JP2015188637A - Respiratory sound analysis apparatus, respiratory sound analysis method, computer program, and recording medium - Google Patents

Respiratory sound analysis apparatus, respiratory sound analysis method, computer program, and recording medium Download PDF

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JP2015188637A
JP2015188637A JP2014068856A JP2014068856A JP2015188637A JP 2015188637 A JP2015188637 A JP 2015188637A JP 2014068856 A JP2014068856 A JP 2014068856A JP 2014068856 A JP2014068856 A JP 2014068856A JP 2015188637 A JP2015188637 A JP 2015188637A
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sound
component
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respiratory sound
whistle
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JP6298339B2 (en
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英幸 大久保
Hideyuki Okubo
英幸 大久保
隆真 亀谷
Ryushin Kametani
隆真 亀谷
長谷部 剛
Takeshi Hasebe
剛 長谷部
石戸谷 耕一
Koichi Ishidoya
耕一 石戸谷
友博 三浦
Tomohiro Miura
友博 三浦
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Pioneer Corp
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Abstract

PROBLEM TO BE SOLVED: To provide a respiratory sound analysis apparatus capable of suitably analyzing continuous rhonchus contained in respiratory sounds.SOLUTION: The respiratory sound analysis apparatus comprises: component acquisition means 210, 220 for acquiring a respiratory sound component which contains continuous rhonchus; frequency acquisition means 230 for acquiring the frequency corresponding to a specific characteristic contained in the respiratory sound component; and determination means 240 for determining a whistling rale component and a rhonchus component contained in the respiratory sound component on the basis of a relationship between the frequency corresponding to the specific characteristic and the threshold value varying in response to the frequency corresponding to the specific characteristic.

Description

本発明は、例えば連続性ラ音を含む呼吸音を解析する呼吸音解析装置及び呼吸音解析方法、並びにコンピュータプログラム及び記録媒体の技術分野に関する。   The present invention relates to a technical field of a respiratory sound analyzing apparatus and a respiratory sound analyzing method for analyzing a respiratory sound including, for example, continuous rales, a computer program, and a recording medium.

この種の装置として、電子聴診器等によって検出される生体の呼吸音について、含まれている複数の音種(例えば、正常呼吸音と異常呼吸音)を夫々判別するものが知られている。例えば特許文献1では、スペクトル上の局所分散値に基づいて、正常呼吸音と連続性ラ音との判別を行うという手法が提案されている。   As this type of device, there is known a device that discriminates a plurality of sound types (for example, normal breath sound and abnormal breath sound) included in a living body breath sound detected by an electronic stethoscope or the like. For example, Patent Document 1 proposes a method of discriminating between normal breath sounds and continuous rales based on local dispersion values on the spectrum.

特開2004−357758号公報JP 2004-357758 A

しかしながら、上述した特許文献1に記載されているような技術では、連続性ラ音を笛声音と類鼾音とに分類することができない。即ち、呼吸音を正常呼吸音と異常呼吸音である連続性ラ音とに分離できたとしても、連続性ラ音について更なる分離を行えないという技術的問題点がある。   However, with the technique described in Patent Document 1 described above, it is not possible to classify the continuous rales into whistle sounds and similar sounds. That is, there is a technical problem that even if the breathing sound can be separated into the normal breathing sound and the continuous rabble which is the abnormal breathing sound, further separation cannot be performed on the continuous rarity sound.

本発明が解決しようとする課題には、上記のようなものが一例として挙げられる。本発明は、呼吸音に含まれる連続性ラ音を好適に解析可能な呼吸音解析装置及び呼吸音解析方法、並びにコンピュータプログラム及び記録媒体を提供することを課題とする。   Examples of problems to be solved by the present invention include the above. It is an object of the present invention to provide a respiratory sound analysis device and a respiratory sound analysis method, a computer program, and a recording medium that can suitably analyze a continuous rale included in a respiratory sound.

上記課題を解決するための呼吸音解析装置は、呼吸音から連続性ラ音を含む呼吸音成分を取得する成分取得手段と、前記呼吸音成分に含まれる所定の特徴に対応する周波数を取得する周波数取得手段と、前記所定の特徴に対応する周波数と、前記所定の特徴に対応する周波数に応じて変動する閾値との関係に基づいて、前記呼吸音成分に含まれる笛声音成分と類鼾音成分とを判定する判定手段とを備える。   A respiratory sound analysis apparatus for solving the above problem acquires component acquisition means for acquiring a respiratory sound component including a continuous rar sound from a respiratory sound, and acquires a frequency corresponding to a predetermined feature included in the respiratory sound component. Based on the relationship between the frequency acquisition means, the frequency corresponding to the predetermined feature, and the threshold value that varies according to the frequency corresponding to the predetermined feature, the whistle voice component and the analog sound included in the respiratory sound component Determination means for determining a component.

上記課題を解決するための呼吸音解析方法は、呼吸音から連続性ラ音を含む呼吸音成分を取得する成分取得工程と、前記呼吸音成分に含まれる所定の特徴に対応する周波数を取得する周波数取得工程と、前記所定の特徴に対応する周波数と、前記所定の特徴に対応する周波数に応じて変動する閾値との関係に基づいて、前記呼吸音成分に含まれる笛声音成分と類鼾音成分とを判定する判定工程とを備える。   A respiratory sound analysis method for solving the above-described problem is a component acquisition step for acquiring a respiratory sound component including a continuous ra sound from a respiratory sound, and acquires a frequency corresponding to a predetermined feature included in the respiratory sound component. Based on the relationship between the frequency acquisition step, the frequency corresponding to the predetermined feature, and the threshold value that varies according to the frequency corresponding to the predetermined feature, the whistle voice component and the analog sound included in the respiratory sound component A determination step of determining the component.

上記課題を解決するためのコンピュータプログラムは、呼吸音から連続性ラ音を含む呼吸音成分を取得する成分取得工程と、前記呼吸音成分に含まれる所定の特徴に対応する周波数を取得する周波数取得工程と、前記所定の特徴に対応する周波数と、前記所定の特徴に対応する周波数に応じて変動する閾値との関係に基づいて、前記呼吸音成分に含まれる笛声音成分と類鼾音成分とを判定する判定工程とをコンピュータに実行させる。   A computer program for solving the above problems includes a component acquisition step of acquiring a respiratory sound component including a continuous rar sound from a respiratory sound, and a frequency acquisition of acquiring a frequency corresponding to a predetermined feature included in the respiratory sound component A whistle sound component and an analog sound component included in the respiratory sound component based on a relationship between a step, a frequency corresponding to the predetermined feature, and a threshold value that varies according to the frequency corresponding to the predetermined feature; And a determination step of determining

上記課題を解決するための記録媒体は、上述したコンピュータプログラムが記録されている。   The above-described computer program is recorded on a recording medium for solving the above problems.

本実施例に係る呼吸音解析装置の全体構成を示すブロック図である。It is a block diagram which shows the whole structure of the respiratory sound analyzer which concerns on a present Example. 本実施例に係る呼吸音解析装置の動作を示すフローチャートである。It is a flowchart which shows operation | movement of the respiratory sound analyzer which concerns on a present Example. 比較例に係る連続性ラ音判定における問題点を示す概念図である。It is a conceptual diagram which shows the problem in the continuous rarity determination which concerns on a comparative example. 本実施例に係る閾値の変動を示すグラフである。It is a graph which shows the fluctuation | variation of the threshold value which concerns on a present Example. 本実施例に係る閾値の具体的な値を示す表である。It is a table | surface which shows the specific value of the threshold value which concerns on a present Example. 本実施例に係る連続性ラ音判定結果の表示例を示すグラフ(その1)である。It is a graph (the 1) which shows the example of a display of the continuous rarity determination result based on a present Example. 本実施例に係る連続性ラ音判定結果の表示例を示すグラフ(その2)である。It is a graph (the 2) which shows the example of a display of the continuous rarity determination result based on a present Example. 本実施例及び比較例に係る呼吸音解析装置により笛声音を解析する例を示す概念図である。It is a conceptual diagram which shows the example which analyzes a whistle voice sound with the respiratory sound analyzer which concerns on a present Example and a comparative example. 本実施例及び比較例に係る呼吸音解析装置により類鼾音を解析する例を示す概念図である。It is a conceptual diagram which shows the example which analyzes the analogy sound by the respiratory sound analyzer which concerns on a present Example and a comparative example.

<1>
本実施形態に係る呼吸音解析装置は、呼吸音から連続性ラ音を含む呼吸音成分を取得する成分取得手段と、前記呼吸音成分に含まれる所定の特徴に対応する周波数を取得する周波数取得手段と、前記所定の特徴に対応する周波数と、前記所定の特徴に対応する周波数に応じて変動する閾値との関係に基づいて、前記呼吸音成分に含まれる笛声音成分と類鼾音成分とを判定する判定手段とを備える。
<1>
The respiratory sound analysis apparatus according to the present embodiment includes a component acquisition unit that acquires a respiratory sound component including a continuous rar sound from a respiratory sound, and a frequency acquisition that acquires a frequency corresponding to a predetermined feature included in the respiratory sound component. And a whistle sound component and an analog sound component included in the respiratory sound component based on a relationship between the means, a frequency corresponding to the predetermined feature, and a threshold value that varies according to the frequency corresponding to the predetermined feature. Determination means for determining whether or not.

本実施形態に係る呼吸音解析装置によれば、その動作時には、先ず呼吸音から連続性ラ音を含む呼吸音成分が取得される。具体的には、正常呼吸音や異常呼吸音等の様々な音を含む呼吸音から、周知の技術等を利用して、連続性ラ音を含む呼吸音成分が抽出される。ただし、ここで取得される呼吸音成分は、連続性ラ音のみを含む呼吸音成分に限定されるものではない。即ち、取得される呼吸音成分は、連続性ラ音以外の音を含んでいても構わない。   According to the respiratory sound analysis apparatus according to the present embodiment, at the time of operation, first, a respiratory sound component including a continuous rarity is acquired from the respiratory sound. Specifically, a breathing sound component including a continuous rarity is extracted from a breathing sound including various sounds such as a normal breathing sound and an abnormal breathing sound using a known technique or the like. However, the respiratory sound component acquired here is not limited to the respiratory sound component including only continuous rales. That is, the acquired respiratory sound component may include a sound other than the continuous rales.

呼吸音成分が取得されると、呼吸音成分に含まれる所定の特徴に対応する周波数が取得される。なお、ここでの「所定の特徴」とは、呼吸音成分に含まれる音種に応じて特定の周波数に発生する特徴を意味しており、例えば周波数解析された信号に現れるピーク等である。   When the respiratory sound component is acquired, a frequency corresponding to a predetermined feature included in the respiratory sound component is acquired. Here, the “predetermined feature” means a feature that occurs at a specific frequency according to the sound type included in the respiratory sound component, such as a peak that appears in a frequency-analyzed signal.

所定の特徴に対応する周波数が取得されると、取得した周波数に基づいて、呼吸音成分に含まれる笛声音成分と類鼾音成分とが判定される。即ち、連続性ラ音を含む呼吸音成分に、連続性ラ音である笛声音成分及び類鼾音成分が含まれているか否かが判定される。なお、ここでの判定は、呼吸音成分中の笛声音成分及び類鼾音成分の存在に関する判定であれば特に限定されるものではない。例えば、呼吸音成分に、笛声音成分及び類鼾音成分のいずれが含まれているか、又は両方が含まれているのかを判定するものであってもよい。或いは、笛声音成分及び類鼾音成分がどのような割合で含まれているのかを判定するものであってもよい。また、笛声音成分及び類鼾音成分が含まれる可能性を判定するものであっても構わない。   When the frequency corresponding to the predetermined feature is acquired, the whistle vocal sound component and the analogy sound component included in the respiratory sound component are determined based on the acquired frequency. In other words, it is determined whether or not the breathing sound component including the continuous ra sound includes the whistle vocal sound component and the analog sound component that are the continuous ra sound. Note that the determination here is not particularly limited as long as it is a determination related to the presence of the whistle voice component and the like sound component in the respiratory sound component. For example, it may be determined whether the breathing sound component includes either a whistle vocal sound component or an analogy sound component, or both. Alternatively, it may be determined at what ratio the whistle voice sound component and the analogy sound component are included. Moreover, you may determine the possibility that a whistle voice sound component and a similar sound component are included.

上述した判定は、所定の特徴に対応する周波数と、所定の特徴に対応する周波数に応じて変動する閾値との関係に基づいて行われる。例えば、取得された周波数と閾値とが互いに比較され、その大小関係に基づいて、笛声音成分及び類鼾音成分の判定が行われる。より具体的には、例えば取得された周波数が閾値より高い場合には笛声音成分であると判定され、低い場合には類鼾音成分と判定される。   The determination described above is performed based on the relationship between the frequency corresponding to the predetermined feature and the threshold value that varies according to the frequency corresponding to the predetermined feature. For example, the acquired frequency and the threshold value are compared with each other, and the whistle sound component and the analogy sound component are determined based on the magnitude relationship. More specifically, for example, when the acquired frequency is higher than the threshold value, it is determined as a whistle voice component, and when it is low, it is determined as an analog sound component.

ここで特に、判定に用いられる閾値は、取得された所定の特徴に対応する周波数に応じて変動するものである。例えば閾値は、取得された周波数が高いほど、類鼾音よりも笛声音と判定する割合が大きくなるように変動する。このように、周波数に応じて閾値を変動させれば、固定値である閾値を用いる場合と比べて、より適切に笛声音及び類鼾音を判別することができる。なお、閾値をどのように変動させるかは、実験結果等に基づいて予め設定しておけばよい。また、閾値の変動特性を、被測定対象である生体の性別、年齢、身長、体重等に応じて決定するようにしてもよい。   Here, in particular, the threshold used for determination varies depending on the frequency corresponding to the acquired predetermined feature. For example, the threshold fluctuates so that the higher the acquired frequency is, the larger the ratio for determining a whistle sound is higher than that of a similar sound. In this way, if the threshold value is varied according to the frequency, it is possible to discriminate the whistle voice sound and the analogy sound more appropriately than in the case of using a fixed threshold value. In addition, what is necessary is just to set beforehand how a threshold value is fluctuated based on an experimental result. Further, the variation characteristics of the threshold may be determined according to the sex, age, height, weight, etc. of the living body to be measured.

以上説明したように、本実施形態に係る呼吸音解析装置によれば、連続性ラ音である笛声音及び類鼾音を好適に判別することが可能である。   As described above, according to the respiratory sound analysis apparatus according to the present embodiment, it is possible to suitably discriminate whistle vocal sounds and analogy sounds that are continuous rales.

<2>
本実施形態に係る呼吸音解析装置の一態様では、前記所定の特徴は、極大値である。
<2>
In one aspect of the respiratory sound analysis apparatus according to the present embodiment, the predetermined feature is a maximum value.

この態様によれば、例えば呼吸音を示す信号に対して、高速フーリエ変換(FFT:Fast Fourier Transform)等による周波数解析が実行され、解析結果の極大値(即ち、ピーク)に対応する周波数に関する情報が取得される。なお、周波数に関する情報は、極大値の位置に対応するものとして取得されるが、極大値の位置と完全に一致する周波数でなくとも、極大値の近傍位置に対応する周波数に関する情報として取得されてもよい。   According to this aspect, for example, a frequency analysis by a fast Fourier transform (FFT) or the like is performed on a signal indicating a respiratory sound, and information on a frequency corresponding to a maximum value (that is, a peak) of the analysis result. Is acquired. Note that the information about the frequency is acquired as corresponding to the position of the maximum value, but even if the frequency is not completely coincident with the position of the maximum value, it is acquired as information about the frequency corresponding to the position near the maximum value. Also good.

上述したように、呼吸音における所定の特徴として極大値を利用することで、より容易且つ的確に周波数を取得できる。   As described above, the frequency can be acquired more easily and accurately by using the maximum value as the predetermined feature in the respiratory sound.

<3>
本実施形態に係る呼吸音解析装置の他の態様では、前記判定手段は、前記呼吸音成分に含まれる前記笛声音成分と前記類鼾音成分との割合を判定する。
<3>
In another aspect of the respiratory sound analysis apparatus according to the present embodiment, the determination unit determines a ratio of the whistle sound component and the analogy sound component included in the respiratory sound component.

この態様によれば、呼吸音成分に、笛声音成分と類鼾音成分とがどのような割合で含まれているかが判定される。よって、例えば呼吸音に基づく健康状態の診断等を適切に行うことが可能となる。なお、笛声音成分及び類鼾音成分以外の成分についても割合を判定するようにしてもよい。   According to this aspect, it is determined at what ratio the breathing sound component contains the whistle vocal sound component and the analogy sound component. Therefore, for example, it is possible to appropriately diagnose the health condition based on the respiratory sound. Note that the ratio may be determined for components other than the whistle sound component and the similar sound component.

<4>
上述した笛声音成分と類鼾音成分との割合を判定する態様では、前記閾値は、前記所定の特徴に対応する周波数が高いほど、前記笛声音成分が含まれる割合が増加し、前記類鼾音成分が含まれる割合が減少するように判定させるものとして変動してもよい。
<4>
In the aspect in which the ratio between the whistle voice component and the analog sound component is determined as described above, the higher the frequency corresponding to the predetermined feature, the higher the frequency corresponding to the predetermined feature, the higher the ratio at which the whistle voice component is included, You may change as what makes it determine so that the ratio in which a sound component is contained decreases.

この場合、類鼾音成分と比べて周波数が高い傾向にある笛声音成分(言い換えれば、笛声音成分と比べて周波数が低い傾向にある類鼾音成分)を精度よく判別することが可能である。なお、判定結果の割合の増減については、直線的な変動であってもよいし、所定の関数に応じた比較的複雑な変動であってもよい。或いは、段階的な変動であっても構わない。   In this case, it is possible to accurately determine a whistle sound component whose frequency tends to be higher than that of the similar sound component (in other words, an analog sound component whose frequency tends to be lower than that of the whistle sound component). . The increase / decrease in the ratio of the determination result may be a linear variation or a relatively complicated variation according to a predetermined function. Alternatively, it may be a stepwise change.

<5>
本実施形態に係る呼吸音解析方法は、呼吸音から連続性ラ音を含む呼吸音成分を取得する成分取得工程と、前記呼吸音成分に含まれる所定の特徴に対応する周波数を取得する周波数取得工程と、前記所定の特徴に対応する周波数と、前記所定の特徴に対応する周波数に応じて変動する閾値との関係に基づいて、前記呼吸音成分に含まれる笛声音成分と類鼾音成分とを判定する判定工程とを備える。
<5>
The respiratory sound analysis method according to the present embodiment includes a component acquisition step of acquiring a respiratory sound component including a continuous ra sound from the respiratory sound, and a frequency acquisition of acquiring a frequency corresponding to a predetermined feature included in the respiratory sound component. A whistle sound component and an analog sound component included in the respiratory sound component based on a relationship between a step, a frequency corresponding to the predetermined feature, and a threshold value that varies according to the frequency corresponding to the predetermined feature; Determining step.

本実施形態に係る呼吸音解析方法によれば、上述した本実施形態に係る呼吸音解析装置と同様に、連続性ラ音である笛声音及び類鼾音を好適に判別することができる。   According to the respiratory sound analysis method according to the present embodiment, it is possible to suitably discriminate the whistle vocal sound and the analogy sound that are continuous rales, as in the respiratory sound analysis device according to the present embodiment described above.

なお、本実施形態に係る呼吸音解析方法においても、上述した本実施形態に係る呼吸音解析装置における各種態様と同様の各種態様を採ることが可能である。   In the respiratory sound analysis method according to the present embodiment, various aspects similar to the various aspects of the respiratory sound analysis apparatus according to the present embodiment described above can be employed.

<6>
本実施形態に係るコンピュータプログラムは、呼吸音から連続性ラ音を含む呼吸音成分を取得する成分取得工程と、前記呼吸音成分に含まれる所定の特徴に対応する周波数を取得する周波数取得工程と、前記所定の特徴に対応する周波数と、前記所定の特徴に対応する周波数に応じて変動する閾値との関係に基づいて、前記呼吸音成分に含まれる笛声音成分と類鼾音成分とを判定する判定工程とをコンピュータに実行させる。
<6>
The computer program according to the present embodiment includes a component acquisition step of acquiring a respiratory sound component including a continuous ra sound from the respiratory sound, and a frequency acquisition step of acquiring a frequency corresponding to a predetermined feature included in the respiratory sound component. And determining a whistle sound component and an analogy sound component included in the respiratory sound component based on a relationship between a frequency corresponding to the predetermined feature and a threshold value varying according to the frequency corresponding to the predetermined feature. And a determination step to be executed by the computer.

本実施形態に係るコンピュータプログラムによれば、上述した本実施形態に係る呼吸音解析方法と同様の処理をコンピュータに実行させることができるため、連続性ラ音である笛声音及び類鼾音を好適に判別することができる。   According to the computer program according to the present embodiment, it is possible to cause the computer to execute the same processing as the respiratory sound analysis method according to the present embodiment described above. Can be determined.

なお、本実施形態に係るコンピュータプログラムにおいても、上述した本実施形態に係る呼吸音解析装置における各種態様と同様の各種態様を採ることが可能である。   Note that the computer program according to the present embodiment can also adopt various aspects similar to the various aspects of the respiratory sound analyzer according to the present embodiment described above.

<7>
本実施形態に係る記録媒体は、上述したコンピュータプログラムが記録されている。
<7>
The recording medium according to the present embodiment records the above-described computer program.

本実施形態に係る記録媒体によれば、上述したコンピュータプログラムをコンピュータにより実行させることにより、連続性ラ音である笛声音及び類鼾音を好適に判別することができる。   According to the recording medium according to the present embodiment, it is possible to suitably determine the whistle sound and the like sound that are continuous rales by causing the computer program described above to be executed by a computer.

本実施形態に係る呼吸音解析装置及び呼吸音解析方法、並びにコンピュータプログラム及び記録媒体の作用及び他の利得については、以下に示す実施例において、より詳細に説明する。   The breathing sound analysis apparatus and breathing sound analysis method according to the present embodiment, the operation of the computer program and the recording medium, and other gains will be described in more detail in the following examples.

以下では、図面を参照して呼吸音解析装置及び呼吸音解析方法、並びにコンピュータプログラム及び記録媒体の実施例について詳細に説明する。   Hereinafter, embodiments of a respiratory sound analysis device, a respiratory sound analysis method, a computer program, and a recording medium will be described in detail with reference to the drawings.

<全体構成>
先ず、本実施例に係る呼吸音解析装置の全体構成について、図1を参照して説明する。ここに図1は、本実施例に係る呼吸音解析装置の全体構成を示すブロック図である。
<Overall configuration>
First, the overall configuration of the respiratory sound analyzer according to the present embodiment will be described with reference to FIG. FIG. 1 is a block diagram showing the overall configuration of the respiratory sound analysis apparatus according to this embodiment.

図1において、本実施例に係る呼吸音解析装置は、主な構成要素として、生体音センサ110と、信号記憶部120と、信号処理部125と、音声出力部130と、表示部140と、処理部200とを備えて構成されている。   In FIG. 1, the respiratory sound analysis apparatus according to the present embodiment includes, as main components, a biological sound sensor 110, a signal storage unit 120, a signal processing unit 125, a sound output unit 130, a display unit 140, And a processing unit 200.

生体音センサ110は、生体の呼吸音を検出可能に構成されたセンサである。生体音センサ110は、例えばECM(Electret Condenser Microphone)やピエゾを利用したマイク、振動センサ等で構成されている。   The biological sound sensor 110 is a sensor configured to be able to detect a respiratory sound of a biological body. The biological sound sensor 110 includes, for example, an ECM (Electret Condenser Microphone), a microphone using a piezo, a vibration sensor, and the like.

信号記憶部120は、例えばRAM(Random Access Memory)等のバッファとして構成されており、生体音センサ110で検出された呼吸音を示す信号(以下、適宜「呼吸音信号」と称する)を一時的に記憶する。信号記憶部120は、記憶した信号を、音声出力部130及び処理部200に夫々出力可能に構成されている。   The signal storage unit 120 is configured as a buffer such as a RAM (Random Access Memory), for example, and temporarily stores a signal indicating a breathing sound detected by the biological sound sensor 110 (hereinafter referred to as “breathing sound signal” as appropriate). To remember. The signal storage unit 120 is configured to be able to output the stored signal to the audio output unit 130 and the processing unit 200, respectively.

信号処理部125は、生体音センサ110で取得した音を加工して音声出力部130に出力する。信号処理部125は、例えばイコライザーやフィルターとして機能し、取得した音を人が聴き易い状態に加工する。   The signal processing unit 125 processes the sound acquired by the biological sound sensor 110 and outputs the processed sound to the audio output unit 130. The signal processing unit 125 functions as, for example, an equalizer or a filter, and processes the acquired sound so that it can be easily heard by a person.

音声出力部130は、例えばスピーカやヘッドホンとして構成されており、生体音センサ110で検出され、信号処理部125で加工された呼吸音を出力する。   The audio output unit 130 is configured, for example, as a speaker or a headphone, and outputs a respiratory sound detected by the biological sound sensor 110 and processed by the signal processing unit 125.

表示部140は、例えば液晶モニタ等のディスプレイとして構成されており、処理部200から出力される画像データを表示する。   The display unit 140 is configured as a display such as a liquid crystal monitor, for example, and displays image data output from the processing unit 200.

処理部200は、複数の演算回路やメモリ等を含んで構成されている。処理部200は、周波数解析部210、連続性ラ音検出部220、ピーク周波数検出部230、笛声音/類鼾音判定部240、及び画像生成部250を備えている。   The processing unit 200 includes a plurality of arithmetic circuits, memories, and the like. The processing unit 200 includes a frequency analysis unit 210, a continuous rale detection unit 220, a peak frequency detection unit 230, a whistle / sound sound determination unit 240, and an image generation unit 250.

処理部200の各部の動作については後に詳述する。   The operation of each unit of the processing unit 200 will be described in detail later.

<動作説明>
次に、本実施例に係る呼吸音解析装置の動作について、図2を参照して説明する。ここに図2は、本実施例に係る呼吸音解析装置の動作を示すフローチャートである。
<Description of operation>
Next, the operation of the respiratory sound analyzer according to the present embodiment will be described with reference to FIG. FIG. 2 is a flowchart showing the operation of the respiratory sound analysis apparatus according to this embodiment.

図2において、本実施例に係る呼吸音解析装置の動作時には、先ず生体音センサ110において呼吸音が検出され、処理部200による呼吸音信号の取得が行われる(ステップS101)。   In FIG. 2, during the operation of the respiratory sound analysis apparatus according to the present embodiment, first, the biological sound sensor 110 detects the respiratory sound, and the processing unit 200 acquires the respiratory sound signal (step S101).

呼吸音信号が取得されると、周波数解析部210において周波数解析(例えば、高速フーリエ変換)が実行される(ステップS102)。周波数解析が実行されると、その結果を用いて、連続性ラ音検出部220において、呼吸音信号に含まれる連続性ラ音を含む成分が検出される(ステップS103)。なお、連続性ラ音を検出する処理は、連続性ラ音以外の呼吸音(例えば、正常呼吸音や、他の異常呼吸音等)を検出する処理と並行して行われてもよい。ここまでの各処理については、周知の技術を利用して行うことが可能であるため、詳細な説明は省略する。   When the respiratory sound signal is acquired, frequency analysis (for example, fast Fourier transform) is performed in the frequency analysis unit 210 (step S102). When the frequency analysis is executed, using the result, the continuous rale detection unit 220 detects a component including the continuous rale included in the respiratory sound signal (step S103). Note that the process for detecting continuous rarity may be performed in parallel with the process for detecting breathing sounds other than continuous rarity (for example, normal breathing sounds, other abnormal breathing sounds, etc.). Since each process so far can be performed using a well-known technique, detailed description is abbreviate | omitted.

連続性ラ音が検出されると(ステップS104:YES)、周波数ピーク検出部230において、連続性ラ音に対応する成分のピーク(極大値)の検出が実行され、ピーク位置に対応する周波数がピーク周波数として検出される(ステップS105)。ピーク検出する場合には、例えば所定の時間間隔(例えば、FFTをかける時間間隔等)で、周波数特性の領域において最大値をとる周波数を求めればよい。   When the continuous rarity is detected (step S104: YES), the frequency peak detection unit 230 detects the peak (maximum value) of the component corresponding to the continuous rarity, and the frequency corresponding to the peak position is detected. It is detected as a peak frequency (step S105). In the case of detecting a peak, for example, a frequency having a maximum value in a frequency characteristic region may be obtained at a predetermined time interval (for example, time interval for applying FFT).

本実施例に係る呼吸音解析装置は、ここで検出されたピーク周波数に基づいて、連続性ラ音が笛声音であるか、又は類鼾音であるかを判定する。具体的には、ピーク周波数と、所定の閾値との大小を比較して、ピーク周波数が所定の周波数より高い場合には笛声音であると判定し、ピーク周波数が所定の周波数より低い場合には類鼾音であると判定する。ただし、本実施例では特に、笛声音及び類鼾音を判定するための閾値として、ピーク周波数に応じて変動する値を用いる。このため本実施例では、ピーク周波数が検出されると、笛声音及び類鼾音の判定に先立って、ピーク周波数に応じた閾値の決定が実行される(ステップS106)。   The respiratory sound analysis apparatus according to the present embodiment determines whether the continuous ra sound is a whistle sound or an analog sound based on the peak frequency detected here. Specifically, the peak frequency is compared with a predetermined threshold, and when the peak frequency is higher than the predetermined frequency, it is determined that the sound is a whistle voice, and when the peak frequency is lower than the predetermined frequency, It is determined that the sound is similar. However, in this embodiment, in particular, a value that varies according to the peak frequency is used as a threshold value for determining the whistle voice sound and the like sound. For this reason, in this embodiment, when the peak frequency is detected, the threshold value is determined according to the peak frequency prior to the determination of the whistle voice and the analogy sound (step S106).

ここで、上述した閾値の決定方法について、図3から図5を参照して具体的に説明する。ここに図3は、比較例に係る連続性ラ音判定における問題点を示す概念図である。また図4は、本実施例に係る閾値の変動を示すグラフであり、図5は、本実施例に係る閾値の具体的な値を示す表である。なお、図3に示されるスペクトルは、正常呼吸音に加えて笛声音を顕著に含む呼吸音のスペクトルである。   Here, the threshold value determination method described above will be specifically described with reference to FIGS. Here, FIG. 3 is a conceptual diagram showing a problem in the continuous rarity determination according to the comparative example. FIG. 4 is a graph showing the variation of the threshold according to the present embodiment, and FIG. 5 is a table showing specific values of the threshold according to the present embodiment. In addition, the spectrum shown by FIG. 3 is a spectrum of the respiratory sound which notably includes a whistle voice sound in addition to a normal respiratory sound.

図3において、笛声音は高音性連続性ラ音、類鼾音は低音性連続性ラ音と呼ばれるように、笛声音と類鼾音とは音の高さ(即ち、周波数)で判別することが可能である。しかしながら、笛声音及び類鼾音は、ピーク周波数が時間的に変化する。このため、図に示すような単一の閾値(即ち、値が変動しない一つの閾値)を利用して笛声音及び類鼾音を判定しようとする比較例では、時間の経過により、判定結果が変化してしまうことがある。例えば、図中に示す笛声音成分のように、ピーク周波数が判定閾値を跨ぐように変化してしまうと、それまでは正確に笛声音と判定されていたものが、誤って類鼾音として判定されることになってしまう。このため本実施例に係る呼吸音解析装置では、ピーク周波数に応じて閾値を変動させる。   In FIG. 3, the whistle sound and the analogy sound are discriminated by the pitch (that is, the frequency) so that the whistle voice sound is called a high-pitched continuous rale and the analogy sound is called a low-pitched continuous rale. Is possible. However, the peak frequency of the whistle sound and the like sound changes with time. For this reason, in the comparative example in which a single threshold value (that is, one threshold value whose value does not vary) as shown in the figure is used to determine the whistle sound and the analogy sound, the determination result is obtained as time passes. It may change. For example, if the peak frequency changes so as to cross the determination threshold, as in the whistle voice component shown in the figure, what was previously determined as a whistle voice sound is erroneously determined as a similar sound Will be done. For this reason, in the respiratory sound analysis apparatus according to the present embodiment, the threshold value is varied according to the peak frequency.

図4及び図5に示すように、本実施例に係る呼吸音解析装置では、笛声音と判定する割合及び類鼾音と判定する割合がピーク周波数に応じてなめらかに変化するように閾値が変動する。例えば、ピーク周波数が200Hzの場合には、笛声音が7%含まれ、類鼾音が93%含まれると判定する。ピーク周波数が250Hzの場合には、笛声音が50%含まれ、類鼾音が50%含まれると判定する。ピーク周波数が280Hzの場合には、笛声音が78%含まれ、類鼾音が22%含まれると判定する。なお、ここでの具体的な数値はあくまで一例であり、異なる値を設定してもよい。また、測定対象である生体の性別、年齢、身長、体重等によって異なる変動特性を有するようにしてもよい。   As shown in FIGS. 4 and 5, in the respiratory sound analysis apparatus according to the present embodiment, the threshold value fluctuates so that the ratio for determining the whistle sound and the ratio for determining the analogy sound change smoothly according to the peak frequency. To do. For example, when the peak frequency is 200 Hz, it is determined that 7% of whistle sounds are included and 93% of similar sounds are included. When the peak frequency is 250 Hz, it is determined that 50% of whistle sounds are included and 50% of similar sounds are included. When the peak frequency is 280 Hz, it is determined that 78% of whistle sounds are included and 22% of similar sounds are included. The specific numerical values here are merely examples, and different values may be set. Moreover, you may make it have a variation characteristic which changes with sex, age, height, weight, etc. of the biological body which is a measuring object.

上述した変動する閾値を利用することで、図3で示したような場合に発生し得る誤判定を好適に防止することができる。即ち、本実施例に係る呼吸音解析装置では、笛声音及び類鼾音を判定するための閾値がピーク周波数に応じて適切な値にとなるよう変動するため、例えば変動しない単一の閾値を用いる場合と比較して、より正確な判定が行える。   By using the above-described fluctuating threshold, it is possible to suitably prevent erroneous determination that may occur in the case shown in FIG. That is, in the respiratory sound analysis apparatus according to the present embodiment, the threshold value for determining the whistle voice sound and the analogy sound changes so as to become an appropriate value according to the peak frequency. Compared with the case of using, more accurate determination can be performed.

図2に戻り、笛声音/類鼾音判定部240では、上述したように変動する閾値を用いて、連続性ラ音が笛声音であるのか、又は類鼾音であるのかが判定される(ステップS107)。そして、画像生成部250では判定結果に基づく画像が生成され、表示部140において判定結果が表示される(ステップS108)。なお、解析結果を表示した後は、解析を継続するか否かが判定される(ステップS109)。解析を継続すると判定された場合(ステップS109:YES)、上述した処理がステップS101から再開される。一方、解析を継続しないと判定された場合(ステップS109:NO)、一連の処理は終了する。   Returning to FIG. 2, the whistle / sound sound determination unit 240 determines whether the continuous ra sound is a whistle sound or a similar sound using the threshold value that fluctuates as described above ( Step S107). Then, the image generation unit 250 generates an image based on the determination result, and the determination result is displayed on the display unit 140 (step S108). Note that after the analysis result is displayed, it is determined whether or not to continue the analysis (step S109). When it is determined that the analysis is to be continued (step S109: YES), the above-described processing is restarted from step S101. On the other hand, when it is determined not to continue the analysis (step S109: NO), the series of processing ends.

ここで、表示部140における解析結果の表示について、図6及び図7を参照して具体的に説明する。ここに図6及び図7は夫々、本実施例に係る連続性ラ音判定結果の表示例を示すグラフである。   Here, the display of the analysis result on the display unit 140 will be specifically described with reference to FIGS. 6 and 7. Here, FIGS. 6 and 7 are graphs showing display examples of the continuous rarity determination result according to the present embodiment, respectively.

図6に示すように、解析結果である笛声音及び類鼾音の割合は、棒グラフとして表示部150に表示される。ただし、この表示方法は一例であり、他の表示態様で表示を行ってもよい。例えば、笛声音及び類鼾音の割合を円グラフとして表示してもよい。或いは、笛声音及び類鼾音の強度を数値化して表示してもよい。   As shown in FIG. 6, the ratio of the whistle sound and the analogy sound, which is the analysis result, is displayed on the display unit 150 as a bar graph. However, this display method is an example, and display may be performed in other display modes. For example, you may display the ratio of a whistle voice sound and an analogy sound as a pie chart. Alternatively, the intensity of the whistle voice sound and the analogy sound may be digitized and displayed.

図7に示すように、笛声音及び類鼾音以外の音種(例えば、正常呼吸音、水泡音、捻髪音等)についても判定可能な場合には、それらの音種の割合も合わせて表示するようにしてもよい。   As shown in FIG. 7, when it is possible to determine sound types other than the whistle voice sound and the like sound (for example, normal breathing sound, water bubble sound, haircut sound, etc.), the ratio of these sound types is also included. You may make it display.

なお、上述した画像としての出力に代えて或いは加えて、音声データによる出力も可能である。具体的には、笛声音と類鼾音とで別々に音声を出力することができる。或いは、笛声音及び類鼾音の一方だけを強調して音声を出力することができる。   Note that, instead of or in addition to the above-described output as an image, output by audio data is also possible. Specifically, the voice can be output separately for the whistle sound and the analogy sound. Alternatively, it is possible to emphasize only one of the whistle sound and the like sound and output the sound.

<判定結果の具体例>
最後に、図8及び図9において解析結果の具体例を挙げて、本実施例の利点について詳細に説明する。ここに図8は、本実施例及び比較例に係る呼吸音解析装置により笛声音を解析する例を示す概念図である。また図9は、本実施例及び比較例に係る呼吸音解析装置により類鼾音を解析する例を示す概念図である。なお、以下では、変動する閾値(図4及び図5を参照)を用いる本実施例に係る呼吸音解析装置、と、単一閾値(250Hz)を用いる比較例に係る呼吸音解析装置とで、笛声音及び類鼾音の各々スペクトルを解析する場合を考える。なお、笛声音及び類鼾音の判定は、所定の時間間隔ごと(時刻t1〜t7)で行われるものとする。
<Specific example of judgment result>
Finally, the advantages of the present embodiment will be described in detail with reference to specific examples of analysis results in FIGS. FIG. 8 is a conceptual diagram showing an example in which the whistle voice sound is analyzed by the breathing sound analyzer according to the present embodiment and the comparative example. FIG. 9 is a conceptual diagram showing an example in which analogy sounds are analyzed by the respiratory sound analysis apparatus according to the present embodiment and the comparative example. In the following, the respiratory sound analysis apparatus according to the present embodiment using a varying threshold (see FIGS. 4 and 5) and the respiratory sound analysis apparatus according to a comparative example using a single threshold (250 Hz), Consider the case of analyzing the spectrum of whistle sounds and similar sounds. In addition, the judgment of a whistle voice sound and an analogy sound shall be performed for every predetermined time interval (time t1-t7).

図8に示すように、ピーク周波数が約350Hzから約200Hzまで低下するような笛声音が解析対象であるとする。この場合、比較例に係る呼吸音解析装置では、時刻t1からt6においては、解析対象である連続性ラ音が笛声音であると正確に判定されている。しかしながら、時刻t7においては、笛声音のスペクトルのピーク周波数が低下している(即ち、閾値である250Hzを下回っている)ため、類鼾音と誤って判定されている。   As shown in FIG. 8, it is assumed that a whistle voice whose peak frequency decreases from about 350 Hz to about 200 Hz is an analysis target. In this case, in the breathing sound analysis apparatus according to the comparative example, it is accurately determined that the continuous rar sound to be analyzed is a whistle voice from time t1 to t6. However, at time t7, the peak frequency of the whistle voice spectrum is lowered (that is, lower than the threshold value of 250 Hz), so that it is erroneously determined as a similar sound.

一方、本実施例に係る呼吸音解析装置では、時刻t1からt4においては、解析対象である連続性ラ音が100%笛声音であると正確に判定されている。そして、時刻t5からは類鼾音の割合が少しずつ増加するが、時刻t7においても笛声音が含まれるものと判定され続ける。即ち、時刻t7において完全に誤った判定をしてしまった比較例に対して、本実施例は笛声音も含まれる可能性があるという結果を提示することができる。   On the other hand, in the respiratory sound analysis apparatus according to the present embodiment, it is accurately determined that the continuous rar sound to be analyzed is a 100% whistle voice from time t1 to t4. Then, although the proportion of the analog sound gradually increases from time t5, it is determined that the whistle sound is included at time t7. That is, the present embodiment can present a result that there is a possibility that a whistle voice is also included in the comparative example in which the determination is completely wrong at time t7.

図9に示すように、ピーク周波数が約240Hzから約260Hzまで上昇し、その後約180Hzまで低下するような類鼾音が解析対象であるとする。この場合、比較例に係る呼吸音解析装置では、時刻t1、及びt4からt7において、解析対象である連続性ラ音が類鼾音であると正確に判定されている。しかしながら、時刻t2及びt3においては、類鼾音のスペクトルのピーク周波数が一時的に上昇している(即ち、閾値である250Hzを上回っている)ため、笛声音と誤って判定されている。   As shown in FIG. 9, it is assumed that an analogy sound whose peak frequency increases from about 240 Hz to about 260 Hz and then decreases to about 180 Hz is an analysis target. In this case, in the breathing sound analysis apparatus according to the comparative example, it is accurately determined that the continuous rale to be analyzed is an analogy sound from time t1 and t4 to t7. However, at times t2 and t3, the peak frequency of the spectrum of the analogy sound temporarily rises (that is, exceeds the threshold value of 250 Hz), so that it is erroneously determined as a whistle sound.

一方、本実施例に係る呼吸音解析装置では、ピーク周波数が上昇する時刻t2及びt3においても類鼾音が含まれるものと判定され続ける。即ち、時刻t2及びt3において完全に誤った判定をしてしまった比較例に対して、本実施例は類鼾音も含まれる可能性があるという結果を提示することができる。   On the other hand, in the respiratory sound analysis apparatus according to the present embodiment, it is continuously determined that analogy sounds are included at times t2 and t3 when the peak frequency increases. That is, the present embodiment can present a result that there is a possibility that similar sounds may also be included in the comparative example in which the determination is completely wrong at the times t2 and t3.

以上説明したように、本実施例に係る呼吸音解析装置によれば、ピーク周波数に応じて変動する閾値を利用することで、より好適に笛声音及び類鼾音を判定することができる。   As described above, according to the respiratory sound analysis apparatus according to the present embodiment, it is possible to more appropriately determine the whistle voice sound and the analogy sound by using the threshold value that varies according to the peak frequency.

本発明は、上述した実施形態に限られるものではなく、特許請求の範囲及び明細書全体から読み取れる発明の要旨或いは思想に反しない範囲で適宜変更可能であり、そのような変更を伴う呼吸音解析装置及び呼吸音解析方法、並びにコンピュータプログラム及び記録媒体もまた本発明の技術的範囲に含まれるものである。   The present invention is not limited to the above-described embodiment, and can be appropriately changed without departing from the spirit or idea of the invention that can be read from the claims and the entire specification, and respiratory sound analysis accompanying such changes The apparatus, the respiratory sound analysis method, the computer program, and the recording medium are also included in the technical scope of the present invention.

110 生体音センサ
120 信号記憶部
125 信号処理部
130 音声出力部
140 表示部
200 処理部
210 周波数解析部
220 連続性ラ音検出部
230 ピーク周波数検出部
240 笛声音/類鼾音判定部
250 画像生成部
DESCRIPTION OF SYMBOLS 110 Body sound sensor 120 Signal memory | storage part 125 Signal processing part 130 Audio | voice output part 140 Display part 200 Processing part 210 Frequency analysis part 220 Continuation ra sound detection part 230 Peak frequency detection part 240 Whistle / sound sound determination part 250 Image generation Part

Claims (7)

呼吸音から連続性ラ音を含む呼吸音成分を取得する成分取得手段と、
前記呼吸音成分に含まれる所定の特徴に対応する周波数を取得する周波数取得手段と、
前記所定の特徴に対応する周波数と、前記所定の特徴に対応する周波数に応じて変動する閾値との関係に基づいて、前記呼吸音成分に含まれる笛声音成分と類鼾音成分とを判定する判定手段と
を備えることを特徴とする呼吸音解析装置。
Component acquisition means for acquiring a respiratory sound component including a continuous ra sound from the respiratory sound;
Frequency acquisition means for acquiring a frequency corresponding to a predetermined characteristic included in the respiratory sound component;
Based on the relationship between the frequency corresponding to the predetermined feature and the threshold value that varies according to the frequency corresponding to the predetermined feature, the whistle voice component and the analogy sound component included in the respiratory sound component are determined. And a breathing sound analyzing apparatus.
前記所定の特徴は、極大値であることを特徴とする請求項1に記載の呼吸音解析装置。   The respiratory sound analysis apparatus according to claim 1, wherein the predetermined feature is a local maximum value. 前記判定手段は、前記呼吸音成分に含まれる前記笛声音成分と前記類鼾音成分との割合を判定することを特徴とする請求項1又は2に記載の呼吸音解析装置。   The respiratory sound analysis apparatus according to claim 1, wherein the determination unit determines a ratio between the whistle vocal sound component and the analogy sound component included in the respiratory sound component. 前記閾値は、前記所定の特徴に対応する周波数が高いほど、前記笛声音成分が含まれる割合が増加し、前記類鼾音成分が含まれる割合が減少するように判定させるものとして変動することを特徴とする請求項3に記載の呼吸音解析装置。   The threshold fluctuates so that the higher the frequency corresponding to the predetermined feature, the higher the ratio of the whistle sound component included, and the lower the ratio of the analogy sound component. The respiratory sound analysis apparatus according to claim 3, wherein the respiratory sound analysis apparatus is characterized. 呼吸音から連続性ラ音を含む呼吸音成分を取得する成分取得工程と、
前記呼吸音成分に含まれる所定の特徴に対応する周波数を取得する周波数取得工程と、
前記所定の特徴に対応する周波数と、前記所定の特徴に対応する周波数に応じて変動する閾値との関係に基づいて、前記呼吸音成分に含まれる笛声音成分と類鼾音成分とを判定する判定工程と
を備えることを特徴とする呼吸音解析方法。
A component acquisition step of acquiring a respiratory sound component including a continuous ra sound from the respiratory sound;
A frequency acquisition step of acquiring a frequency corresponding to a predetermined feature included in the respiratory sound component;
Based on the relationship between the frequency corresponding to the predetermined feature and the threshold value that varies according to the frequency corresponding to the predetermined feature, the whistle voice component and the analogy sound component included in the respiratory sound component are determined. A breathing sound analysis method comprising: a determination step.
呼吸音から連続性ラ音を含む呼吸音成分を取得する成分取得工程と、
前記呼吸音成分に含まれる所定の特徴に対応する周波数を取得する周波数取得工程と、
前記所定の特徴に対応する周波数と、前記所定の特徴に対応する周波数に応じて変動する閾値との関係に基づいて、前記呼吸音成分に含まれる笛声音成分と類鼾音成分とを判定する判定工程と
をコンピュータに実行させることを特徴とするコンピュータプログラム。
A component acquisition step of acquiring a respiratory sound component including a continuous ra sound from the respiratory sound;
A frequency acquisition step of acquiring a frequency corresponding to a predetermined feature included in the respiratory sound component;
Based on the relationship between the frequency corresponding to the predetermined feature and the threshold value that varies according to the frequency corresponding to the predetermined feature, the whistle voice component and the analogy sound component included in the respiratory sound component are determined. A computer program for causing a computer to execute the determination step.
請求項6に記載のコンピュータプログラムが記録されていることを特徴とする記録媒体。   A recording medium in which the computer program according to claim 6 is recorded.
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