイオードが好適である。ところで、波長が６００ｎｍ〜１０００ｎｍの外光は組織を透過し易いので、受光部に外光が入射すると脈波信号のＳ／Ｎが劣化してしまう。そこで、発光部から偏光したレーザー光を照射し、透過光を偏光フィルタを介して受光部で受光するようにしてもよい。これにより、外光の影響を受けることなく、脈波信号を良好なＳ／Ｎ比で検出することができる。
【０１４６】
この場合には、図２９（ａ）に示すように、発光部４００を締着具１４５の締め付け側に設け、時計本体側には受光部４０１を設けている。この場合、発光部２００から照射された光は、血管１４３を透過した後、橈骨４０２と尺骨４０３の間を通って、受光部２０１に達する。なお、透過光を用いる場合には、照射光は組織を透過する必要があるため、組織の吸収を考慮すると、その波長は６００ｎｍ〜１０００ｎｍであることが望ましい。
【０１４７】
また、同図（ｂ）は検出部位を耳朶とする例である。把持部材４０４と把持部材４０５は、バネ４０７で付勢され、軸４０６を中心に回動できるようになっている。また、把持部材４０４と把持部材４０５には、発光部４００と受光部４０１が設けられている。この脈波検出部を用いる場合には、耳朶を把持部材４０４と把持部材４０５で把持して脈波を検出する。なお、反射光を用いる場合には、同図（ｃ）に示すように指尖部から脈波波形ＭＨを検出するようにしてもよい。
【０１４８】
次に、光電式脈波センサを眼鏡と組み合わせた使用態様を説明する。なお、この眼鏡の形態では、使用者に対する告知手段としての表示装置も一緒に組み込まれた構造になっている。したがって、脈波検出部として以外に表示装置としての機能についても併せて説明する。
図３０は、脈波検出部が接続された装置を眼鏡に取り付けた様子を表わす斜視図である。図のように、装置本体は本体７５ａと本体７５ｂに分かれ、それぞれ別々に眼鏡の蔓７６に取り付けられており、これら本体が蔓７６内部に埋め込まれたリード線を介して互いに電気的に接続されている。
【０１４９】
本体７５ａは表示制御回路を内蔵しており、この本体７５ａのレンズ７７側の側面には全面に液晶パネル７８が取り付けられ、また、該側面の一端には鏡７９が所定の角度で固定されている。さらに本体７５ａには、光源（図示略）を含む液晶パネル７８の駆動回路と、表示データを作成するための回路が組み込まれている。この光源から発射された光は、液晶パネル７８を介して鏡７９で反射されて、眼鏡のレンズ７７に投射される。また、本体７５ｂには、装置の主要部が組み込まれており、その上面には各種のボタンが設けられている。なお、これらボタン８０，８１の機能は装置毎に異なる。また。光電式脈波センサを構成するＬＥＤ３２およびフォトトランジスタ３３（図４を参照）はパッド８２，８３に内蔵されると共に、パッド８２，８３を耳朶へ固定するようになっている。これらのパッド８２，８３は、本体７５ｂから引き出されたリード線８４，８４によって電気的に接続されている。
【０１５０】
次に、圧力センサによって脈波波形ＭＨを検出する例を説明する。図３１（ａ）は圧力センサを用いた心機能診断装置の外観構成を示す斜視図である。この図に示すように、心機能診断装置１には、一対のバンド１４４，１４４が設けられており、その一方の締着具１４５の締め付け側には、圧力センサ１３０’の弾性ゴム１３１が突出して設けられている。締着具１４５を備えるバンド１４４は、圧力センサ１３０による検出信号を供給するべくＦＰＣ（Flexible Printed Circuit）基板を軟性プラスチックで被覆した構造（詳細は図示省略）となっている。
【０１５１】
また、使用時においては、図３１（ｂ）に示すように、締着具１４５に設けられた弾性ゴム１３１が橈骨動脈１４３の近傍に位置するべく、腕時計１４６が被験者の左腕１４７に巻回される。このため、脈波を恒常的に検出することが可能となる。なお、この巻回については通常の腕時計の使用状態と何等変わることがない。こうして弾性ゴム１３１が、被験者の橈骨動脈１４３近傍に押圧されると、該動脈の血流変動（すなわち脈波）が弾性ゴム１３１を介して圧力センサ１３０’に伝達され、圧力センサ１３０’はこれを血圧として検知する。
【０１５２】
（６）上述した第１実施形態においてＦＦＴを用いて心拍数ＨＲを求めるようにしてもよい。この場合、心拍数ＨＲは、基本周波数ｆを測定し、ｆ・６０を算出すればよい。
【０１５３】
（７）上述した各実施形態において、心拍数ＨＲに対応して心拍出量ＣＯを格納する個人データベースを設けるようにしてもよい。この場合、個人データベースに心拍出量ＣＯを自動的に格納するにしておき、操作ボタンを操作すると過去の心拍出量ＣＯが表示部１７に表示されるにしておけば、被験者は心拍出量ＣＯのトレンドを知ることができる。例えば、ランニング等のトレーニングを行う場合には、トレーニングの効果をこのトレンドによって知ることができる。また、心疾患のリハビリテーションの場合には、心機能の回復の程度をトレンドによって知ることができる。
【０１５４】
（８）上述した各実施形態において、安静時に心拍出量を算出する装置にあっては、体動がないので、体動波形を検出する加速度センサ１３０’（体動検出手段）と、脈波波形ＭＨから体動成分を除去して体動除去脈波波形ＭＨ’を生成する体動除去部１１を用いなくてもよい。この場合、第５実施形態に記載された一回拍出量算出部１４を用いると、演算ステップを減少させることができるので、処理時間の短縮と消費電力の低減を図ることができる。
【０１５５】
（９）上述した各実施形態では、心機能診断装置１を説明したが、心機能診断装置１は、脈波検出用センサユニット１３０から一回拍出量算出部１４までの構成によって、一回拍出量ＳＶが算出されるので、これらの構成を一回拍出量検出装置として把握できることは勿論である。また、脈波検出用センサユニット１３０から心拍出量算出部１５までの構成によって、心拍出量ＣＯが算出されるので、これらの構成を拍出量検出装置として把握できることは勿論である。
【０１５６】
（１０）上述した各実施形態では、心機能診断装置１を説明したが、心機能診断装置１は、脈波検出用センサユニット１３０から一回拍出量算出部１４までの構成によって、一回拍出量ＳＶが算出されるので、これらの構成を一回拍出量検出装置として把握できることは勿論である。また、脈波検出用センサユニット１３０から心拍出量算出部１５までの構成によって、心拍出量ＣＯが算出されるので、これらの構成を拍出量検出装置として把握できることは勿論である。
【０１５７】
（１１）上述した第６実施形態においては、精密に計測された基準一回拍出量ＳＶｒと、一回拍出量算出部１４によって算出された一回拍出量ＳＶとに基づいて、補正係数ＫＨが算出しこれが補正係数メモリ２４１に記憶されるが、この補正係数ＴＨを心拍数ＨＲに対応付けて補正係数メモリ２４１に記憶するようにしてもよい。この場合には、通常の測定モードにおいて心拍数ＨＲに応じた補正係数ＴＨを読み出すことにより、より精密な心拍出量ＣＯを算出することができる。
【０１５８】
また、補正係数ＫＨを一回拍出量ＳＶについて算出するのではなく、心拍数ＨＲに応じた心拍出量ＣＯについて算出するようにしてもよい。この場合には、外部機器から精密に測定された基準心拍出量ＣＯ’と、心拍出量算出部１５で算出される心泊出量ＣＯとを補正係数算出部２４０に供給し、そこで生成される補正係数ＫＨを補正係数メモリ２４１に心拍数ＨＲと対応付けて格納するようにすればよい。そして、通常の測定モードにおいて、その時点の心拍数ＨＲに応じて補正係数メモリ２４１から読み出した補正係数ＫＨと、心拍出量ＣＯとを乗算器２４２で乗算することによって補正された心拍出量ＣＯを得るようにしてもよい。
【０１５９】
【発明の効果】
以上説明したように、本発明の発明特定事項によれば、簡易な構成で運動中や日常生活において連続的に心拍出量を検出することができる。また、心拍出量に基づいて、心機能を評価することができる。
【図面の簡単な説明】
【図１】 本発明の一実施形態に係わる心機能診断装置の機能構成を示す機能ブロック図である。
【図２】
【図３】 第１実施形態に係わる心機能診断装置の外観構成を示す斜視図である。
【図４】 同実施形態に係わる脈波検出用センサユニット１３０の回路図である。
【図５】 同実施形態に係わる心機能診断装置の電気的構成を示すブロック図である。
【図６】 同実施形態に係わる１拍分の体動除去脈波波形ＭＨ’の一例を示す図である。
【図７】 同実施形態に係わる拍数検出部と駆出期間検出部の具体的な構成を示す回路図である。
【図８】 同実施形態に係わる脈波波形の一例を示す図である。
【図９】 同実施形態に係わるピーク情報の内容を示す図である。
【図１０】 第２実施形態に係わる心機能診断装置の電気的構成を示すブロック図である。
【図１１】 同実施形態に係わる第１のウエーブレット変換部の構成を示すブロック図である。
【図１２】 同実施形態に係わる脈波波形の一部の期間について、脈波解析データを示したものである。
【図１３】 同実施形態に係わる体動除去部の動作を説明するためのタイミングチャートである。
【図１４】 同実施形態において、期間Ｔｃにおける脈波補正データＭＫＤ’を示す図である。
【図１５】 同実施形態において、期間Ｔｃにおける体動補正データＴＫＤ’を示す図である。
【図１６】 同実施形態において、体動成分が除去された脈波補正データＭＫＤ''を示す図である。
【図１７】 同実施形態における、一回拍出量算出部の動作を説明するための図である。
【図１８】 第３実施形態に係わる心機能診断装置のブロック図である。
【図１９】 第４実施形態に係わる心機能診断装置のブロック図である。
【図２０】 同実施形態に係わる体動除去部１１の詳細なブロック図である。
【図２１】 同実施形態に係わる体動除去脈波データＭＫＤ’’の一例を示す図である。
【図２２】 第５実施形態に係わる一回拍出量算出部の構成を示すブロック図である。
【図２３】 同実施形態に係わる心拍出量テーブルを示すブロック図である。
【図２４】 第６実施形態に係わる一回拍出量補正部２４のブロック図である。
【図２５】 第７実施形態に係わる評価部１５のブロック図である。
【図２６】 変形例においてウエーブレット変換をフィルタバンクで構成した場合の例を示すブロック図である。
【図２７】 変形例において逆ウエーブレット変換をフィルタバンクで構成した場合の例を示すブロック図である。
【図２８】 変形例において告知手段の一態様としてのフェイスチャートを示す図である。
【図２９】 変形例に係わる光電式脈波センサの例を示す図である。
【図３０】 変形例において光電式脈波センサを眼鏡に応用した例を示す図である。
【図３１】 変形例において圧力センサを用いた心機能診断装置の外観構成を示す斜視図である。
【図３２】 収縮期面積法を説明する図である。
【符号の説明】
１３０ 脈波検出用センサユニット（脈波検出手段）
１３０’加速度センサ（体動検出手段）
１１ 体動除去部（体動除去手段）
１２ 心拍数検出部（心拍数検出手段）
１３ 駆出期間検出部（駆出期間検出手段）
１７ 表示部（告知手段）
２０ 第１のウエーブレット変換部（第１のウエーブレット変換手段）
２１ 第１の周波数補正部（第１の周波数補正手段）
２２ 第２のウエーブレット変換部（第２のウエーブレット変換手段）
２３ 第２の周波数補正部（第２の周波数補正手段）[0001]
BACKGROUND OF THE INVENTION
The present invention relates to a cardiac output detection device suitable for continuously detecting cardiac output during daily life or exercise, and a cardiac function diagnosis device using the same.
[0002]
[Prior art]
The heart is a muscular organ mainly composed of myocardial tissue, and contracts at a constant rhythm to send blood to the aorta. The heart is divided into an upper atrium and a lower ventricle, and the atrium and ventricle are divided into left and right by an atrial septum and a ventricular septum. Both the atria and ventricles regularly contract and expand, but the timing is slightly different. When the atria contract, the ventricles expand and atrial blood pushes the atrioventricular valves open and flows into the ventricles. At this time, the arterial valve is closed to prevent blood from the aorta from flowing into the ventricle.
[0003]
Conversely, when the atria expand and suck blood from the veins, the ventricles contract and pump blood into the aorta. At this time, the atrioventricular valve is pushed up from the ventricle side, but the chords formed between the atrioventricular valve and the ventricular wall are stretched so that the valve does not reverse. As the ventricle contracts, the arterial valve is forced against the artery wall to pass blood. At this time, the aorta expands and stores a portion of the blood that has been pushed out of the ventricle.
[0004]
Next, while the ventricle is dilated, the aorta gradually contracts and pumps the stored blood toward the periphery. For this reason, blood always flows through the aorta even when blood is not being pumped out of the ventricle.
[0005]
In this way, the heart pumps blood to the aorta, and the amount of blood pumped by one contraction is called the stroke volume SV. The unit is liters. The product of stroke volume SV and heart rate HR (times / minute) is called cardiac output CO. The cardiac output CO indicates the volume of blood per minute delivered from the heart, and its unit is liter / minute.
[0006]
If there is a disease in the heart, the function of the myocardial tissue is reduced and a large amount of blood cannot be pumped, so that the cardiac output CO is reduced. On the other hand, a person who is training like an athlete increases the cardiac output CO as the exercise intensity increases. Thus, since the cardiac output CO reflects the quality of cardiac function, it is often used as an index when evaluating cardiac function.
[0007]
In measuring the cardiac output CO, first, it is necessary to measure the stroke volume SV. As a method for directly measuring this, there is a method in which the internal pressure of the heart is measured with a cardiac catheter, and the stroke volume SV is calculated from the measurement result.
[0008]
As another method, there is a systolic area method in which a cuff band is attached to the upper arm, the blood pressure of the artery is measured, and the stroke volume SV is calculated from the pulse wave waveform. FIG. 32 shows a general pulse wave waveform. Since the pulse waveform is obtained by measuring the pulsation of the blood flow caused by the contraction / expansion of the heart at the peripheral portion, the waveform shape reflects the motion of the heart. The ED in the figure is called an ejection period and corresponds to the time that blood flows from the heart during one heartbeat. In the systolic area method, the area S is calculated by integrating the ejection period ED and the blood pressure value of the pulse wave waveform corresponding to this period, and this is multiplied by the coefficient Ksv, whereby the stroke volume SV is calculated. Is calculated. In this case, the cardiac output CO is calculated by the following equation.
CO = S * Ksv * HR
[0009]
[Problems to be solved by the invention]
By the way, since the cardiac output CO is used as an evaluation index of cardiac function as described above, scientific training can be performed if the cardiac output CO can be known during exercise such as running. Become. In addition, patients suffering from heart disease may be at risk because their cardiac function is reduced during daily work. In such a case, since the cardiac output CO decreases, if the cardiac output CO can be known during the work, it can be used for the health management of the patient.
[0010]
However, in the method of measuring the internal pressure of the heart with a cardiac catheter, it is assumed that the subject is in a resting state, and the cardiac output CO cannot be measured continuously during exercise or in daily life. .
[0011]
Moreover, when using a cuff belt, it is necessary to cover the upper arm partly, which imposes a burden on the subject. Further, when the subject moves his / her arm during exercise or daily life, the blood flow is affected by the body movement, and the body movement component is superimposed on the pulse wave waveform. For this reason, the cardiac output CO cannot be continuously measured during exercise or daily life.
[0012]
The present invention has been made in view of such circumstances, and an object thereof is to provide a cardiac output detection device that continuously detects cardiac output CO during exercise or in daily life. Another object is to provide a cardiac function diagnostic apparatus that evaluates cardiac function based on cardiac output CO obtained by a cardiac output detection device.
[0013]
[Means for Solving the Problems]
In order to solve the above-mentioned problems, in the invention according to claim 1, a pulse wave detecting means for detecting a pulse wave waveform from a detection part of a living body, and a body for detecting a body movement waveform indicating the body movement of the living body. Body motion removal for generating a body motion component in the pulse wave waveform based on the body motion waveform and removing the body motion component from the pulse wave waveform to generate a body motion removal pulse wave waveform Means, heart rate detecting means for detecting the heart rate of the living body, ejection period detecting means for detecting the ejection period of the heart based on the body motion removal pulse wave waveform, and the ejection period of the heart And a cardiac output calculating means for calculating a cardiac output based on the heart rate.
[0014]
In the invention according to claim 2, a pulse wave detecting means for detecting a pulse wave waveform from a detection part of a living body, a body motion detecting means for detecting a body motion waveform indicating the body movement of the living body, Body motion removing means for generating a body motion component in the pulse waveform based on the body motion waveform, removing the body motion component from the pulse waveform, and generating a body motion removed pulse waveform; and the living body A heart rate detecting means for detecting a heart rate of the patient, an ejection period detecting means for detecting a cardiac ejection period based on the body motion elimination pulse waveform, and the body motion elimination pulse in the cardiac ejection period. A cardiac output calculating means for calculating a cardiac output based on a wave waveform and the heart rate is provided.
[0015]
Further, in the invention according to claim 3, the body motion removing unit includes a determination unit that determines presence / absence of body motion of the living body based on a body motion waveform detected by the body motion detecting unit. When the determination result of the determination means indicates no body motion, the body motion removal operation is stopped, and the pulse wave waveform is output instead of the body motion removal pulse wave waveform.
[0016]
Further, in the invention according to claim 4, the heart rate detecting means obtains the heart rate based on a periodicity of the electrocardiogram waveform of the heart or the body motion removal pulse waveform. To do.
[0017]
In the invention according to claim 5, the heart rate detecting means performs frequency analysis on the electrocardiogram waveform of the heart or the body motion-removed pulse waveform, and based on the analysis result, the heart rate It is characterized by calculating | requiring.
[0018]
In the invention according to claim 6, the ejection period detection means detects each peak of the body motion removal pulse wave waveform, and the negative appearing first or second from the maximum peak The ejection period is detected by specifying a peak and a minimum peak.
[0019]
Further, in the invention according to claim 7, a pulse wave detecting means for detecting a pulse wave waveform from a detection part of a living body, a body motion detecting means for detecting a body motion waveform indicating the body motion of the living body, Body motion removing means for generating a body motion component in the pulse wave waveform based on the body motion waveform, removing the body motion component from the pulse wave waveform to generate a body motion removal pulse wave waveform, and the body Wavelet transform means for generating body motion removal pulse wave analysis data obtained by performing wavelet transform on the motion removal pulse waveform to remove body motion for each frequency region, and based on the body motion removal pulse wave analysis data A heart rate detecting means for detecting a heart rate; an ejection period detecting means for detecting an ejection period of the heart based on the body motion removal pulse wave analysis data; and the body motion removal in the ejection period of the heart. Calculate cardiac output based on pulse wave waveform and heart rate Characterized in that a calculation means.
[0020]
  In the invention according to claim 8, a pulse wave detecting means for detecting a pulse wave waveform from a detection part of a living body, a body movement detecting means for detecting a body movement waveform indicating the body movement of the living body, First body motion removal means for generating a body motion component in the pulse wave waveform based on the body motion waveform, and removing the body motion component from the pulse wave waveform to generate a body motion removal pulse wave waveform; A second body motion removing unit that performs wavelet transform on the body motion removed pulse wave waveform to generate body motion removed pulse wave analysis data in which the body motion is removed for each frequency region; and based on each corresponding frequency And correcting the body motion-removed pulse wave analysis data so as to normalize the power per frequency to generate corrected pulse wave data, and detecting the heart rate based on the corrected pulse wave data. Based on the heart rate detection means and the corrected pulse wave data, the ejection period of the heart An ejection period detecting means for detecting the interval, and a cardiac output calculating means for calculating a cardiac output based on the body motion removal pulse wave waveform and the heart rate during the cardiac ejection period. It is characterized by that.
[0021]
In the invention according to claim 9, a pulse wave detecting means for detecting a pulse wave waveform from a detection part of a living body, and a pulse wave analysis for each frequency region by applying wavelet transform to the pulse wave waveform. First wavelet transforming means for generating data, body motion detecting means for detecting a body motion waveform indicating the body motion of the living body, and subjecting the body motion waveform to wave motion for each frequency region by performing wavelet transform Second wavelet conversion means for generating analysis data; and body motion removal means for generating body motion removal pulse wave analysis data by subtracting the body motion analysis data from the pulse wave analysis data to remove body motion; A heart rate detecting means for detecting a heart rate based on the body motion removed pulse wave analysis data; and an ejection period detecting means for detecting a cardiac ejection period based on the body motion removed pulse wave analysis data. , Each round in the ejection period of the heart On the basis of the heart rate and the result obtained by adding the body motion removing pulse wave analysis data of several regions, characterized in that a cardiac output calculating means for calculating cardiac output.
[0022]
In the invention according to claim 10, pulse wave detection means for detecting a pulse wave waveform from a detection part of a living body, and wave wave analysis is applied to the pulse wave waveform for each frequency region. First wavelet transform means for generating data, and first correction wavelet analysis data is generated by correcting the pulse wave analysis data so as to normalize the power per frequency based on each corresponding frequency. Frequency correction means, body motion detection means for detecting a body motion waveform indicating body motion of the living body, and second to generate wave motion analysis data for each frequency region by performing wavelet transform on the body motion waveform. And a second frequency correction unit that generates correction body motion analysis data by correcting the body motion analysis data so as to normalize the power per frequency based on each corresponding frequency, Above Based on the body motion removal pulse wave analysis data, body motion removal means for subtracting the corrected body motion analysis data from the normal pulse wave analysis data to generate body motion removal pulse wave analysis data from which body motion has been removed, A heart rate detecting means for detecting a heart rate; an ejection period detecting means for detecting an ejection period of the heart based on the body motion removal pulse wave analysis data; and the frequency regions in the cardiac ejection period. A cardiac output calculating means for calculating a cardiac output based on a result obtained by adding the body motion removal pulse wave analysis data and the heart rate is provided.
[0023]
The invention according to claim 11 is characterized in that the first wavelet conversion means and the second wavelet conversion means perform wavelet conversion synchronously.
[0024]
In the invention according to claim 12, pulse wave detection means for detecting a pulse wave waveform from a detection part of a living body, and wavelet conversion is performed on the pulse wave waveform detected by the pulse wave detection means. A wavelet transforming means for generating pulse wave analysis data for each frequency region, and removing a frequency component corresponding to a predetermined body motion from the pulse wave analysis data to perform body motion removal pulse wave analysis Body motion removal means for generating data, heart rate detection means for detecting a heart rate based on the body motion removal pulse wave analysis data, and a cardiac ejection period based on the body motion removal pulse wave analysis data A heart rate for calculating cardiac output based on the result of adding the body motion removal pulse wave analysis data in each frequency region in the ejection period of the heart and the heart rate Having an output calculation means And butterflies.
[0025]
In the invention according to claim 13, pulse wave detection means for detecting a pulse wave waveform from a detection part of a living body, and wavelet conversion is performed on the pulse wave waveform detected by the pulse wave detection means. A wavelet transforming means for generating pulse wave analysis data for each frequency region, and removing a frequency component corresponding to a predetermined body motion from the pulse wave analysis data to perform body motion removal pulse wave analysis Body motion removal means for generating data and frequency correction for generating corrected pulse wave analysis data by correcting the body motion removal pulse wave analysis data to normalize the power per frequency based on each corresponding frequency Means, heart rate detecting means for detecting a heart rate based on the corrected pulse wave analysis data, ejection period detecting means for detecting a cardiac ejection period based on the corrected pulse wave analysis data, Ejection of the heart The result of said adding the correction pulse wave analysis data in each frequency region and between on the basis of the heart rate, and cardiac output calculating means for calculating cardiac output
It is provided with.
[0026]
In the invention described in claim 14, pulse wave detection means for detecting a pulse wave waveform from a detection part of a living body, and wavelet conversion is performed on the pulse wave waveform detected by the pulse wave detection means. A wavelet transforming means for generating pulse wave analysis data for each frequency region, and removing a frequency component corresponding to a predetermined body motion from the pulse wave analysis data to perform body motion removal pulse wave analysis Body motion removing means for generating data, inverse wavelet transform means for generating a body motion removal pulse wave waveform by applying inverse wavelet transform to the body motion removal analysis pulse wave data, and the body motion removal pulse wave waveform Based on the heart rate detecting means for detecting the heart rate, the ejection period detecting means for detecting the ejection period of the heart based on the body motion removal pulse wave waveform, and the body movement in the ejection period of the heart Elimination pulse waveform and heart rate Based on, characterized in that a cardiac output calculating means for calculating cardiac output.
[0027]
In the invention according to claim 15, the cardiac output calculating means integrates the body motion removal pulse wave waveform in the cardiac ejection period to integrate the body movement removal corresponding to the period. An area of the pulse wave waveform is calculated, and the cardiac output is calculated based on the area.
[0028]
Further, in the invention according to claim 16, the cardiac output calculating means is configured to determine the body corresponding to the period based on each peak value of the body motion removal pulse wave waveform in the ejection period of the heart. An area of the motion removal pulse wave waveform is calculated, and the cardiac output is calculated based on the area.
[0031]
  In the invention according to claim 17, a ratio between the reference cardiac output measured by the reference device and the cardiac output measured by the cardiac output calculating means is stored as a correction coefficient. Storage means for multiplying the correction coefficient read from the storage means by the cardiac output calculated by the cardiac output calculating means, and multiplying means for outputting the multiplication result as cardiac output. Is further provided.
[0032]
  The invention according to claim 18 is a cardiac function diagnostic apparatus comprising the cardiac output detecting device, wherein the cardiac output detected by the cardiac output detecting device is notified. A notification means is further provided.
[0033]
  The invention according to claim 19 is a cardiac function diagnostic apparatus comprising the cardiac output detecting device, wherein the cardiac output detected by the cardiac output detecting device is set to each threshold value. Compared to the above, it is further characterized by further comprising an evaluation means for generating an evaluation index and a notification means for notifying the evaluation index generated by the evaluation means.
[0034]
  The invention according to claim 20 is characterized in that the evaluation means further comprises a changing section for changing the threshold values according to the heart rate detected by the heart rate detecting means. .
[0035]
  In the invention according to claim 21, the evaluation means inputs an input unit for inputting a parameter for calculating the body surface area of the subject, and an operation for calculating the body surface area based on the input parameter. And a changing unit that changes each of the threshold values based on the calculated body surface area.
[0036]
DETAILED DESCRIPTION OF THE INVENTION
A. Functional configuration
First, functions of a cardiac function diagnostic apparatus according to an embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a functional block diagram of a cardiac function diagnostic apparatus according to this embodiment. In the figure, f1 is a pulse wave detecting means for detecting a pulse wave waveform. The pulse wave waveform is obtained, for example, by detecting a blood flow in a peripheral part such as a fingertip or a finger base with an optical sensor. f2 is a body motion detecting means for detecting a body motion and outputting a body motion waveform. Thereby, it is detected that the person has moved.
[0037]
Next, f3 is a body motion removing means, which generates a body motion component in the pulse waveform based on the body motion waveform, removes the body motion component from the pulse waveform, and generates the body motion removed pulse waveform. Generate. This makes it possible to generate a pulse wave waveform that is not affected by body movement even during exercise.
[0038]
Next, f4 is a heart rate detection means, which detects the heart rate based on the body motion removal pulse wave waveform. Further, f5 is an ejection period detection means that detects the ejection period of the heart based on the body motion removal pulse wave waveform. The ejection period is the period during which the heart pumps blood to the aorta with a single contraction. Here, the ejection period will be described in more detail.
[0039]
FIG. 2 shows the heart cycle. In the figure, SW is an electrocardiogram waveform, MH1 is an aortic blood pressure waveform immediately after flowing out of the heart, and MH2 is a general pulse waveform in the peripheral part (radial artery). In this figure, the time delay associated with blood flow is ignored. Strictly speaking, the ejection period ED is a time interval between the aortic valve opening time t1 and the aortic valve closing time t2 in the aortic blood pressure waveform MH1, and is about 280 ms at rest. Since the closure of the aortic valve is caused by ventricular contraction, this time interval approximately coincides with the time of the ventricular systole (Sysolic Time). By the way, the notch N2 in the peripheral pulse wave waveform MH2 is referred to as a destructive notch N2 and is caused by aortic valve closure. For this reason, the time interval from the minimum peak P0 to the peak P4 in the pulse wave waveform MH2 corresponds to the ejection period ED.
[0040]
Further, it is known that there are individual differences in the pulse wave waveform, and the waveform shape changes depending on physical condition etc. even in the same individual. For this reason, in the pulse wave waveform MH2 in the peripheral portion, as shown by MH3, the peak P1 and the peak P3 may overlap, and the notch N1 may not occur. Also in this case, the ejection period ED is a time interval from the minimum peak P0 to the peak P4.
[0041]
By the way, the period from the minimum peak P0 to the peak P2 of the notch N1 in the pulse wave waveform MH2 is called an estimated systolic time, and there is a theory that considers this time interval as the ejection period ED. In any case, there is no disagreement that these periods are representative of the cardiac contraction period.
[0042]
Thus, the ejection period ED used in this specification is not only the ejection duration in the strict sense, but also the ventricular systolic time and the estimated systolic time. ) Is included, and the following explanation is advanced. Specifically, the ejection period ED is grasped as a period from the minimum peak to the first or second negative peaks P2 and P4 that occur after the maximum peak P1.
[0043]
Next, f6 is a cardiac output detection means, which detects the heart rate hand quantity CO. For example, the stroke volume SV is calculated based on the body movement elimination pulse wave waveform during the ejection period, and the cardiac output CO is detected by multiplying the stroke volume SV by the heart rate. ing.
[0044]
Next, f7 is an evaluation means that evaluates the state of cardiac function based on the cardiac output. That is, the evaluation of cardiac function is evaluated by the amount of blood per minute delivered from the heart. Further, f8 is a notification means for notifying the evaluation result. Thereby, the doctor who is a subject and a third party can know the heart function of the subject.
[0045]
Next, f9 is a determination unit that determines the presence or absence of body movement based on the level change of the body movement waveform, and controls to stop the operation of the body movement removal unit f3 when there is no body movement. To do. Thereby, the calculation accompanying a body movement removal process can be reduced.
[0046]
B. First embodiment
1. Configuration of the first embodiment
A configuration of a cardiac function diagnostic apparatus according to an embodiment of the present invention will be described with reference to the drawings.
1-1: External configuration of the first embodiment
FIG. 3 is a perspective view showing an external configuration of the cardiac function diagnostic apparatus according to the first embodiment. In FIG. 3, the cardiac function diagnosis apparatus 1 of the present example includes an apparatus main body 110 having a wristwatch structure, a cable 120 connected to the apparatus main body 110, and a pulse wave detection sensor provided on the distal end side of the cable 120. The unit 130 is generally configured. A connector piece 80 is formed on the distal end side of the cable 120, and this connector piece 80 is detachable from the connector portion 70 formed on the 6 o'clock side of the apparatus main body 10. The apparatus main body 10 is provided with a wristband 60 which is wound around the arm from 12 o'clock in the wristwatch and fixed in the 6 o'clock direction. With the wristband 60, the apparatus main body 110 is detachable from the arm. The pulse wave detection sensor unit 130 is attached to the base of the index finger while being shielded by the sensor fixing band 140. In this way, when the pulse wave detection sensor unit 130 is attached to the base of the finger, the cable 120 can be shortened, so the cable 120 does not get in the way during running. Further, when the distribution of the body temperature from the palm to the fingertip is measured, the temperature of the fingertip is remarkably reduced when it is cold, but the temperature of the base of the finger is not relatively lowered. Therefore, if the pulse wave detection sensor unit 130 is attached to the base of the finger, the pulse rate and the like can be accurately measured even when running outdoors on a cold day.
[0047]
The apparatus main body 110 includes a resin watch case 200 (main body case). On the surface side of the watch case 200, in addition to the current time and date, a pitch and a pulse during running and walking are provided. A liquid crystal display device 210 with an EL backlight that displays pulse wave information such as the number is configured. The liquid crystal display device 210 displays the state of cardiac function represented by cardiac output CO and stroke volume SV. In addition to the segment display area, the liquid crystal display device 210 includes a dot display area. In the dot display area, various types of information can be displayed graphically.
[0048]
In addition, an acceleration sensor 130 'is incorporated in the watch case 200, thereby detecting body movement caused by swinging of the arm during running or vertical movement of the body. In addition, changes in the cardiac output CO and stroke volume SV are obtained based on the pulse wave waveform MH measured by the pulse wave detection sensor unit 130, and this is stored in the liquid crystal display device 210. In order to display, a control unit including a microcomputer that performs various controls and data processing is configured. The control unit is also configured with a timer circuit so that the normal time, lap time, split time, and the like can be displayed on the liquid crystal display device 210. In addition, button switches 111 to 115 for performing external operations such as time adjustment and display mode switching are configured on the outer periphery of the watch case 200.
[0049]
Next, the pulse wave detection sensor unit 130 includes an LED 32, a phototransistor 33, and the like as shown in FIG. When the switch SW is turned on and a power supply voltage is applied, light is emitted from the LED 32, reflected by a blood vessel or tissue, received by the phototransistor 33, and a pulse wave signal M is detected. Here, the emission wavelength of the LED is selected in the vicinity of the absorption wavelength peak of hemoglobin in the blood. For this reason, a light reception level changes according to a blood flow rate. Therefore, the pulse wave waveform can be detected by detecting the light reception level.
As the LED 32, an InGaN-based (indium-gallium-nitrogen-based) blue LED is suitable. The emission spectrum of the blue LED has an emission peak at 450 nm, for example, and the emission wavelength region is in the range from 350 nm to 600 nm. In this case, a GaAsP-based (gallium-arsenic-phosphorus-based) phototransistor may be used as the phototransistor 33 in correspondence with the LED having such light emission characteristics. In the photoreception wavelength region of the phototransistor 33, for example, the main sensitivity region is in the range from 300 nm to 600 nm, and there is also a sensitivity region at 300 nm or less. When such a blue LED and the phototransistor 33 are combined, a pulse wave is detected in the wavelength region from 300 nm to 600 nm, which is the overlapping region. In this case, there are the following advantages.
[0050]
First, of the light included in the external light, light having a wavelength region of 700 nm or less tends to be difficult to transmit through the finger tissue, so the external light is applied to the finger portion that is not covered with the sensor fixing band. However, only light in a wavelength region that does not reach the phototransistor 33 via the finger tissue and does not affect detection reaches the phototransistor 33. On the other hand, since light in a wavelength region lower than 300 nm is almost absorbed by the skin surface, even if the light receiving wavelength region is 700 nm or less, the substantial light receiving wavelength region is 300 nm to 700 nm. Therefore, the influence of external light can be suppressed without covering the finger with a large scale. Moreover, hemoglobin in blood has a large extinction coefficient for light having a wavelength of 300 nm to 700 nm, and is several times to about 100 times or more larger than that for light having a wavelength of 880 nm. Therefore, as shown in this example, when light of a wavelength region (300 nm to 700 nm) having a large light absorption characteristic is used as detection light in accordance with the light absorption characteristic of hemoglobin, the detection value changes with sensitivity according to the blood volume change. Therefore, the S / N ratio of the pulse wave waveform MH based on the blood volume change can be increased.
[0051]
1-2: Electrical configuration of the first embodiment
Next, the electrical configuration of the cardiac function diagnostic apparatus will be described with reference to FIG. FIG. 5 is a block diagram showing an electrical configuration of the cardiac function diagnostic apparatus.
The cardiac function diagnostic apparatus 1 includes the following parts. The pulse wave detection sensor unit 130 detects the pulse wave waveform MH and outputs it to the body motion removal unit 11. The acceleration sensor 130 'detects body movement as acceleration and generates a body movement waveform TH. The waveform processing unit 10 performs waveform processing on the body motion waveform TH in order to accurately remove the body motion component in the body motion removal unit 11.
[0052]
Here, if the body motion component in the pulse wave waveform MH is represented by MHt and the true pulse wave component (body motion removal pulse waveform) is represented by MH ′, MH = MHt + MH ′. The body motion waveform TH is detected as the acceleration of the arm swing itself, but since the blood flow is affected by blood vessels and tissues, the body motion component MHt is a dull body motion waveform TH. For this reason, the waveform processing unit 10 is configured by a low-pass filter. Note that the type and constant of the low-pass filter are determined from actually measured data.
[0053]
Next, the body motion removal unit 11 subtracts the output waveform MHt of the waveform processing unit 10 from the pulse wave waveform MH to generate a body motion removal pulse wave waveform MH ′. The body motion removal pulse wave waveform MH ′ is converted into a digital signal via an A / D converter (not shown) and supplied to the heart rate detection unit 12 and the ejection period detection unit 13.
[0054]
By the way, even if there is no body movement, if the body movement removing unit 11 is operated to remove body movement, the SN ratio of the output signal of the body movement removing unit 11 deteriorates due to noise of the acceleration sensor 130 '. In addition, power is consumed for the body motion removal operation. For this reason, in this embodiment, the determination part 11 'is provided. The determination unit 11 ′ determines the presence or absence of body movement based on the body movement waveform TH and generates a control signal C. Specifically, the determination is made by comparing the threshold value with the body motion waveform TH. This threshold is determined in advance so that the presence or absence of body movement can be determined in consideration of the noise level of the acceleration sensor 130 '. When the control signal C indicates no body movement, the operations of the waveform processing unit 10 and the body movement removing unit 11 are stopped. In this case, the pulse wave waveform MH is directly output from the body motion removal unit 11. Thereby, the S / N ratio of the output signal of the body motion removal unit 11 can be improved, and the power consumption of the apparatus can be reduced.
[0055]
Next, the heart rate detection unit 12 and the ejection period detection unit 13 detect the heart rate HR and the ejection period ED based on the body motion removal pulse wave waveform MH ′. In the present embodiment, the heart rate HR and the ejection period ED are obtained by analyzing the amplitude level of the body motion removal pulse wave waveform MH ′.
The heart rate detection unit 12 and the ejection period detection unit 13 extract waveform parameters that specify the shape of the body motion removal pulse waveform MH ′. Here, assuming that the body motion elimination pulse wave waveform MH 'for one beat has a shape as shown in FIG. 6, the waveform parameters are defined as follows. In FIG. 6, the vertical axis is blood pressure, and the horizontal axis is time.
(1) Time t from when the pulse wave corresponding to one beat rises (hereinafter, this rise time is referred to as pulse wave start time) until the pulse wave corresponding to the next beat starts to rise₆
(2) Blood pressure values y at local maximum point P1, local minimum point P2, local maximum point P3, local minimum point P4 and local maximum point P5 appearing sequentially in the pulse wave₁~ Y_Five
(3) Elapsed time t from the pulse wave start time until the above points P1 to P5 appear₁~ T_Five
[0056]
The heart rate detection unit 12 and the ejection period detection unit 13 extract information called “peak information” related to each of the maximum points or the minimum points in order to calculate the waveform parameter. Note that the details of the peak information are related to the configuration and operation of the pulsation determination unit, and therefore the details of the peak information will be referred to when the circuit configuration is described.
[0057]
FIG. 7 is a block diagram showing the configuration of the heart rate detection unit 12 and the ejection period detection unit 13. In the figure, reference numeral 181 denotes a microcomputer which controls each component. Reference numeral 184 denotes a waveform memory constituted by a RAM, which sequentially stores the waveform value W of the body motion removal pulse waveform MH ′. Reference numeral 191 denotes a waveform value address counter that counts the sampling clock φ while the waveform collection instruction START is output from the microcomputer 181 and outputs the count result as the waveform value address ADR1 to which the waveform value W is to be written. The waveform value address ADR1 is monitored by the microcomputer 181.
[0058]
A selector 192 selects the waveform value address ADR1 output by the waveform value address counter 191 and supplies it to the address input terminal of the waveform memory 184 when the select signal S1 is not output from the microcomputer 181. On the other hand, when the select signal S 1 is output from the microcomputer 181, the read address ADR 4 output from the microcomputer 181 is selected and supplied to the address input terminal of the waveform memory 184.
The body motion removal pulse wave waveform MH ′ is taken into the waveform memory 183 via the A / D converter 182 and the low-pass filter 183.
[0059]
Reference numeral 201 denotes a differentiating circuit, which calculates and outputs time derivatives of the waveform values W sequentially output from the low-pass filter 183.
Reference numeral 202 denotes a zero cross detection circuit, which outputs a zero cross detection pulse Z when the waveform value W becomes a maximum value or a minimum value and the time differentiation of the waveform value W becomes zero. More specifically, the zero-cross detection circuit 202 is a circuit provided for detecting peak points P0, P1, P2,... In the pulse wave waveform illustrated in FIG. When the waveform value W is input, the zero cross detection pulse Z is output.
[0060]
Reference numeral 203 denotes a peak address counter, which counts the zero cross detection pulse Z while the waveform collection instruction START is output from the microcomputer 181 and outputs the count result as the peak address ADR2.
204 is a moving average calculation circuit, which calculates an average value of time differential values of a predetermined number of waveform values W that have been output from the differentiation circuit 201 up to the present time, and calculates the result of the inclination of the pulse wave up to the present time Is output as tilt information SLP.
[0061]
Reference numeral 205 denotes a peak information memory provided for storing peak information described below. Here, details of the peak information will be described below. That is, the details of the content of the peak information shown in FIG. 9 are as listed below.
(1) Waveform value address ADR1
This is the write address output from the waveform value address counter 191 when the waveform value W output from the low-pass filter 183 becomes a maximum value or a minimum value. In other words, it is the write address in the waveform memory 184 of the waveform value W corresponding to the maximum value or the minimum value.
(2) Peak type B / T
This is information indicating whether the waveform value W written in the waveform value address ADR1 is the maximum value T (Top) or the minimum value B (Bottom).
(3) Waveform value W
This is a waveform value corresponding to the maximum value or the minimum value.
(4) Stroke information STRK
This is a change in waveform value from the previous peak value to the peak value.
▲ 5 ▼ Inclination information SLP
It is an average value of time derivatives of a predetermined number of waveform values up to the peak value.
[0062]
Next, operations of the heart rate detection unit 12 and the ejection period detection unit 13 under the control of the microcomputer 181 will be described.
[0063]
(A) Collection of waveform and its peak information
When the waveform collection instruction START is output from the microcomputer 181, the reset of the waveform value address counter 191 and the peak address counter 203 is released.
As a result, the waveform value address counter 191 starts counting the sampling clock φ, and the count value is supplied to the waveform memory 184 via the selector 192 as the waveform value address ADR1. Then, the pulse wave signal detected from the human body is input to the A / D converter 182, is sequentially converted into a digital signal according to the sampling clock φ, and is sequentially output as a waveform value W through the low-pass filter 183. The waveform values W output in this way are sequentially supplied to the waveform memory 184, and are written in the storage area designated by the waveform value address ADR1 at that time. With the above operation, a series of waveform values W corresponding to the pulse waveform illustrated in FIG. 8 is accumulated in the waveform memory 184.
[0064]
On the other hand, in parallel with the above operation, detection of peak information and writing to the peak information memory 205 are performed as described below.
First, the time differentiation of the waveform value W of the body motion removal pulse waveform MH ′ is calculated by the differentiation circuit 201, and this time differentiation is input to the zero cross detection circuit 202 and the moving average calculation circuit 204. The moving average calculation circuit 204 calculates the average value (that is, moving average value) of the past predetermined number of time differential values each time the time differential value of the waveform value W is supplied in this way, and calculates the calculation result as the slope information. Output as SLP. Here, when the waveform value W is rising or has finished rising, the maximum value is output as the inclination information SLP, and when the waveform value W is falling or has finished falling and has reached the minimum state, the inclination information is obtained. A negative value is output as SLP.
[0065]
For example, when the waveform value W corresponding to the maximum point P1 shown in FIG. 8 is output from the low-pass filter 183, 0 is output as the time differentiation from the differentiation circuit 201, and the zero cross detection pulse Z is output from the zero cross detection circuit 202. Is output.
As a result, the microcomputer 181 causes the waveform address ADR1, which is the count value of the waveform value address counter 191 at that time, the waveform value W, the peak address ADR2 which is the count value of the peak address counter (in this case, ADR2 = 0), and the inclination Information SLP is captured. Also, the count value ADR2 of the peak address counter 203 becomes 1 by outputting the zero cross detection pulse Z.
[0066]
On the other hand, the microcomputer 181 creates the peak type B / T based on the captured sign of the slope information SLP. When the waveform value W of the maximum value P1 is output as in this case, since positive slope information is output at that time, the microcomputer 181 corresponds the peak information B / T to the maximum value. And Then, the microcomputer 181 designates the peak address ADR2 (ADR2 = 0 in this case) fetched from the peak address counter 203 as it is as the write address ADR3, the waveform value W, and the waveform address ADR1, the peak corresponding to this waveform value W The type B / T and the slope information SLP are written in the peak information memory 205 as the first peak information. In the first writing of peak information, the stroke information STRK is not created and written because there is no previous peak information.
[0067]
Thereafter, when the waveform value W corresponding to the minimum point P2 shown in FIG. 8 is output from the low-pass filter 183, the zero cross detection pulse Z is output in the same manner as described above, and the write address ADR1, waveform value W, peak address ADR2 (= 1), the inclination information SLP (<0) is taken in by the microcomputer 181.
Similarly to the above, the microcomputer 181 determines the peak type B / T (in this case, “B”) based on the slope information SLP. Further, the microcomputer 181 supplies an address smaller than the peak address ADR2 by 1 as the read address ADR3 to the peak information memory 205, and the waveform value W written for the first time is read. Then, the microcomputer 181 calculates the difference between the waveform value W acquired this time from the low-pass filter 183 and the first waveform value W read from the peak information memory 205, and the stroke information STRK is obtained. The peak type B / T and the stroke information STRK obtained in this way are other information, that is, the waveform value address ADR1, the waveform value W, the slope information SLP, and the peak address of the peak information memory 205 as the second peak information. Data is written in a storage area corresponding to ADR3 = 1. Thereafter, the same operation is performed when the peak points P3, P4,... Are detected.
At a predetermined timing, the microcomputer 181 stops the output of the waveform collection instruction START, and the collection of the waveform value W and peak information is completed.
[0068]
(B) Pulse wave waveform division processing
Of the various types of information stored in the peak information memory 205, the microcomputer 181 performs processing for specifying information corresponding to the waveform for one beat from which waveform parameters are collected.
First, the slope information SLP and the stroke information STRK corresponding to the peak points P0, P1, P2,. Next, stroke information corresponding to a positive inclination is selected from the stroke information STRK (that is, the corresponding inclination information SLP has a positive value), and a larger value is selected from these stroke information. The upper predetermined number is selected. Then, the one corresponding to the median value is selected from the selected stroke information STRK, and the rising portion of the pulse wave for one beat from which the waveform parameter is to be extracted (for example, the rising portion indicated by the symbol STRKM in FIG. 27). Stroke information is required. Then, a peak address that is one prior to the peak address of the stroke information (that is, the peak address of the pulse wave start point P6 for one beat from which the waveform parameter should be extracted) is obtained.
When the waveform for one beat is specified in this way, the time t shown in FIG.₆Is calculated.
[0069]
(C) Extraction of waveform parameters
The microcomputer 181 calculates each waveform parameter with reference to each peak information corresponding to the pulse wave for one beat stored in the peak information memory 205. This process is obtained as follows, for example.
(1) Blood pressure value y₁~ Y_Five
The waveform values corresponding to the peak points P6 to P11 are y₀~ Y_FiveAnd
(2) Time t₁
Subtract the waveform address corresponding to the peak point P6 from the waveform address corresponding to the peak point P7, and multiply the result by the period of the sampling clock φ t₁ Is calculated.
(3) Time t₂~ T₆
T above₁Similar to the above, the calculation is performed based on the waveform address difference between the corresponding peak points.
Each waveform parameter obtained as described above is stored in a buffer memory inside the microcomputer 181.
[0070]
(D) Calculation of the number of beats based on the waveform parameter
Time t₆Is the time for one beat. The microcomputer 181 takes time t₆60 / t based on₆To calculate the heart rate HR.
[0071]
(E) Calculation of ejection period based on waveform parameters
The microcomputer 181 accesses the internal buffer memory, and specifies the minimum peak Pmin and the maximum peak Pmax in one heartbeat based on the waveform parameter. For example, in the waveform shown in FIG. 6, P0 is specified as the minimum peak Pmin and P1 is specified as the maximum peak Pmax.
Next, the negative peak (notch) appearing first or second after the maximum peak Pmax is specified. In this example, if the negative peak that appears second is specified, for example, in the waveform shown in FIG. 6, P4 is specified as a negative peak. Then, the period from the minimum peak Pmin to the negative peak P4 is calculated as the ejection period ED. For example, in the waveform shown in FIG. 6, the period t4 is output as the ejection period ED.
In this way, the heart rate HR and the ejection period ED are calculated.
[0072]
Next, the stroke volume calculation unit 14 shown in FIG. 5 specifies the body motion removal pulse waveform MH ′ during the ejection period ED based on the body motion removal pulse waveform MH ′ and the ejection period ED. Then, the area S is calculated. Specifically, the area S is calculated by integrating the body motion removal pulse waveform MH ′ by sequentially adding the body motion removal pulse waveform MH ′ in each sample during the ejection period ED. Then, the stroke volume SV is calculated by multiplying the area S by the coefficient coefficient Ksv. That is, the single cardiac output SV is calculated by the following equation.
SV = Ksv * S
[0073]
Next, the cardiac output calculation unit 15 calculates the cardiac output CO by multiplying the heart rate HR and the stroke volume SV. That is, the cardiac output CO is calculated by the following formula.
CO = Ksv * S * HR
In calculating the cardiac output CO, the cardiac output SV per minute may be sequentially added and calculated.
[0074]
Next, the evaluation unit 16 includes a memory and a comparator (not shown), and evaluates cardiac function based on the cardiac output CO to generate an evaluation index X. The memory stores a threshold value used for delaying the cardiac output CO. The threshold is set according to the number of grading, but in this example, R1 and R2 are set as the threshold. The threshold values R1 and R2 may be stored in advance, or may be set by a doctor or a trainer.
[0075]
The comparator also compares the cardiac output CO with the threshold values R1 and R2 to generate an evaluation index X. In this example, the evaluation index X1 is generated when CO <R1, the evaluation index X2 is generated when R1 ≦ CO <R2, and the evaluation index X3 is generated when R2 ≦ CO. Here, the meanings of the evaluation indices X1 to X3 differ depending on how the cardiac function diagnostic apparatus 1 is used. For example, when used for exercise training, it is a measure for maintaining appropriate exercise intensity, and when monitoring cardiac function in heart disease rehabilitation, it is a measure indicating the degree of recovery.
[0076]
Next, the display unit 17 is composed of the above-described liquid crystal display device 210 or the like, on which a cardiac output CO, an evaluation index X, a message associated with the evaluation index X, or the like is displayed. Note that display modes include a face chart, characters, symbols, and the like. Thereby, the evaluation result of the cardiac function can be notified to the subject.
For example, when the cardiac function diagnostic apparatus 1 is used in running, the trainer can notify the subject to maintain an appropriate cardiac output CO by setting threshold values R1 and R2. In this case, the display unit 17 displays message sentences such as “Let's increase the pace” with the evaluation index X1, “Let's keep pace” with the evaluation index X2, and “Let's decrease the pace” with the evaluation index X3. Can be displayed.
[0077]
C. Second embodiment
Next, a cardiac function diagnostic apparatus according to the second embodiment will be described.
1. Configuration of the second embodiment
FIG. 10 is a block diagram of the cardiac function diagnostic apparatus 1 according to the second embodiment. In the second embodiment, the body motion component MHt is detected using the acceleration sensor 130 ′ and the waveform processing unit 10 as in the first embodiment. However, the body motion removal, the heart rate, and the driving force described in the first embodiment are detected. The difference is that the departure period is detected by using wavelet transform. The external configuration of the second embodiment is the same as the external configuration of the first embodiment shown in FIG.
[0078]
1-1. First and second wavelet transform units and first and second frequency correction units
In FIG. 10, reference numeral 20 denotes a first wavelet conversion unit, which performs known wavelet conversion on the pulse wave waveform MH output from the pulse wave detection sensor unit 130 to generate pulse wave analysis data MKD. Generate. Reference numeral 22 denotes a second wavelet converter, which performs known wavelet conversion on the body motion waveform MHt output from the acceleration sensor 130 'to generate body motion analysis data TKD.
[0079]
In general, in a time-frequency analysis in which a signal is simultaneously captured from both time and frequency, a wavelet is a unit for cutting out a signal portion. The wavelet transform represents the size of each part of the signal cut out in this unit. In order to define the wavelet transform, a function ψ (x) localized in terms of time and frequency is introduced as a mother wavelet as a basis function. Here, the wavelet transformation of the function f (x) by the mother wavelet ψ (x) is defined as follows.
[Expression 1]

[0080]
In Equation 1, b is a parameter used when the mother wavelet ψ (x) is translated (translated), while a is a parameter used when scaling (stretching). Accordingly, in Equation 1, the wavelet ψ ((x−b) / a) is obtained by translating the mother wavelet ψ (x) by b and expanding and contracting by a. In this case, since the width of the mother wavelet ψ (x) is expanded corresponding to the scale parameter a, 1 / a corresponds to the frequency. The detailed configuration will be described later.
[0081]
Next, 21 is a first frequency correction unit that performs frequency correction on the pulse wave analysis data MKD. In the above formula 1, "1 / a" corresponding to the frequency^1/2However, when comparing data between different frequency regions, it is necessary to correct the influence of this term. The first frequency correction unit 21 is provided for this purpose, and a coefficient a is added to the wavelet data WD.^1/2To generate pulse wave correction data MKD ′. Thereby, based on each corresponding frequency, it can correct | amend so that the power density per frequency may become fixed. Reference numeral 23 denotes a second frequency correction unit, which performs frequency correction in the same manner as the first frequency correction unit 21, and generates body motion correction data TKD 'from the body motion analysis data TKD.
[0082]
Here, the configuration of the first wavelet conversion unit 20 will be described in detail with reference to FIG. Note that the second wavelet conversion unit 22 is configured in the same manner as the first wavelet conversion unit 20, and a description thereof will be omitted.
The pulse wave waveform MH is converted into pulse wave data MD by an A / D converter and supplied to the first wavelet converter 20.
The first wavelet conversion unit 20 is configured to perform the arithmetic processing of Equation 1 described above, is supplied with the clock CK, and performs arithmetic processing in the clock cycle, and the mother wavelet ψ A basis function storage unit W1 that stores (x), a scale conversion unit W2 that converts the scale parameter a, a buffer memory W3, a translation unit W4 that performs translation, and a multiplication unit W5. The mother wavelet ψ (x) stored in the basis function storage unit W1 may be a Gabor wavelet, a Mexican hat, a Haar wavelet, a Meyer wavelet, a Shannon wavelet, or the like.
[0083]
First, when the mother wavelet ψ (x) is read from the basis function storage unit W1, the scale conversion unit W2 converts the scale parameter a. Here, since the scale parameter a corresponds to the period, the mother wavelet ψ (x) is expanded on the time axis when a increases. In this case, since the amount of data of the mother wavelet ψ (x) stored in the basis function storage unit W1 is constant, the amount of data per unit time decreases as a increases. The scale conversion unit W2 performs an interpolation process so as to compensate for this, and performs a thinning process when a becomes smaller to generate a function ψ (x / a). This data is temporarily stored in the buffer memory W3.
[0084]
Next, the translation unit W4 reads the function ψ (x / a) from the buffer memory W3 at a timing according to the translation parameter b, thereby translating the function ψ (x / a) and performing the function ψ (x−a). b / a) is generated.
[0085]
Next, the multiplication unit W4 uses the variable 1 / a^1/2, The function ψ (x−b / a) and the pulse wave data MD are multiplied to perform wavelet transform in units of heartbeats, thereby generating pulse wave analysis data MKD. In this example, pulse wave analysis data MKD is 0 Hz to 0.5 Hz, 0.5 Hz to 1.0 Hz, 1.0 Hz to 1.5 Hz, 1.5 Hz to 2.0 Hz, 2.0 Hz to 2.5 Hz, 2 The output is divided into frequency regions such as .5 Hz to 3.0 Hz, 3.0 Hz to 3.5 Hz, and 3.5 Hz to 4.0 Hz.
FIG. 12 shows the pulse wave analysis data MKD for a part of the pulse wave waveform MH. In this figure, the period T is in the vicinity of the peak P4, and the pulse wave analysis data MKD is obtained at time intervals obtained by dividing the period T into eight. By the way, in wavelet transform, since the frequency resolution and the time resolution are in a trade-off relationship, the pulse wave analysis data can be obtained at shorter time intervals if the frequency resolution is sacrificed.
Thus, the generated pulse wave analysis data MKD and body motion analysis data TKD are subjected to frequency correction by the first and second frequency correction units 21 and 23, and the pulse wave correction data MKD ′ and body motion correction are performed. Output as data TKD '.
[0086]
1-2. Body motion removal unit
Next, the body motion removal unit 11 subtracts the body motion correction data TKD ′ from the pulse wave correction data MKD ′ to generate body motion removal pulse wave data MKD ″. This point will be specifically described. In the following description, it is assumed that the user lifts the cup by hand and then returns it to the original position. In this case, the pulse wave waveform MH shown in FIG. 13 (a) is detected by the pulse wave detection sensor unit 130, and the body motion waveform MHt shown in FIG. 13 (b) is simultaneously detected by the waveform processing unit 10. And
[0087]
Here, the body motion waveform MHt starts to increase from time T1, reaches a positive peak at time T2, then gradually decreases, passes level 0 at time T2, reaches a negative peak at time T3, and reaches time T4. It has returned to level 0. By the way, since the body motion waveform TH is detected by the acceleration sensor 21, the time T3 corresponds to the time when the user lifted the cup to the maximum, the time T1 corresponds to the lifting start time, and the time T4 has the holding time. Corresponds to the upper end time. Therefore, the period from time T1 to time T4 is a period in which body motion exists. FIG. 14C shows the pulse wave waveform MH ′ when there is no body movement. In this example, the fundamental wave frequency of the pulse wave waveform MH is 1.3 Hz.
[0088]
Here, FIG. 14 shows the pulse wave correction data MKD ′ in the period Tc (see FIG. 13), and FIG. 15 shows the body motion correction data TKD ′ in the period Tc. From this figure, it can be seen that the body motion waveform TH includes a relatively large level of frequency components in the frequency range of 0.0 Hz to 1.0 Hz. When the pulse wave correction data MKD ′ and the body motion correction data TKD ′ are supplied to the body motion removal unit 11, the body motion removal unit 11 subtracts the body motion correction data TKD ′ from the pulse wave correction data MKD ′. , The body motion removal pulse wave data MKD ″ from which the body motion component is removed is generated as shown in FIG. Thereby, even when there is a body motion, it becomes possible to cancel the influence.
[0089]
1-3. Judgment part
Next, the determination unit 11 ′ compares the body motion waveform TH with a predetermined threshold value to generate a control signal C indicating the presence or absence of body motion, and this is used as the waveform processing unit 10 and the second wavelet transform. To the unit 22 and the second frequency correction unit 23. Thereby, when there is no body movement, the operations of the waveform processing unit 10, the second wavelet conversion unit 22 and the second frequency correction unit 23 are stopped, and the calculation processing time is reduced, the power consumption is reduced, In addition, the SN ratio is improved.
[0090]
1-4. Heart rate detector
Next, the heart rate detector 12 calculates a heart rate based on the body motion removal pulse wave data MKD ″. In this case, the heart rate detection unit 12 specifies the maximum peak Pmax in one beat based on the body motion removal pulse wave data MKD ″. Since the high frequency component becomes large at the maximum peak Pmax of the pulse wave waveform MH ′, a threshold value corresponding to the high frequency component is determined in advance, and the body motion removal pulse wave data MKD ″ is compared with the threshold value to obtain the maximum value. The peak Pmax is specified. Then, a time interval T between a certain maximum peak Pmax and the next maximum peak Pmax is obtained, and a heart rate HR is calculated from 60 / T.
[0091]
1-5. Ejection period detector
Next, the ejection period detection unit 13 may be configured in the same manner as in the first embodiment. In this example, the minimum peak Pmin is specified based on the body motion removal pulse wave data MKD ″. At the same time, the negative peak P4 (notch) appearing second after the maximum peak Pmax is specified. In this case, the frequency component corresponding to the minimum peak Pmin and the frequency component corresponding to the peak P4 are stored in advance as threshold values, and these threshold values are compared with the body motion removal pulse wave data MKD ″ to obtain the minimum value. The peak Pmin and the peak P4 are specified, and the time interval between them is calculated as the ejection period ED.
[0092]
1-6. Stroke volume calculation part
Next, the stroke volume calculation unit 14 adds the body motion removal pulse wave data MKD ″ in each frequency region in the ejection period ED to obtain the energy amount E in the period, and contracts based on this. The term area S is calculated.
[0093]
By the way, since the pulse waveform from the minimum peak Pmin to the peak P4 is a relatively steep mountain-like waveform, most of the frequency components contained therein are in the high frequency region. Therefore, the body motion elimination pulse wave data MKD ″ in the low frequency range (for example, 0 Hz to 1 Hz) can be considered as a noise component. Therefore, instead of adding the body motion removal pulse wave data MKD ″ in all frequency regions in the ejection period ED, the energy amount E may be obtained by adding a part thereof.
[0094]
For example, as shown in FIG. 17, if body motion removal pulse wave data MKD ″ is obtained, there are many noise components in the frequency region of 0 Hz to 1 Hz, and therefore the body motion removal pulse in the frequency region of 1 Hz to 4 Hz. The wave data MKD ″ may be added. If the body motion elimination pulse wave data MKD ″ in each frequency region is expressed by Mnm, the energy amount E in this case is given by the following equation.
[Expression 2]

[0095]
Next, the stroke volume SV is calculated by the following equation.

However, Ke is a conversion coefficient between the energy amount E and the area S.
[0096]
As described above, in the second embodiment, since the systolic area S is calculated using wavelet transform, the stroke volume SV can be obtained while removing the noise component of the pulse wave waveform. Therefore, it is possible to calculate an accurate cardiac output CO.
[0097]
D. Third embodiment
In the second embodiment described above, in order to perform frequency analysis by wavelet transform, the first wavelet transform unit 20, the first frequency correction unit 21, the second wavelet transform unit 22, and the second frequency correction. Part 23 was used. In contrast, the third embodiment is different from the second embodiment in that the second wavelet conversion unit 22 and the second frequency correction unit 23 are omitted.
[0098]
Since the external configuration of the cardiac function diagnostic apparatus according to the third embodiment is the same as the external configuration of the first embodiment shown in FIG. 3, the description thereof will be omitted here, and the electrical configuration will be described. FIG. 18 is a block diagram of a cardiac function diagnostic apparatus according to the third embodiment. In the figure, when the body motion removal pulse wave waveform MH ′ from which the body motion component has been removed by the body motion removal unit 11 is generated, the first wavelet conversion unit 20 performs wavelet conversion to the body motion removal pulse wave waveform MH ′. Apply. The first frequency correction unit 21 performs frequency correction on the output of the first wavelet conversion unit 16 to generate body motion removal pulse wave data MKD ″.
[0099]
In this case, the output of the first frequency correction unit 21 is equivalent to the output of the body motion removal unit 11 shown in FIG. That is, since the wavelet transform is linear, the order of processing may be changed. Therefore, wavelet transform is performed after body motion removal is performed with an analog signal (third embodiment), and the wavelet-transformed pulse is converted. This is because removing the body motion based on the wave correction data MKD ′ and the body motion correction data TKD ′ (second embodiment) is equivalent.
The determination unit 11 ′ is the same as in the first embodiment, and the heart rate detection unit 12, the ejection period detection unit 13, the stroke volume calculation unit 14, the cardiac output calculation unit 15, and the evaluation Since the part 16 and the display part 17 are the same as that of 2nd Embodiment, description is abbreviate | omitted.
[0100]
As described above, according to the third embodiment, the cardiac output CO can be calculated even if the second wavelet conversion unit 22 and the second frequency correction unit 23 are omitted. The state of cardiac function can be diagnosed with a simple configuration.
[0101]
E. Fourth embodiment
In the first to third embodiments, the body motion waveform TH is detected by the acceleration sensor 130, the pulse wave waveform MH is compared with the body motion waveform TH, and the body included in the frequency component of the pulse wave waveform MH. The dynamic component was canceled, the heart rate HR and the ejection period ED were calculated, and the state of cardiac function was diagnosed based on these. However, since the acceleration sensor 130 and the waveform processing unit 10 are required, the configuration is complicated. The fourth embodiment has been made in view of this point, and provides a cardiac function diagnostic apparatus capable of accurately diagnosing the state of cardiac function with a simple configuration even when there is body movement. .
[0102]
Since the external configuration of the cardiac function diagnostic apparatus according to the fourth embodiment is the same as the external configuration of the first embodiment shown in FIG. 3, the description thereof will be omitted here, and the electrical configuration will be described. FIG. 19 is a block diagram of the cardiac function diagnostic apparatus according to the fourth embodiment, in which the acceleration sensor 130, the waveform processing unit 10, the second wavelet conversion unit 22, and the second frequency correction unit 23 are omitted. Except for the point and the internal configuration of the body motion removing unit 11, the configuration is the same as the cardiac function diagnostic apparatus 1 according to the second embodiment shown in FIG. Hereinafter, differences will be described.
The body motion removal unit 11 separates and removes the body motion component from the pulse wave correction data MKD ′ to generate body motion separated pulse wave data TBD. Here, the body motion removing unit 11 utilizes the properties of body motion described below.
[0103]
The body movement is caused by the vertical movement of the arm, the swing of the arm during running, etc., but in daily life, the human body is hardly moved instantaneously. For this reason, in daily life, the frequency component of the body motion waveform TH is not so high and is usually in the range of 0 Hz to 1 Hz. In this case, the fundamental frequency of the pulse wave waveform MH is often in the range of 1 Hz to 2 Hz. Therefore, in daily life, the frequency component of the body motion waveform TH is in a frequency region lower than the fundamental frequency of the pulse wave waveform MH.
[0104]
On the other hand, during sports such as jogging, the frequency component of the body movement waveform TH is somewhat higher due to the influence of arm swinging, etc., but the heart rate increases according to the amount of exercise, so the pulse wave waveform The fundamental frequency of MH also increases at the same time. For this reason, even during sports, the frequency component of the body movement waveform TH is usually in a frequency region lower than the fundamental frequency of the pulse wave waveform MH.
[0105]
The body motion removal unit 11 focuses on this point and separates the body motion component, and is configured to ignore a frequency region lower than the fundamental wave component of the pulse wave waveform MH. In this case, if a body motion component exists in a frequency region higher than the fundamental wave component of the pulse wave waveform MH, the detection accuracy of the cardiac function is lowered. However, since the body motion component has a high probability of being in a lower frequency region than the fundamental wave component of the pulse wave waveform MH as described above, the state of cardiac function can be diagnosed with high accuracy.
[0106]
FIG. 20 is a detailed block diagram of the body movement removing unit 11. The waveform shaping unit 301 performs waveform shaping on the pulse wave waveform MH and generates a reset pulse synchronized with the pulse wave waveform MH. The counter 302 counts clock pulses (not shown), and the count value is reset by the reset pulse. The average value calculation circuit 303 calculates the average value of the count values of the counter 302. In this case, the average value calculated by the average value calculation circuit 303 corresponds to the average period of the pulse wave waveform MH. Therefore, the fundamental frequency of the pulse wave waveform MH can be detected by referring to the average value.
[0107]
Next, the replacement circuit 304 specifies a frequency region including the fundamental frequency of the pulse wave waveform MH based on the average value. For example, when the average value indicates 0.71 seconds, the fundamental frequency is 1.4 Hz, and thus the specified frequency region is 1 Hz to 1.5 Hz. Thereafter, the replacement circuit 304 generates the body motion separated pulse wave data TBD by replacing the pulse wave correction data MKD ′ with “0” in a frequency region less than the specific frequency region. Thereby, components in the frequency region lower than the fundamental frequency of the pulse wave waveform MH are ignored. In this case, the pulse wave component is also replaced with “0” together with the body motion component, but the characteristic part of the pulse wave waveform MH exists in a frequency region higher than the fundamental frequency, and thus is set to “0”. Even if it is replaced, the pulse waveform is hardly affected.
[0108]
For example, if the pulse wave waveform MH (fundamental wave frequency: 1.3 Hz) shown in FIG. 13A is detected by the pulse wave detection sensor unit 130, the pulse wave correction data MKD ′ for the period Tc is As shown in FIG.
In this case, since the frequency region specified by the replacement circuit 194 is 1.0 Hz to 1.5 Hz, the frequency region to be replaced is Ma12 to Ma82 and 0 Hz to 0 corresponding to 0.5 Hz to 1.0 Hz. Ma11 to Ma81 corresponding to 5 Hz. Therefore, the data Ma12 to Ma82 and Ma11 to Ma81 of the pulse wave correction data MKD ′ are replaced with “0”, and the body motion removal pulse wave data MKD ″ shown in FIG. 21 is generated.
Based on the body motion removal pulse wave data MKD ″ generated in this way, the heart rate detection unit 12 and the ejection period detection unit 13 shown in FIG. 19 detect the heart rate HR and ejection period ED, respectively.
[0109]
As described above, according to the fourth embodiment, the body motion component is skillfully utilized in the body motion property that it is probable that the body motion component is present in a lower frequency region than the fundamental frequency component of the pulse wave waveform MH. The dynamic component was removed. For this reason, configurations such as the acceleration sensor 130 and the waveform processing unit 10 required in the first to third embodiments can be omitted, and the state of the cardiac function can be accurately diagnosed even when there is a body motion. It becomes possible.
[0110]
F. Fifth embodiment
The fifth embodiment relates to a modification of the stroke volume calculation unit 14 described in the first embodiment, and other components are the same as those of the first embodiment. The stroke volume calculation unit 14 of the fifth embodiment has the following modes.
[0111]
1. First aspect
First, in the first mode, the stroke volume SV is calculated from the blood pressure values of the peaks P1 to P4 of the body motion removal pulse wave waveform MH ′ in the ejection period ED and their generation times. The body motion elimination pulse wave waveform MH ′ is as shown in FIG. 6, and if the period from P0 to P4 is the ejection period ED, the stroke volume SV is calculated by the following equation.

In this example, the systolic area S is calculated by linear approximation, and the stroke volume SV is calculated based on this.
Therefore, the amount of calculation can be reduced compared to the method of calculating the area S by adding the body motion removal pulse wave waveform MH ′ for all samples in the ejection period ED.
[0112]
2. Second aspect
Next, in the second mode, the stroke volume SV is obtained from the ejection period ED and the heart rate HR. Here, the systolic area S in the systolic area method is calculated from the ejection period ED and the heart rate HR.
[0113]
The pulse wave waveform MH includes individual differences or individual differences even for the same individual. By measuring a large number of actually measured data, the approximate shape of the pulse wave waveform MH at a certain heart rate HR is specified. be able to. When the pulse wave waveform MH is specified, the area S corresponding to the ejection period ED can be obtained.
[0114]
FIG. 22 is a block diagram of the stroke volume detection unit 14 according to the second mode. A stroke volume table 140 stores a systolic area S in association with the cardiac ejection period ED and the heart rate HR. This stroke volume table 140 is composed of a plurality of tables TB1, TB2,... TBn provided for each heart rate HR, and each table TB1, TB2,. The attached systolic area S is stored. These table contents are generated by a large number of actually measured data. Reference numeral 141 denotes a multiplier provided at the subsequent stage of the stroke volume table 140, which multiplies the coefficient Ksv and the systolic area S to calculate the stroke volume SV.
[0115]
With the above configuration, when the ejection period ED and the heart rate HR are supplied to the stroke volume table 140, the stroke volume detection unit 140 stores one table TB corresponding to the heart rate HR. Identify. Thereafter, when the systolic area S corresponding to the ejection period ED is read from the table TB, the multiplier 141 calculates the stroke volume SV.
[0116]
As described above, in the second mode, since the stroke volume SV can be calculated only from the ejection period ED and the heart rate HR, the stroke volume can be stroked in a short time with a simple configuration. The quantity SV can be determined.
[0117]
If the stroke volume SV associated with the ejection period ED is stored in each of the tables TB1, TB2,... TBn described above, the multiplier 141 can be omitted. In this case, instead of the systolic area S, S * Ksv may be stored in each table TB1, TB2,.
[0118]
Further, if the cardiac output CO associated with the ejection period ED is stored in each of the above-described tables TB1, TB2,... TBn, the cardiac output CO is directly calculated from the ejection period ED and the heart rate HR. Can be sought. FIG. 23 is a diagram showing a cardiac output table 140 '. In this case, instead of the systolic area S, S * Ksv * HR may be stored in each table TB1, TB2,.
[0119]
G. Sixth embodiment
The cardiac function diagnostic apparatus 1 according to the first to fifth embodiments described above applies the systolic area method, and multiplies the coefficient Ksv in the area S of the pulse wave waveform in the ejection period ED, thereby making the stroke volume SV. Was calculated. Here, the coefficient Ksv is strictly different for each subject. Therefore, in order to calculate an accurate stroke volume SV, it is desirable to correct the stroke volume SV obtained by the systolic area method.
Therefore, in the sixth embodiment, the stroke volume is calculated between the stroke volume calculation unit 14 and the cardiac output calculation unit 15 in the first to fifth embodiments shown in FIGS. The output amount correction unit 24 is provided to correct the stroke amount SV.
[0120]
FIG. 24 is a block diagram of the stroke volume correction unit 24 according to the present embodiment. As shown in the figure, the stroke volume correction unit 24 includes a correction coefficient calculation unit 240 that calculates a correction coefficient KH, a correction coefficient memory 241 that stores the correction coefficient KH, and a multiplication unit 242.
[0121]
The correction coefficient calculation unit 240 is supplied with a reference stroke volume SVr precisely measured by a thermopigment dilution method or the like from an external device, and the stroke volume calculated by the stroke volume calculation unit 14 is also used. The output amount SV is supplied. The correction coefficient calculation unit 240 includes a divider, and calculates SVr / SV as the correction coefficient KH when the subject operates the operation button to enter the calibration mode. The calculated correction coefficient KH is stored in the correction coefficient memory 241 and is read from and used in the normal measurement mode. The multiplier 242 multiplies the stroke volume SV by the correction coefficient KH to generate a corrected stroke volume SV ′.
[0122]
As described above, according to the present embodiment, the correction coefficient KH is calculated in the calibration mode, and the corrected stroke volume SV ′ is calculated using the correction coefficient KH in the normal measurement mode. The output CO can be obtained.
[0123]
Moreover, the cardiac function diagnostic apparatus 1 of this embodiment is suitable for health management in a hospital or health management during rehabilitation, for example. More specifically, an accurate reference stroke volume SVr is measured by a thermopigment dilution method after surgery for heart disease or the like, and at the same time, the stroke volume SV is measured by the portable cardiac function diagnosis apparatus 1. Then, the correction coefficient KH calculated from these measurement results is stored, and a precise cardiac output CO is obtained using the correction coefficient KH in the normal measurement mode. As a result, the patient can receive a diagnosis of cardiac function based on the accurate cardiac output CO in the process of returning to normal by rehabilitation.
[0124]
H. Seventh embodiment
The seventh embodiment varies the threshold value serving as a reference for the evaluation index X according to the body surface area, and is the same as the configuration of the first to sixth embodiments except for the configuration of the evaluation unit 16. Hereinafter, the evaluation unit 16 which is a difference will be described.
FIG. 25 is a block diagram of the evaluation unit 16 according to the seventh embodiment. Reference numeral 160 denotes a body surface area calculation unit in which body weight W (kg) and height H (cm) are input, and the body surface area TS is calculated based on these. . In this example, the body surface area TS is calculated by a well-known empirical formula called the Dubois formula. The empirical formula is shown below.
TS = W^0.425× H^0.725× 71.84
[0125]
Next, reference numeral 161 denotes a threshold value table, in which threshold values R1 and R2 for generating the evaluation index X are stored in association with the body surface area TS and the heart rate HR. This threshold table 161 is composed of a plurality of tables TB1 ', TB2',... TBn, and threshold values R1, R2 are stored in each table in association with the heart rate HR. When the body surface area TS is supplied, one table corresponding to the body surface area TS is selected from each table. Therefore, referring to the threshold table 161, thresholds R1 and R2 corresponding to the body surface area TS and the heart rate HR can be obtained.
[0126]
Next, reference numeral 162 denotes a comparison unit that compares the thresholds R1 and R2 with the cardiac output CO to generate an evaluation index X.
[0127]
As described above, the threshold values R1 and R2 are made variable by the body surface area TS for the following reason. In general, a person with a large body surface area TS has a large body and a high cardiac output CO, but conversely, a person with a small body surface area TS tends to have a small body and a low cardiac output CO. For this reason, even if both cardiac output CO is evaluated with the same threshold values R1 and R2, it is difficult to evaluate the cardiac function according to the individual, and by using the evaluation index X according to the body surface area TS, This is because it is possible to evaluate the cardiac function according to the individual body shape.
[0128]
The reason why the threshold values R1 and R2 are varied according to the heart rate HR is as follows. When exercise such as running is performed, a large amount of oxygen is consumed in the skeletal muscle, so the heart rate HR increases and the cardiac output CO increases. In other words, the heart rate HR and the cardiac output CO vary according to the exercise intensity. Therefore, by using the evaluation index X corresponding to the heart rate HR, the cardiac function can be continuously evaluated even when the exercise intensity of the subject changes.
[0129]
Therefore, if the cardiac function diagnostic apparatus 1 of the present embodiment is used, the threshold values R1 and R2 can be automatically changed according to the body shape of the subject and the heart rate HR that dynamically changes. It becomes possible to continuously evaluate the cardiac function in life.
[0130]
I. Modified example
It goes without saying that the present invention is not limited to the above-described embodiment, and for example, various modifications described below are possible.
(1) In the second to fourth embodiments described above, the first frequency correction unit 21 or the second frequency correction unit 23 is used for comparing energy in different frequency regions, and the correction result is compared with a threshold value. The maximum peak Pmax and the like were obtained. In this case, the frequency correction unit may be omitted with the threshold value itself considering frequency correction.
[0131]
(2) The wavelet transform performed in the second to fourth embodiments described above may be performed using a filter bank. A configuration example of the filter bank is shown in FIG. In the figure, the filter bank is composed of three stages, and its basic units are a high-pass filter 1A and a decimation filter 1C, and a low-pass filter 1B and a decimation filter 1C. The high-pass filter 1A and the low-pass filter 1B divide a predetermined frequency band and output a high-frequency component and a low-frequency component, respectively. In this example, since 0 Hz to 4 Hz is assumed as the frequency band of the pulse wave data MD, the pass band of the first-stage high-pass filter 1A is set to 2 Hz to 4 Hz, while the first-stage low-pass filter The pass band of 1B is set to 0 Hz to 2 Hz. The decimation filter 1C thins out data every other sample.
When the data thus generated is supplied to the next stage, the frequency band division and the data thinning are repeated, and finally, data M1 to M8 obtained by dividing the frequency band of 0 Hz to 4 Hz into 8 are obtained.
[0132]
Further, the high-pass filter 1A and the low-pass filter 1B may be configured by a transversal filter including a delay element (D flip-flop) therein. By the way, a person's pulse rate is in the range of 40 to 200, and the fundamental frequency of the pulse wave waveform MH fluctuates according to the state of the living body. In this case, if the band to be divided can be varied in synchronization with the fundamental frequency, information that follows the dynamic state of the living body can be obtained. Therefore, the band to be divided may be adaptively varied by setting the clock supplied to the transversal filter to the pulse wave waveform MH.
[0133]
In addition, typical frequency components representing the characteristics of the pulse wave waveform MH in the pulse wave analysis data MKD are the frequency components of the fundamental wave, the second harmonic wave, and the third harmonic wave. Therefore, the image may be determined using a part of the output data M * 1 to M * 8 of the filter bank. In this case, if the filter bank is configured to be synchronized with the pulse wave waveform MH as described above, a part of the high-pass filter 1A, the low-pass filter 1B, and the decimation filter 1C is omitted, and the configuration is simplified. can do.
[0134]
(3) The body motion removal unit 11 in the first embodiment described above may be performed by the wavelet conversion described in the fourth embodiment. In this case, the inverse wavelet shown in Equation 3 is applied to the wavelet from which body motion has been removed to re-synthesize the waveform, and the heart rate HR and ejection period ED are calculated based on the re-synthesized pulse waveform. What is necessary is just to calculate.
[Equation 3]

[0135]
Moreover, what is necessary is just to comprise an inverse wavelet using an inverse filter bank. In this case, the inverse wavelet transform unit may be configured by a filter bank shown in FIG. In the figure, the filter bank is composed of three stages, and its basic units are a high-pass filter 2A and an interpolation filter 2C, a low-pass filter 1B and an interpolation filter 2C, and an adder 2D. The high-pass filter 2A and the low-pass filter 2B divide a predetermined frequency band and output a high-frequency component and a low-frequency component, respectively. The interpolation filter 2C interpolates one sample every two samples.
[0136]
Here, in order to reproduce the waveform, it is necessary to use a complete reconstruction filter bank for the filter bank shown in FIG. 26 and the filter bank shown in FIG. In this case, the characteristics of the high-pass filters 1A and 2A and the low-pass filters 1B and 2B need to have the following relationship.
H0 (-Z) F0 (Z) + H1 (-Z) F1 (Z) = 0
H0 (Z) F0 (Z) + H1 (-Z) F1 (Z) = 2Z^-L
[0137]
Further, the high-pass filter 2A and the low-pass filter 2B may be configured by a transversal filter including a delay element (D flip-flop) therein. Note that the filter bank used in the wavelet converter 10 is synchronized with the fundamental wave frequency of the pulse wave waveform MH, and the band to be divided is varied. Therefore, when the supplied clock is synchronized with the pulse wave waveform MH, This clock may be supplied to the high-pass filter 2A and the low-pass filter 2B.
[0138]
(4) Further, in each of the above-described embodiments, the display unit 17 has been described as an example of the notification unit f8. However, examples of a unit for notifying the person from the apparatus include those described below. . It is considered appropriate to classify these means based on the five senses. Of course, these means are not only used alone, but a plurality of means may be combined. And, as explained below, for example, if a means that appeals to something other than sight is used, the contents of the notification can be understood even by a visually impaired person. Similarly, if a means that appeals to something other than hearing is used, This makes it possible to make a device that is friendly to users with disabilities.
[0139]
First, there are auditory notification means for the purpose of notifying the analysis / diagnosis result of cardiac function or for the purpose of warning. For example, a buzzer, a piezoelectric element, and a speaker are applicable. As a special example, it is conceivable that a person to be notified has a portable wireless call receiver, and when making a notification, this portable wireless call receiver is called from the apparatus side. In addition, when making a notification using these devices, there are many cases where it is desired not only to notify but also to transmit some information together. In such a case, the level of information such as volume shown below may be changed according to the content of the information to be transmitted. For example, the pitch, volume, tone color, voice, and music type (song number, etc.).
[0140]
Next, the visual notification means is used for the purpose of notifying various messages and measurement results from the apparatus or for giving a warning. The following devices can be considered as means for that purpose. For example, there are a display device, a CRT (cathode ray tube display device), an LCD (liquid crystal display), a printer, an XY plotter, a lamp, and the like. Note that there is a glasses-type projector as a special display device. In addition, the following variations can be considered for notification. For example, there are digital display and numerical display in the announcement of numerical values, graph display, display color shading, bar graph display when displaying numerical values as they are or with numerical values graded, pie charts, face charts, and the like. An example of the face chart is shown in FIG.
[0141]
Next, it is considered that a tactile notification means may be used for warning purposes. The following means are available for that purpose. First, there is an electrical stimulus for providing a shape memory alloy that protrudes from the back surface of a portable device such as a wristwatch and energizing the shape memory alloy. Further, there is a mechanical stimulus that gives a stimulus by the protrusion as a structure in which a protrusion (for example, a needle that is not very sharp) can be taken in and out from the back of a portable device such as a wristwatch.
[0142]
Next, the notification means appealing to the sense of smell may be configured such that the device is provided with a discharge mechanism such as a fragrance, the content to be notified is associated with the scent, and the fragrance according to the notification content is discharged. good. Incidentally, a micropump or the like is optimal for a discharge mechanism for a fragrance or the like.
[0143]
(5) In each of the above-described embodiments, the pulse wave detection sensor unit 130 has been described as an example of the pulse wave detection means f1, but the present invention is not limited to this and can detect pulsation. Anything is acceptable.
[0144]
For example, the pulse wave detection sensor unit 130 uses reflected light, but may use transmitted light. By the way, light having a wavelength region of 700 nm or less tends to hardly pass through the finger tissue. For this reason, when using transmitted light, light having a wavelength of 600 nm to 1000 nm is irradiated from the light emitting unit, and the irradiated light is transmitted in the order of tissue → blood vessel → tissue, and a change in the amount of the transmitted light is detected. Since the transmitted light is absorbed by hemoglobin in the blood, the pulse wave waveform can be detected by detecting the change in the amount of the transmitted light.
[0145]
In this case, an InGaAs-based (indium-gallium-arsenic) or GaAs-based (gallium-arsenic) laser light-emitting diode is suitable for the light-emitting portion. By the way, since external light having a wavelength of 600 nm to 1000 nm easily passes through the tissue, the S / N of the pulse wave signal is deteriorated when the external light is incident on the light receiving unit. Therefore, it is also possible to irradiate polarized laser light from the light emitting unit and receive the transmitted light at the light receiving unit via the polarizing filter. As a result, the pulse wave signal can be detected with a good S / N ratio without being affected by external light.
[0146]
In this case, as shown in FIG. 29A, the light emitting unit 400 is provided on the fastening side of the fastener 145, and the light receiving unit 401 is provided on the watch body side. In this case, the light emitted from the light emitting unit 200 passes through the blood vessel 143 and then passes between the ribs 402 and the ulna 403 to reach the light receiving unit 201. In addition, when using transmitted light, since irradiation light needs to permeate | transmit a structure | tissue, when the absorption of a structure | tissue is considered, it is desirable that the wavelength is 600 nm-1000 nm.
[0147]
FIG. 5B shows an example in which the detection site is the earlobe. The gripping member 404 and the gripping member 405 are urged by a spring 407 so that they can rotate around a shaft 406. The gripping member 404 and the gripping member 405 are provided with a light emitting unit 400 and a light receiving unit 401. When this pulse wave detection unit is used, a pulse wave is detected by holding the earlobe with the holding member 404 and the holding member 405. When using reflected light, the pulse wave waveform MH may be detected from the fingertip as shown in FIG.
[0148]
Next, a usage mode in which a photoelectric pulse wave sensor is combined with glasses will be described. In the form of glasses, a display device as a notification means for the user is also incorporated. Therefore, the function as a display device in addition to the pulse wave detection unit will be described together.
FIG. 30 is a perspective view illustrating a state in which the apparatus to which the pulse wave detection unit is connected is attached to the glasses. As shown in the figure, the apparatus main body is divided into a main body 75a and a main body 75b, which are separately attached to the spectacle vine 76, and these main bodies are electrically connected to each other via lead wires embedded in the vine 76. ing.
[0149]
The main body 75a has a built-in display control circuit. A liquid crystal panel 78 is attached to the entire surface of the main body 75a on the lens 77 side, and a mirror 79 is fixed to one end of the side surface at a predetermined angle. Yes. Further, a driving circuit for a liquid crystal panel 78 including a light source (not shown) and a circuit for creating display data are incorporated in the main body 75a. The light emitted from this light source is reflected by the mirror 79 through the liquid crystal panel 78 and projected onto the lens 77 of the glasses. Further, the main part of the apparatus is incorporated in the main body 75b, and various buttons are provided on the upper surface thereof. The functions of these buttons 80 and 81 are different for each apparatus. Also. The LED 32 and the phototransistor 33 (see FIG. 4) constituting the photoelectric pulse wave sensor are built in the pads 82 and 83, and the pads 82 and 83 are fixed to the earlobe. These pads 82 and 83 are electrically connected by lead wires 84 and 84 drawn from the main body 75b.
[0150]
Next, an example in which the pulse waveform MH is detected by the pressure sensor will be described. FIG. 31A is a perspective view showing an external configuration of a cardiac function diagnostic apparatus using a pressure sensor. As shown in this figure, the cardiac function diagnostic apparatus 1 is provided with a pair of bands 144 and 144, and the elastic rubber 131 of the pressure sensor 130 ′ projects from the tightening side of one of the fasteners 145. Is provided. The band 144 including the fastener 145 has a structure (details not shown) in which a flexible printed circuit (FPC) substrate is covered with a soft plastic so as to supply a detection signal from the pressure sensor 130.
[0151]
In use, the wristwatch 146 is wound around the left arm 147 of the subject so that the elastic rubber 131 provided on the fastener 145 is positioned in the vicinity of the radial artery 143 as shown in FIG. The For this reason, it becomes possible to detect a pulse wave constantly. This winding does not change from the normal usage state of a wristwatch. When the elastic rubber 131 is pressed in the vicinity of the radial artery 143 of the subject in this way, the blood flow fluctuation (that is, the pulse wave) of the artery is transmitted to the pressure sensor 130 ′ via the elastic rubber 131, and the pressure sensor 130 ′ Is detected as blood pressure.
[0152]
(6) In the first embodiment described above, the heart rate HR may be obtained using FFT. In this case, the heart rate HR may be calculated by measuring the fundamental frequency f and calculating f · 60.
[0153]
(7) In each of the above-described embodiments, a personal database that stores the cardiac output CO corresponding to the heart rate HR may be provided. In this case, if the cardiac output CO is automatically stored in the personal database, and the past cardiac output CO is displayed on the display unit 17 when the operation button is operated, the subject can be You can know the trend of CO output. For example, when training such as running is performed, the effect of training can be known from this trend. In the case of rehabilitation of heart disease, the degree of recovery of cardiac function can be known by trend.
[0154]
(8) In each of the above-described embodiments, since there is no body movement in the apparatus for calculating the cardiac output at rest, the acceleration sensor 130 ′ (body movement detection means) that detects the body movement waveform, The body motion removal unit 11 that removes the body motion component from the wave waveform MH and generates the body motion removal pulse waveform MH ′ may not be used. In this case, if the stroke volume calculation unit 14 described in the fifth embodiment is used, the number of calculation steps can be reduced, so that the processing time and power consumption can be reduced.
[0155]
(9) In each of the above-described embodiments, the cardiac function diagnostic apparatus 1 has been described. Since the stroke volume SV is calculated, it is needless to say that these configurations can be grasped as a stroke volume detection device. Further, since the cardiac output CO is calculated by the configuration from the pulse wave detection sensor unit 130 to the cardiac output calculation unit 15, it is needless to say that these configurations can be grasped as a cardiac output detection device.
[0156]
(10) In each of the above-described embodiments, the cardiac function diagnostic device 1 has been described. Since the stroke volume SV is calculated, it is needless to say that these configurations can be grasped as a stroke volume detection device. Further, since the cardiac output CO is calculated by the configuration from the pulse wave detection sensor unit 130 to the cardiac output calculation unit 15, it is needless to say that these configurations can be grasped as a cardiac output detection device.
[0157]
(11) In the above-described sixth embodiment, correction is performed based on the precisely measured reference stroke volume SVr and the stroke volume SV calculated by the stroke volume calculation unit 14. The coefficient KH is calculated and stored in the correction coefficient memory 241, but the correction coefficient TH may be stored in the correction coefficient memory 241 in association with the heart rate HR. In this case, a more precise cardiac output CO can be calculated by reading the correction coefficient TH corresponding to the heart rate HR in the normal measurement mode.
[0158]
Further, the correction coefficient KH may be calculated not for the stroke volume SV but for the cardiac output CO corresponding to the heart rate HR. In this case, the reference cardiac output CO ′ precisely measured from the external device and the cardiac output CO calculated by the cardiac output calculator 15 are supplied to the correction coefficient calculator 240, where The generated correction coefficient KH may be stored in the correction coefficient memory 241 in association with the heart rate HR. In the normal measurement mode, the cardiac output corrected by multiplying the correction coefficient KH read from the correction coefficient memory 241 in accordance with the current heart rate HR by the cardiac output CO with the multiplier 242. The amount CO may be obtained.
[0159]
【The invention's effect】
As described above, according to the invention specific matter of the present invention, the cardiac output can be continuously detected during exercise and daily life with a simple configuration. Further, the cardiac function can be evaluated based on the cardiac output.
[Brief description of the drawings]
FIG. 1 is a functional block diagram showing a functional configuration of a cardiac function diagnostic apparatus according to an embodiment of the present invention.
[Figure 2]
FIG. 3 is a perspective view showing an external configuration of the cardiac function diagnostic apparatus according to the first embodiment.
FIG. 4 is a circuit diagram of a pulse wave detection sensor unit 130 according to the embodiment.
FIG. 5 is a block diagram showing an electrical configuration of the cardiac function diagnostic apparatus according to the embodiment.
FIG. 6 is a view showing an example of a body motion elimination pulse wave waveform MH ′ for one beat according to the embodiment;
FIG. 7 is a circuit diagram showing a specific configuration of a beat number detection unit and an ejection period detection unit according to the embodiment.
FIG. 8 is a diagram showing an example of a pulse wave waveform according to the embodiment.
FIG. 9 is a diagram showing the content of peak information according to the embodiment.
FIG. 10 is a block diagram showing an electrical configuration of a cardiac function diagnostic apparatus according to a second embodiment.
FIG. 11 is a block diagram showing a configuration of a first wavelet conversion unit according to the embodiment;
FIG. 12 shows pulse wave analysis data for a partial period of the pulse wave waveform according to the embodiment.
FIG. 13 is a timing chart for explaining the operation of the body movement removing unit according to the embodiment;
FIG. 14 is a diagram showing pulse wave correction data MKD ′ in a period Tc in the embodiment.
FIG. 15 is a diagram showing body motion correction data TKD ′ in a period Tc in the embodiment.
FIG. 16 is a view showing pulse wave correction data MKD ″ from which body motion components are removed in the same embodiment;
FIG. 17 is a diagram for explaining the operation of a stroke volume calculation unit in the same embodiment;
FIG. 18 is a block diagram of a cardiac function diagnostic apparatus according to a third embodiment.
FIG. 19 is a block diagram of a cardiac function diagnostic apparatus according to a fourth embodiment.
FIG. 20 is a detailed block diagram of a body motion removal unit 11 according to the embodiment.
FIG. 21 is a diagram showing an example of body motion removal pulse wave data MKD ″ according to the embodiment;
FIG. 22 is a block diagram illustrating a configuration of a stroke volume calculating unit according to a fifth embodiment.
FIG. 23 is a block diagram showing a cardiac output table according to the embodiment.
FIG. 24 is a block diagram of a stroke volume correction unit 24 according to the sixth embodiment.
FIG. 25 is a block diagram of an evaluation unit 15 according to the seventh embodiment.
FIG. 26 is a block diagram illustrating an example in which wavelet transform is configured by a filter bank in a modified example.
FIG. 27 is a block diagram illustrating an example in which inverse wavelet transform is configured with a filter bank in a modified example.
FIG. 28 is a diagram showing a face chart as one mode of notification means in a modified example.
FIG. 29 is a diagram illustrating an example of a photoelectric pulse wave sensor according to a modification.
FIG. 30 is a diagram showing an example in which the photoelectric pulse wave sensor is applied to eyeglasses in a modified example.
FIG. 31 is a perspective view showing an external configuration of a cardiac function diagnostic apparatus using a pressure sensor in a modified example.
FIG. 32 is a diagram for explaining a systolic area method.
[Explanation of symbols]
130 Pulse wave detection sensor unit (pulse wave detection means)
130 'acceleration sensor (body motion detection means)
11 Body motion removing unit (body motion removing means)
12 Heart rate detector (Heart rate detector)
13 Ejection period detection unit (ejection period detection means)
17 Display (notification means)
20 1st wavelet conversion part (1st wavelet conversion means)
21 1st frequency correction part (1st frequency correction means)
22 2nd wavelet conversion part (2nd wavelet conversion means)
23 Second frequency correction unit (second frequency correction means)

Claims (21)

Pulse wave detection means for detecting a pulse wave waveform from a detection site of a living body;
Body motion detecting means for detecting a body motion waveform indicating the body motion of the living body;
Body motion removing means for generating a body motion component in the pulse wave waveform based on the body motion waveform, and removing the body motion component from the pulse wave waveform to generate a body motion removal pulse wave waveform;
Heart rate detecting means for detecting the heart rate of the living body;
An ejection period detecting means for detecting an ejection period of the heart based on the body motion removal pulse waveform;
A cardiac output detecting device comprising: a cardiac output calculating means for calculating cardiac output based on the cardiac ejection period and the heart rate. Pulse wave detection means for detecting a pulse wave waveform from a detection site of a living body;
Body motion detecting means for detecting a body motion waveform indicating the body motion of the living body;
Body motion removing means for generating a body motion component in the pulse wave waveform based on the body motion waveform, and removing the body motion component from the pulse wave waveform to generate a body motion removal pulse wave waveform;
Heart rate detecting means for detecting the heart rate of the living body;
An ejection period detecting means for detecting an ejection period of the heart based on the body motion removal pulse waveform;
A cardiac output detecting device comprising: a cardiac output calculating means for calculating a cardiac output based on the body movement-removed pulse wave waveform and the heart rate during the ejection period of the heart . Determination means for determining the presence or absence of body movement of the living body based on the body movement waveform detected by the body movement detection means;
The body movement removing unit stops the body movement removing operation when the determination result of the determining unit indicates no body movement, and outputs the pulse wave waveform instead of the body movement removing pulse wave waveform. The cardiac output detection device according to claim 1, wherein   3. The cardiac output detection according to claim 1, wherein the heart rate detection means obtains the heart rate based on a periodicity of the electrocardiogram waveform of the heart or the body motion elimination pulse waveform. apparatus.   The said heart rate detection means performs a frequency analysis on the electrocardiogram waveform of the heart or the body motion elimination pulse wave waveform, and obtains the heart rate based on the analysis result. Cardiac output detection device.   The ejection period detecting means detects each peak of the body movement elimination pulse wave waveform, and specifies the negative peak and the minimum peak appearing first or second from the maximum peak, thereby determining the ejection period. The cardiac output detection device according to claim 1, wherein the cardiac output is detected. Pulse wave detection means for detecting a pulse wave waveform from a detection site of a living body;
Body motion detecting means for detecting a body motion waveform indicating the body motion of the living body;
Body motion removing means for generating a body motion component in the pulse wave waveform based on the body motion waveform, and removing the body motion component from the pulse wave waveform to generate a body motion removal pulse wave waveform;
Wavelet transforming means for generating body motion removal pulse wave analysis data obtained by performing wavelet transformation on the body motion removal pulse wave waveform to remove body motion for each frequency region;
A heart rate detecting means for detecting a heart rate based on the body motion removed pulse wave analysis data;
Based on the body motion removal pulse wave analysis data, ejection period detection means for detecting the ejection period of the heart,
A cardiac output detecting device comprising: a cardiac output calculating means for calculating a cardiac output based on the body movement-removed pulse wave waveform and the heart rate during the ejection period of the heart . Pulse wave detection means for detecting a pulse wave waveform from a detection site of a living body;
Body motion detecting means for detecting a body motion waveform indicating the body motion of the living body;
First body motion removal means for generating a body motion component in the pulse wave waveform based on the body motion waveform, and removing the body motion component from the pulse wave waveform to generate a body motion removal pulse wave waveform; ,
Second body motion removing means for generating body motion removed pulse wave analysis data obtained by performing wavelet transform on the body motion removed pulse wave waveform and removing the body motion for each frequency region;
Based on each corresponding frequency, frequency correction means for generating correction pulse wave data by performing correction so as to normalize the power per frequency to the body motion removal pulse wave analysis data,
Heart rate detecting means for detecting a heart rate based on the corrected pulse wave data;
An ejection period detecting means for detecting an ejection period of the heart based on the corrected pulse wave data;
A cardiac output detecting device comprising: a cardiac output calculating means for calculating a cardiac output based on the body movement-removed pulse wave waveform and the heart rate during the ejection period of the heart . Pulse wave detection means for detecting a pulse wave waveform from a detection site of a living body;
First wavelet transforming means for performing wavelet transform on the pulse wave waveform to generate pulse wave analysis data for each frequency domain;
Body motion detecting means for detecting a body motion waveform indicating the body motion of the living body;
Second wavelet transforming means for performing wavelet transform on the body motion waveform to generate body motion analysis data for each frequency domain;
Body motion removing means for subtracting the body motion analysis data from the pulse wave analysis data to generate body motion removed pulse wave analysis data from which body motion has been removed;
A heart rate detecting means for detecting a heart rate based on the body motion removed pulse wave analysis data;
Based on the body motion removal pulse wave analysis data, ejection period detection means for detecting the ejection period of the heart,
A cardiac output calculating means for calculating a cardiac output based on a result obtained by adding the body motion removal pulse wave analysis data of each frequency region in the ejection period of the heart and the heart rate; A characteristic cardiac output detection device. Pulse wave detection means for detecting a pulse wave waveform from a detection site of a living body;
First wavelet transforming means for performing wavelet transform on the pulse wave waveform to generate pulse wave analysis data for each frequency domain;
First frequency correction means for generating correction pulse wave analysis data by correcting the pulse wave analysis data so as to normalize the power per frequency based on each corresponding frequency;
Body motion detecting means for detecting a body motion waveform indicating the body motion of the living body;
Second wavelet transforming means for performing wavelet transform on the body motion waveform to generate body motion analysis data for each frequency domain;
Second frequency correction means for generating corrected body motion analysis data by performing correction so as to normalize the power per frequency to the body motion analysis data based on each corresponding frequency;
Body motion removing means for subtracting the corrected body motion analysis data from the corrected pulse wave analysis data to generate body motion removed pulse wave analysis data from which body motion has been removed;
A heart rate detecting means for detecting a heart rate based on the body motion removed pulse wave analysis data;
Based on the body motion removal pulse wave analysis data, ejection period detection means for detecting the ejection period of the heart,
A cardiac output calculating means for calculating a cardiac output based on a result obtained by adding the body motion removal pulse wave analysis data of each frequency region in the ejection period of the heart and the heart rate; A characteristic cardiac output detection device.   The cardiac output detection device according to claim 9 or 10, wherein the first wavelet conversion unit and the second wavelet conversion unit perform wavelet conversion in synchronization. Pulse wave detection means for detecting a pulse wave waveform from a detection site of a living body;
Wavelet transforming means for performing wavelet transform on the pulse wave waveform detected by the pulse wave detecting means to generate pulse wave analysis data for each frequency region;
A body motion removing means for generating a body motion removal pulse wave analysis data by removing a frequency component corresponding to a predetermined body motion from the pulse wave analysis data;
A heart rate detecting means for detecting a heart rate based on the body motion removed pulse wave analysis data;
Based on the body motion removal pulse wave analysis data, ejection period detection means for detecting the ejection period of the heart,
A cardiac output calculating means for calculating a cardiac output based on a result obtained by adding the body motion removal pulse wave analysis data of each frequency region in the ejection period of the heart and the heart rate; A characteristic cardiac output detection device. Pulse wave detection means for detecting a pulse wave waveform from a detection site of a living body;
Wavelet transforming means for performing wavelet transform on the pulse wave waveform detected by the pulse wave detecting means to generate pulse wave analysis data for each frequency region;
A body motion removing means for generating a body motion removal pulse wave analysis data by removing a frequency component corresponding to a predetermined body motion from the pulse wave analysis data;
Based on each corresponding frequency, frequency correction means for generating correction pulse wave analysis data by performing correction to normalize the power per frequency to the body motion removal pulse wave analysis data,
Heart rate detecting means for detecting a heart rate based on the corrected pulse wave analysis data;
Based on the corrected pulse wave analysis data, ejection period detection means for detecting the ejection period of the heart,
A cardiac output calculating means for calculating a cardiac output based on a result obtained by adding the corrected pulse wave analysis data of each frequency region in the ejection period of the heart and the heart rate; A cardiac output detection device. Pulse wave detection means for detecting a pulse wave waveform from a detection site of a living body;
Wavelet transforming means for performing wavelet transform on the pulse wave waveform detected by the pulse wave detecting means to generate pulse wave analysis data for each frequency region;
A body motion removing means for generating a body motion removal pulse wave analysis data by removing a frequency component corresponding to a predetermined body motion from the pulse wave analysis data;
An inverse wavelet transforming means for generating a body motion removal pulse wave waveform by applying an inverse wavelet transform to the body motion removal analysis pulse wave data;
A heart rate detecting means for detecting a heart rate based on the body motion removal pulse waveform;
An ejection period detecting means for detecting an ejection period of the heart based on the body motion removal pulse waveform;
A cardiac output detecting device comprising: a cardiac output calculating means for calculating a cardiac output based on the body movement-removed pulse wave waveform and the heart rate during the ejection period of the heart .   The cardiac output calculating means calculates an area of the body motion removal pulse wave waveform corresponding to the period by integrating the body motion removal pulse wave waveform in the ejection period of the heart, and based on the area The cardiac output detection device according to any one of claims 1 to 8, wherein the cardiac output is calculated.   The cardiac output calculating means calculates an area of the body motion removal pulse waveform corresponding to the period based on each peak value of the body motion removal pulse waveform during the cardiac ejection period, and calculates the area. 15. The cardiac output detection device according to claim 1, wherein the cardiac output is calculated based on the cardiac output. Storage means for storing a ratio of the reference cardiac output measured by the reference device and the cardiac output measured by the cardiac output calculating means as a correction coefficient;
Multiplying means for multiplying the correction coefficient read from the storage means by the cardiac output calculated by the cardiac output calculating means and outputting the multiplication result as cardiac output. The cardiac output detection device according to any one of claims 1 to 16, characterized in that: A cardiac function diagnosis device comprising the cardiac output detection device according to any one of claims 1 to 16,
A cardiac function diagnosis apparatus comprising notification means for notifying a cardiac output detected by the cardiac output detection apparatus. A cardiac function diagnosis device comprising the cardiac output detection device according to any one of claims 1 to 16,
An evaluation means for generating an evaluation index by comparing the cardiac output detected by the cardiac output detection device with each threshold;
A cardiac function diagnostic apparatus comprising: notification means for notifying an evaluation index generated by the evaluation means.   The cardiac function diagnosis apparatus according to claim 19, wherein the evaluation unit includes a changing unit that changes the thresholds according to the heart rate detected by the heart rate detection unit. The evaluation means includes
An input unit for inputting parameters for calculating the body surface area of the subject;
A calculation unit for calculating the body surface area based on the input parameters;
The cardiac function diagnostic apparatus according to claim 19, further comprising: a changing unit that changes the threshold values based on the calculated body surface area.

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