WO2023048158A1 - Sleep apnea syndrome determination device, sleep apnea syndrome determination method, and program - Google Patents

Sleep apnea syndrome determination device, sleep apnea syndrome determination method, and program Download PDF

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Publication number
WO2023048158A1
WO2023048158A1 PCT/JP2022/035080 JP2022035080W WO2023048158A1 WO 2023048158 A1 WO2023048158 A1 WO 2023048158A1 JP 2022035080 W JP2022035080 W JP 2022035080W WO 2023048158 A1 WO2023048158 A1 WO 2023048158A1
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sleep
average
apnea syndrome
frequency spectrum
sleep apnea
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PCT/JP2022/035080
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French (fr)
Japanese (ja)
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圭樹 ▲高▼玉
怡恒 中理
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国立大学法人電気通信大学
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Publication of WO2023048158A1 publication Critical patent/WO2023048158A1/en

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    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/08Detecting, measuring or recording devices for evaluating the respiratory organs
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/103Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/103Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/113Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb occurring during breathing
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/16Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state

Definitions

  • the present invention relates to a sleep apnea syndrome determination device, a sleep apnea syndrome determination method, and a program for determining sleep apnea syndrome of a subject.
  • the sleep state of the person being measured is measured in order to diagnose sleep disorders and sleep apnea syndrome.
  • the human sleep stage is classified into six stages from the viewpoint of the depth of sleep, and the six sleep stages are wakefulness, REM sleep, non-REM sleep (stage 1 to 4).
  • these six sleep stages are determined by attaching a large number of electrodes to the subject's face and head, for example, and measuring electroencephalograms, eye movements, and jaw electromyograms from the large number of electrodes. Analysis of the results was done.
  • sleep stages should be measured.
  • various measurements such as air flow associated with breathing such as mouth and nose air flow and ventilation movements of the chest and abdomen are required. measurements must be made simultaneously. Then, based on the analysis result of the sleep stage and the measurement result of the respiratory state, the doctor diagnoses whether or not the patient has an apnea syndrome.
  • Patent Document 1 describes a method called Database-based Compact Genetic Algorithm, which is an improved learning method using a genetic algorithm, and a technology for estimating sleep stages from detection data of a mattress type pressure sensor.
  • the technique described in this Patent Document 1 estimates a sleep stage based on the subject's body motion and heart rate detected by a mattress-type pressure sensor. According to the technique described in Patent Literature 1, by estimating the sleep stage using the mattress-type pressure sensor, it is possible to estimate the sleep state of the subject without imposing a burden on the subject.
  • detecting sleep stages using the detection data of the mattress-type pressure sensor is an index for determining apnea syndrome.
  • the sleep apnea syndrome determination device of the present invention includes a biological data acquisition unit that acquires biological vibration data due to heartbeat, respiration, and body movement during sleep of the subject, and the biological vibration data acquired by the biological data acquisition unit.
  • a biological data acquisition unit that acquires biological vibration data due to heartbeat, respiration, and body movement during sleep of the subject, and the biological vibration data acquired by the biological data acquisition unit.
  • the sleep apnea syndrome determination method of the present invention includes a biological data acquisition process for acquiring biological vibration data due to heartbeat, respiration, and body movement during sleep of the subject, and biological vibration acquired by the biological data acquisition process.
  • a biological data acquisition process for acquiring biological vibration data due to heartbeat, respiration, and body movement during sleep of the subject, and biological vibration acquired by the biological data acquisition process.
  • Frequency analysis of the data the average of the frequency spectrum of the location determined to be arousal during sleep, the average of the frequency spectrum of the location determined to be other than awake, and the location determined to be awake and other locations
  • Biological data processing for obtaining one of the averages of both frequency spectra
  • Approximate curve acquisition processing for obtaining an approximated curve for the logarithm of the average of the frequency spectra obtained by the biological data processing, and obtained by the biological data processing Detects the amount by which the average logarithm value of the frequency spectrum deviates from the approximated curve acquired in the approximated curve acquisition process in the positive
  • the program of the present invention is a program that causes a computer to execute each process performed by the sleep apnea syndrome determination method as a procedure.
  • FIG. 4 is a diagram showing an example of sleep apnea syndrome determination state according to an embodiment of the present invention
  • 1 is a block diagram showing a hardware configuration example of a device for determining sleep apnea syndrome according to an embodiment of the present invention
  • FIG. 5 is a diagram showing an example of processing for obtaining a power spectrum during sleep at regular intervals according to an embodiment of the present invention
  • FIG. 5A shows an example of changes in sleep stages during sleep of a healthy subject.
  • FIG. 5B shows an example of sleep stage changes during sleep of a patient with sleep apnea syndrome.
  • 4 is a flow chart showing the flow of processing for determining sleep apnea syndrome according to one embodiment of the present invention.
  • FIG. 7A shows an example of contribution to vibration frequency during sleep of a healthy subject.
  • FIG. 7B shows an example of the average vibration frequency in the wakefulness (W) section during sleep of a healthy subject.
  • FIG. 7C shows an example of the average frequency of vibrations in the non-awakening period during sleep of a healthy subject.
  • FIG. 7D shows an example of the average logarithmic value of the vibration frequency in the wakefulness (W) period during sleep of a healthy subject.
  • FIG. 7A shows an example of contribution to vibration frequency during sleep of a healthy subject.
  • FIG. 7B shows an example of the average vibration frequency in the wakefulness (W) section during sleep of a healthy subject.
  • FIG. 7C shows an example of the average frequency of vibrations in the non-awa
  • FIG. 7E is a diagram showing an example of the average logarithmic value of the vibration frequency in the non-awakening period during sleep of a healthy subject.
  • FIG. 8A shows an example of the contribution to the frequency of oscillations during sleep of a patient with sleep apnea.
  • FIG. 8B shows an example of the average frequency of oscillations in the arousal (W) interval during sleep of a patient with sleep apnea syndrome.
  • FIG. 8C shows an example of the average frequency of vibrations in the non-awakening period during sleep of a patient with sleep apnea syndrome.
  • FIG. 8D shows an example of the average logarithmic value of the oscillation frequency in the wakefulness (W) interval during sleep of a patient with sleep apnea syndrome.
  • FIG. 8E is a diagram showing an example of the average logarithmic value of the frequency of the vibration in the interval other than the awakening period during sleep of a patient with sleep apnea syndrome.
  • FIG. 10 is a diagram showing a representative example in which the average logarithmically calculated value of the frequency of vibrations in a section other than awakening of a patient with sleep apnea syndrome is superimposed on an approximate curve.
  • FIG. 10A is a diagram showing a first example in which the average logarithmically calculated value of the frequency of vibration in a section other than awakening of a healthy person is superimposed on an approximate curve.
  • FIG. 10B is a diagram showing an example of the second person in which the average logarithmically calculated value of the vibration frequency in the non-awakening section of the healthy person is superimposed on the approximate curve.
  • FIG. 10C is a diagram showing an example of a third person in which the average logarithmically calculated value of the vibration frequency in the non-awakening section of the healthy person is superimposed on the approximation curve.
  • FIG. 10D is a diagram showing an example of a fourth person in which the average logarithmically calculated value of the vibration frequency in the section other than the awakening of the healthy person is superimposed on the approximate curve.
  • FIG. 10B is a diagram showing an example of the second person in which the average logarithmically calculated value of the vibration frequency in the non-awakening section of the healthy person is superimposed on the approximate curve.
  • FIG. 10C is a diagram showing an example of a third person in which the average logarithmically calculated value of the vibration frequency in the non-awakening section of the healthy
  • FIG. 10E is a diagram showing an example of the fifth person in which the average logarithmically calculated value of the vibration frequency in the section other than the awakening of the healthy person is superimposed on the approximation curve.
  • FIG. 11A is a diagram showing a first example in which an average logarithmically calculated value of the frequency of vibrations in a section other than awakening of a patient with sleep apnea syndrome is superimposed on an approximate curve.
  • FIG. 11B is a diagram showing a second example in which the average logarithmically calculated value of the vibration frequency in the interval other than the awakening period of the patient with sleep apnea syndrome is superimposed on the approximation curve.
  • FIG. 11A is a diagram showing a first example in which an average logarithmically calculated value of the frequency of vibrations in a section other than awakening of a patient with sleep apnea syndrome is superimposed on an approximate curve.
  • FIG. 11B is a diagram showing a second example in which the average loga
  • FIG. 11C is a diagram showing a third example in which the average logarithmically calculated value of the vibration frequency in the interval other than the awakening period of the patient with sleep apnea syndrome is superimposed on the approximation curve.
  • FIG. 11D is a diagram showing a fourth example in which the average logarithmically calculated value of the vibration frequency in the interval other than the awakening period of the patient with sleep apnea syndrome is superimposed on the approximate curve.
  • FIG. 11E is a diagram showing a fifth example in which the average logarithmically calculated value of the vibration frequency in the interval other than the awakening period of the patient with sleep apnea syndrome is superimposed on the approximate curve. It is a figure which shows the example of the determination result by one embodiment of this invention.
  • FIG. 1 is a block diagram showing the configuration of a sleep apnea syndrome determination device 10 of this example.
  • FIG. 2 is a diagram showing an example of a state in which sleep apnea syndrome determination is performed using the sleep apnea syndrome determination device 10 of this example.
  • the sleep apnea syndrome determination device 10 of the present embodiment acquires the body vibration of the person to be measured as pressure data with the mattress sensor 2 .
  • the bio-vibration includes vibration components due to heartbeat and respiration in addition to vibration components due to the body motion of the person being measured.
  • the mattress sensor 2 detects the bio-vibration of the upper body of the subject A during sleep as a change in pressure.
  • the mattress sensor 2 is used by laying it on or under the mattress of the bed 1 on which the subject A sleeps, as shown in FIG. 2, for example. It should be noted that the arrangement of the mattress sensor 2 on the mattress under the subject A is an example, and the mattress sensor 2 may be incorporated in the mattress, for example.
  • FIG. 2 shows an example in which the sleep apnea syndrome determination device 10 is installed beside the bed 1 and the mattress sensor 2 and the sleep apnea syndrome determination device 10 are connected with a cable.
  • the pressure data (biological vibration data) may be wirelessly transmitted to the sleep apnea syndrome determination device 10 in another room.
  • biological vibration data the pressure data output by the mattress sensor 2 will be referred to as biological vibration data.
  • obtaining biological vibration data from a pressure sensor is an example, and other sensors may be used. For example, an infrared sensor, a laser, or the like may be used to measure the vibration of the subject during sleep without contact.
  • the sleep apnea syndrome determination device 10 includes a biological data acquisition unit 11, a biological data processing unit 12, a sleep stage determination unit 13, a sleep apnea syndrome determination unit (hereinafter, "SAS determination unit"). ) 14 and an output unit 15 .
  • the biometric data acquisition unit 11 performs biodata acquisition processing for acquiring biovibration data output from the mattress sensor 2 .
  • the biological vibration data acquired by the biological data acquisition section 11 is supplied to the biological data processing section 12 .
  • the biological data processing unit 12 samples the supplied biological vibration data, converts it into digital data, and calculates the frequency power spectrum of the digitalized biological vibration data.
  • the process of calculating the power spectrum of the frequency of this biovibration data is performed in a cycle of 30 seconds. However, in this example, one calculation is actually performed for 32 seconds, and the calculation for the 32 seconds is performed in a cycle of 30 seconds, that is, overlapped by 2 seconds.
  • the calculation of the power spectrum by the biological data processing unit 12 at a cycle of 30 seconds is an example, and the power spectrum may be calculated at a cycle shorter than 30 seconds or longer than 30 seconds.
  • the biological data processing unit 12 may calculate the power spectrum in 60-second cycles. The overlapping of two seconds in one calculation is also an example, and there may be no overlapping period. Then, the biological data processing unit 12 supplies the calculation result of the power spectrum for each fixed period during sleep to the sleep stage determination unit 13 and the SAS determination unit 14 .
  • the sleep stage determination unit 13 determines the subject's sleep stage for each period based on the calculation result of the power spectrum for each certain period.
  • this sleep stage not only the power spectrum but also various feature amounts obtained by calculating the biological vibration data may be used for determination.
  • a random forest which is one of machine learning, may be used to determine a sleep stage from feature amount data.
  • Non-REM sleep has four stages from stage 1 to stage 4 (NR1-NR4). divided into Therefore, sleep stages are divided into six stages in total.
  • stage 4 non-REM sleep is the deepest sleep stage.
  • the sleep stage determination unit 13 of this example does not need to determine all of these six sleep stages, and may at least determine whether or not it is wakefulness (WAKE).
  • the sleep stage determination unit 13 of this example may determine sleep stages from other biological data such as electroencephalograms.
  • the SAS determination unit 14 determines whether or not the subject has sleep apnea syndrome (SAS) based on the calculation result of the power spectrum for each fixed period and the sleep stage determination result of the sleep stage determination unit 13 . It should be noted that the use of the sleep stage when the SAS determination unit 14 determines whether or not it is SAS is merely an example, and it is not necessary to use the determination result of the sleep stage for determination of SAS. The details of the processing procedure for the SAS determination unit 14 to determine the SAS based on the calculation results of the power spectrum for each fixed period will be described later.
  • SAS sleep apnea syndrome
  • the output unit 15 outputs the result of sleep apnea syndrome determined by the SAS determination unit 14 .
  • the output unit 15 is configured by, for example, a display device, and displays the determination result of sleep apnea syndrome.
  • the output unit 15 may be configured as a recording device to record the determination result of the sleep apnea syndrome together with the sleeping state of the night. Further, when displaying or recording, the output unit 15 may display or record not only the sleep apnea syndrome determination result but also the sleep stage determination result at the same time.
  • the output unit 15 may be another external terminal to transmit the determination result via the network.
  • the output unit 15 may be a pre-registered smartphone, and the determination result may be transmitted.
  • the sleep apnea syndrome determination apparatus 10 of this example may perform data acquisition and determination during sleep in real time.
  • the biological data acquisition unit 11 only acquires biological vibration data during sleep, records the acquired data, and uses the recorded data to perform processing up to determination at a later date. You can do it.
  • FIG. 3 shows a hardware configuration example when the sleep apnea syndrome determination device 10 is configured by a computer device.
  • the computer device C includes a CPU (Central Processing Unit) C1, a ROM (Read Only Memory) C2, and a RAM (Random Access Memory) C3 connected to a bus C8. Further, computer device C comprises non-volatile storage C4, network interface C5, input device C6, and display device C7.
  • CPU Central Processing Unit
  • ROM Read Only Memory
  • RAM Random Access Memory
  • the CPU C1 reads from the ROM C2 the program code of the software that realizes each function of the biological data processing unit 12, the sleep stage determination unit 13, and the SAS determination unit 14 of the sleep apnea syndrome determination device 10 and executes it.
  • the programs for executing these processes are also read out from ROM C2 and executed by CPU C1.
  • Variables, parameters, etc. generated during arithmetic processing are temporarily written to RAM C3.
  • non-volatile storage C4 for example, HDD (Hard disk drive), SSD (Solid State Drive), flexible disk, optical disk, magneto-optical disk, CD-ROM, non-volatile memory, etc. are used.
  • OS Operating System
  • the nonvolatile storage C4 stores a program for causing the computer device C to function as the sleep apnea syndrome determination device 10, and as a recording medium for recording the program there is
  • data about the sleep stages determined by the sleep stage determination unit 13 and the SAS determination results determined by the SAS determination unit 14 are also recorded in the nonvolatile storage C4.
  • the network interface C5 for example, a NIC (Network Interface Card) or the like is used, and various data can be transmitted and received via a LAN (Local Area Network) to which terminals are connected, a dedicated line, or the like.
  • the computer device C acquires pressure data output by the mattress sensor 2 via the network interface C5.
  • the input device C6 is configured by, for example, a device such as a keyboard, and the input device C6 is used to set the period for determining sleep apnea syndrome by the sleep apnea syndrome determination device 10, to instruct the display format of the determination result, etc. is done.
  • the result of sleep apnea syndrome determination by the sleep apnea syndrome determination device 10 is displayed on the display device C7.
  • the sleep apnea syndrome determination device 10 is an example of a computer device that functions as a determination device by executing a program (software) recorded on a recording medium, and the sleep apnea syndrome determination device 10 Dedicated hardware that executes some or all of the processing may be prepared.
  • the biological data processing unit 12 calculates the power spectrum of the frequencies of biological vibrations during sleep for a certain period of time.
  • the biological data processing unit 12 processes data for 32 seconds at intervals of 30 seconds. That is, as shown in FIG. 4, the biometric data acquisition unit 11 first acquires the sensor values of the mattress sensor 2 from falling asleep (0 seconds) to 32 seconds, and thereafter acquires sensor values for 32 seconds at intervals of 30 seconds. I will get.
  • the biological data acquisition unit 11 continuously acquires the biological vibration data from going to bed to waking up, and supplies the acquired biological vibration data to the biological data processing unit 12 .
  • the biometric data processing unit 12 calculates the frequency power spectrum for 32 seconds as shown in the lower right of FIG.
  • Frequency analysis for calculating this power spectrum is performed by, for example, fast Fourier transform (FFT).
  • FFT fast Fourier transform
  • the frequencies exhibiting a high density are the bio-vibration component due to heartbeat and the bio-vibration component due to respiration.
  • the heartbeat frequency is around 1 Hz
  • the component of bio-vibration due to respiration is around 2 Hz.
  • there is an apnea section and therefore there is a period during which no biological vibration due to respiration occurs.
  • a bio-vibration component is generated due to body movements that are much larger than heartbeats and respirations.
  • the sleep stage determination unit 13 determines the main sleep stage in 30 seconds from the power spectrum of biological vibrations as shown in FIG.
  • the method already proposed by the inventors of the present application can be applied as a method for determining sleep stages from the power spectrum of biological vibrations. Although description is omitted here, according to this method, for example, the sleep stage can be determined from the occurrence of different feature amounts for each sleep stage.
  • the first 30 seconds after falling asleep are determined to be wakefulness (W), and the next 30 seconds are determined to be stage 2 non-REM (N2).
  • Figure 5 compares an example of measuring the sleep stages of a healthy subject (Figure 5A) and an example of measuring the sleep stages of a patient with sleep apnea syndrome (SAS patient) during a certain sleep period ( Figure 5B). It is a diagram.
  • the horizontal axis indicates sleep time, and the vertical axis indicates sleep stages.
  • the sleep stage on the vertical axis shows the lightest wakefulness (WAKE) at the top, and REM sleep (REM), stage 1 non-REM sleep (NREM1), stage 2 non-REM sleep (NREM1) toward the bottom. NREM 2), stage 3 non-REM sleep (NREM 3), and stage 4 non-REM sleep (NREM 4), indicating a deep sleep stage.
  • WAKE lightest wakefulness
  • REM sleep REM
  • stage 1 non-REM sleep NREM1
  • stage 2 non-REM sleep NREM1
  • NREM1 stage 2 non-REM sleep
  • NREM 4 stage 4 non-REM sleep
  • the SAS patient frequently wakes up due to apnea (WAKE).
  • WAKE apnea
  • the tongue blocks the airway, causing apnea, and its influence appears in changes in pulsation (heart rate).
  • SAS is determined according to the processing procedure described below, mainly from the features that appear in the frequency spectrum in intervals other than wake (WAKE). However, it is an example to determine the SAS from the frequency spectrum of the interval other than wakefulness (WAKE). It is also possible to determine SAS using the frequency spectrum of all sections.
  • FIG. 6 is a flow chart showing the flow of processing for determining SAS by the sleep apnea syndrome determination device 10 of this example.
  • the biometric data acquisition unit 11 acquires biovibration data during sleep (step S11).
  • the biological vibration data may be real-time data acquired during sleep, or may be recorded biological vibration data.
  • the biometric data processing unit 12 calculates feature amounts of biovibration data at regular intervals (step S12). Then, the sleep stage determination unit 13 sets the sleep stage at regular intervals (30 seconds) based on the feature amount calculated by the biological data processing unit 12, and converts the set sleep stage into the biological vibration data of the corresponding section. Label (step S13).
  • the SAS determination unit 14 acquires, from the biological data processing unit 12, the frequency spectrum, which is the frequency analysis result of the biological vibration data in the interval determined to be other than wakefulness (WAKE) by the sleep stage determination unit 13 (step S14 ).
  • WAKE biological vibration data in a section other than wakefulness
  • the SAS determination unit 14 calculates the average of all frequency spectra in the interval other than the acquired wakefulness (WAKE), and further performs logarithmic calculation (for example, calculation of log2) on the average of the frequency spectrum to obtain a logarithmic value (log calculated value) is obtained (step S15).
  • the SAS determination unit 14 calculates an approximated curve of changes in the average logarithmic value of the frequency spectrum, and performs a process of acquiring the approximated curve (approximate curve acquisition process) (step S16).
  • the SAS determination unit 14 employs, for example, the method of least squares as a method of calculating the approximate curve. When calculating this approximated curve, it may be calculated by excluding some values such as the lowest frequency.
  • the SAS determination unit 14 compares the calculated approximated curve with the average logarithm value of the frequency spectrum, and determines that the average logarithm value exceeds the approximated curve. Then, the size of the portion where the image is drawn is calculated (step S17). In the case of this example, as a method of calculating the size of the portion exceeding the approximate curve, the area of the portion where the average logarithm value is larger than the approximate curve on the graph described in FIG. 7 is calculated. A calculation method is adopted.
  • the SAS determination unit 14 compares the size (area) calculated in step S17 with a threshold value for determination prepared in advance to determine whether or not it is SAS (step S18). That is, when the area calculated in step S17 is equal to or larger than the threshold value, it is determined to be SAS, and when the area calculated in step S17 is less than the threshold value, it is determined not to be SAS. A determination result is output from the output unit 15 .
  • FIG. 7 shows a frequency spectrum acquired in one sleep of a healthy subject and data obtained by processing the frequency spectrum.
  • FIG. 8 shows a frequency spectrum acquired in one sleep of an SAS patient and data obtained by processing the frequency spectrum.
  • FIGS. 7A and 8A show the contribution to the overall frequency of the frequency spectrum acquired in one sleep.
  • Figures 7B and 8B show the average of all frequency spectra for the wake interval (WAKE).
  • FIGS. 7C and 8C show averages of all frequency spectra in the non-wake interval (NON-WAKE).
  • FIGS. 7D and 8D show logarithmically calculated values from the average of all frequency spectra of the arousal interval of FIGS. 7B and 8B, and FIGS.
  • FIGS. 7B to 7E and 8B to 8E show the frequency on the horizontal axis and the density on the vertical axis, respectively.
  • FIG. 7A and FIG. 8A there are some differences in the distribution of the frequency spectrum of the healthy subject and the contribution of the frequency spectrum of the SAS patient, but the average of each figure is shown in FIG. In 8B and in FIGS. 7C and 8C, no apparent clear difference appears between the healthy subject and the SAS patient.
  • FIGS. 7D and 8D, and in FIGS. 7E and 8E which show the values obtained by calculating the logarithmic value from the average of the frequency spectrum, there is a relatively large difference at frequencies around 3 Hz between healthy subjects and SAS patients. is appearing. That is, both the average logarithmic value of the arousal interval (WAKE) shown in FIGS.
  • WAKE average logarithmic value of the arousal interval
  • the SAS determination unit 14 of this example determines that the patient is an SAS patient from the difference in the average logarithmic value between the healthy subject and the SAS patient.
  • the fact that the SAS patient can be determined from the frequency around 3 Hz will be described in more detail.
  • a vibration phenomenon called microvibration is observed on the surface of the body, and the component of 3 to 4 Hz becomes stronger at a low wakefulness level when one becomes sleepy. From this result, SAS patients have frequent wake-sleep cycles and fall into a low wakefulness level when transitioning from wakefulness to sleep. It is assumed that the component will come out strongly.
  • the frequency component of 3 Hz appears less as a whole when the logarithmic spectrum for one night is averaged. Therefore, it is possible to appropriately determine that the patient is an SAS patient from the frequency around 3 Hz.
  • FIG. 9 shows an example of an approximation curve C1 obtained by the least-squares method for the average logarithmic value NW1 of the non-awakening interval (NON-WAKE) of the SAS patient.
  • the SAS determination unit 14 excludes several (here, two) values from the lowest frequency in the average logarithmic value NW1 (the values in the range x in FIG. 9). Then, calculation is performed by the method of least squares.
  • the approximation curve obtained in this manner has the highest value at a low frequency and gradually decreases to a lower value as the frequency increases, drawing a gentle curve.
  • the SAS determination unit 14 compares the approximated curve C1 with the average logarithmic value NW1.
  • the area value Supper of the region in which the logarithmic value NW1 is larger than the approximate curve C1 on the graph becomes a relatively large value.
  • the approximated curve C1 basically reflects (approximates) the state of the average logarithmic value NW1, the value Sunder of the area of the region where the logarithmic value NW1 is smaller than that of the approximated curve C1 and the approximation The difference from the area value Supper of the region where the logarithmic value NW1 is larger than the curve C1 is small. Therefore, the area value Sunder of the region where the logarithmic value NW1 is smaller than the approximate curve C1 also becomes a large value corresponding to the area value Super of the region where the logarithmic value NW1 is larger than the approximate curve C1. .
  • the approximated curve C1 and the logarithmic value NW1 shown in FIG. is a unimodal shape, it can be determined as an SAS patient.
  • the logarithmic value NW is larger than the approximation curve C1 at a frequency around 3 Hz, if it does not have a single peak, it is determined that the patient is not an SAS patient.
  • the size of the unimodal portion where the logarithmic value NW is larger than the approximate curve C1 that is, based on the size of the amount deviating in the positive direction, whether the subject is an SAS patient may be determined.
  • the patient When the magnitude of the deviation of the unimodal portion in the positive direction is greater than or equal to a set threshold value, the patient is determined to be an SAS patient, and when it is smaller than the set threshold value, the patient is determined not to be an SAS patient. As a result, it is possible to improve the accuracy of determination as an SAS patient.
  • the portion where the logarithmic value NW is larger than the approximated curve C1 at a frequency around 3 Hz has a unimodal shape, it is determined to be an SAS patient, but it is detected from the frequency around 3 Hz. is an example, and even if the frequency deviates from 3 Hz, if the location where the logarithmic value NW is larger has a unimodal shape, it may be determined as an SAS patient. However, detection accuracy is higher when detection is performed at a frequency near 3 Hz.
  • FIG. 9 shows an example of a single SAS patient
  • FIGS. 10 and 11 show examples of multiple (five) healthy subjects and SAS patients.
  • FIGS. 10A, 10B, 10C, 10D, and 10E show the average logarithmic values NW11 to NW15 of the non-awakening interval (NON-WAKE) of five healthy subjects and their approximate curves C11 to C15.
  • FIGS. 11A, 11B, 11C, 11D, and 11E show average logarithmic values NW21 to NW25 of non-awakening intervals (NON-WAKE) of five SAS patients and their approximate curves C11 to C15.
  • FIG. 12 shows the logarithmic value obtained from the vibration data during one sleep of 18 subjects a to r and the area value of the area where the logarithmic value obtained from the approximate curve is larger than the approximate curve. Supper and the value Sunder of the area of the region whose logarithmic value is smaller than the approximate curve.
  • the vertical axis in FIG. 12 indicates the area value. In the bar graphs of each measurer a to r, the left side is the value Supper and the right side is the value Sunder.
  • the SAS determination unit 14 sets a threshold value TH1 for determination between the measured values of the healthy subjects j to r and the measured values of the SAS patients a to i, and the value Supper or the value Sunder is compared with the threshold value TH1. As a result, the SAS determination unit 14 determines that the patient is an SAS patient when the threshold TH is equal to or greater than TH1, and determines that the patient is not an SAS patient when the threshold TH is less than TH1, thereby accurately determining whether the patient suffers from sleep apnea syndrome. can be diagnosed.
  • the sleep apnea syndrome determination device 10 of the present example from the biological vibration data based on the body movement or pressure change during sleep of the subject, the patient with sleep apnea syndrome with high accuracy can be determined.
  • the data determined by the sleep apnea syndrome determination device 10 of this example is biological vibration data that can be measured by the mattress sensor 2 or the like. It is possible to determine sleep apnea syndrome with high accuracy. Therefore, the apparatus 10 for determining sleep apnea syndrome according to the present embodiment can produce an effect that the burden on the person to be measured is extremely small compared to the conventional method of making determination by attaching electrodes or the like to the body of the person to be measured. can.
  • the sleep apnea syndrome determination device of this example When the sleep apnea syndrome determination device of this example is used for determination, healthy subjects who are not SAS patients may be erroneously determined to be SAS patients, albeit in a small percentage. However, when the health conditions of the erroneously determined healthy subjects are examined in detail, all of the healthy subjects have characteristics similar to those of SAS patients, such as being heavier than the average. Therefore, it indicates the possibility of screening those who have a tendency similar to SAS patients, and it is also possible to detect signs of SAS at an early stage.
  • the processing described in the above-described embodiment is a preferred example, and the processing is not limited to that described in the embodiment.
  • the SAS determination unit 14 compares the average logarithmic value with the approximated curve, calculates the area where the average logarithmic value is larger or smaller than the approximated curve, and compares the area with the threshold value. I made it On the other hand, as can be seen from FIGS. 9 and 11, the SAS determination unit 14 determines whether or not the average logarithmic value greatly deviates from the approximate curve in the vicinity of 3 Hz, which is a characteristic feature of SAS patients.
  • the approximated curve is calculated by the method of least squares, but similar approximated curves may be calculated by other calculation methods instead of the method of least squares.
  • the biological vibration data is obtained from the mattress sensor as the biological data acquisition unit that acquires the biological vibration data due to the heartbeat, respiration, and body movement of the subject during sleep.
  • other sensors may be used as long as they can similarly acquire biological vibration data due to heartbeat, respiration, and body movement of the subject during sleep.

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Abstract

The present invention obtains biological vibration data from the heart rate, respiration, and body motion of a subject during sleep, performs frequency analysis on the acquired biological vibration data, and acquires any one among an average of the frequency spectrum at a location determined to be awakening, an average of the frequency spectrum at a location determined to be other than awakening, and an average of the frequency spectrum both at the location determined to be awakening and the location determined to be other than awakening. The present invention acquires an approximation curve for the logarithmic value of the obtained average of the frequency spectrum, and determines whether the subject suffers from sleep apnea syndrome on the basis of the unimodal characteristics of the logarithmic value of the average of the frequency spectrum.

Description

睡眠時無呼吸症候群判定装置、睡眠時無呼吸症候群判定方法およびプログラムDevice for determining sleep apnea syndrome, method and program for determining sleep apnea syndrome
 本発明は、被験者の睡眠時無呼吸症候群を判定する睡眠時無呼吸症候群判定装置、睡眠時無呼吸症候群判定方法およびプログラムに関する。 The present invention relates to a sleep apnea syndrome determination device, a sleep apnea syndrome determination method, and a program for determining sleep apnea syndrome of a subject.
 医療現場では、睡眠障害や睡眠時無呼吸症候群を診断するために、被測定者の睡眠状態を測定することが行われている。人間の睡眠段階は、睡眠の深さの観点で6段階に分類したものが知られており、その6つの睡眠段階は、眠りが浅い段階から順に、覚醒、レム睡眠、ノンレム睡眠(ステージ1~4)と呼ばれている。これらの6段階の睡眠段階の判定は、従来、例えば被計測者の顔や頭部に多数の電極を装着して、その多数の電極から脳波、眼球運動、および顎筋電を測定し、測定結果の解析により行われていた。 In the medical field, the sleep state of the person being measured is measured in order to diagnose sleep disorders and sleep apnea syndrome. It is known that the human sleep stage is classified into six stages from the viewpoint of the depth of sleep, and the six sleep stages are wakefulness, REM sleep, non-REM sleep (stage 1 to 4). Conventionally, these six sleep stages are determined by attaching a large number of electrodes to the subject's face and head, for example, and measuring electroencephalograms, eye movements, and jaw electromyograms from the large number of electrodes. Analysis of the results was done.
 また、睡眠時無呼吸症候群の患者は、睡眠時に無呼吸になって、息が苦しくなって眠りが浅くなり、睡眠段階が覚醒の状態になることが多く、無呼吸症候群の診断を行う上でも、睡眠段階を測定する必要がある。
 但し、睡眠時無呼吸症候群の判定を行うためには、睡眠段階の測定の他に、口と鼻の気流などの呼吸に伴った空気の流れの測定や、胸部および腹部の換気運動などの様々な測定を同時に行う必要がある。そして、睡眠段階の解析結果と呼吸状態の測定結果などに基づいて、医師が無呼吸症候群であるか否かを診断している。
In addition, patients with sleep apnea syndrome often experience apnea during sleep, difficulty breathing, light sleep, and awake sleep stages. , sleep stages should be measured.
However, in order to determine sleep apnea syndrome, in addition to the measurement of sleep stages, various measurements such as air flow associated with breathing such as mouth and nose air flow and ventilation movements of the chest and abdomen are required. measurements must be made simultaneously. Then, based on the analysis result of the sleep stage and the measurement result of the respiratory state, the doctor diagnoses whether or not the patient has an apnea syndrome.
 このような診断を行うために必要な、顔や頭部に多数の電極を装着した状態での睡眠の検査は、通常、医療機関に宿泊して、長時間連続して電極を身体に装着して行う検査であり、被測定者(患者)に精神的な負担と肉体的な負担を強いることになる。また、取得したデータは、専門知識と経験を持つ医師が解析して判定する必要がある。このため、睡眠時無呼吸症候群の判定は、簡単にできなかった。 Sleep tests with a large number of electrodes attached to the face and head, which are necessary for making such a diagnosis, are usually performed by staying at a medical institution and attaching electrodes to the body continuously for a long time. This is an examination performed by the patient, and imposes a mental and physical burden on the person (patient) to be measured. In addition, the acquired data must be analyzed and judged by a doctor with specialized knowledge and experience. For this reason, sleep apnea syndrome cannot be determined easily.
 睡眠段階の測定に関する問題を解決するために、専門医師による診断を不要とする睡眠段階推定手法は、従来から数多く提案されている。
 例えば、特許文献1には、遺伝的アルゴリズムによる学習手法を改良したDatabase-based Compact Genetic Algorithmと称される手法であって、マットレス型圧力センサの検出データから睡眠段階を推定する技術が記載されている。この特許文献1に記載された技術は、マットレス型圧力センサが検出した被測定者の体動と心拍に基づいて、睡眠段階を推定するものである。特許文献1に記載の技術によれば、マットレス型圧力センサを使って睡眠段階を推定することで、被測定者に負担を強いることなく、被測定者の睡眠状態を推定することができる。
In order to solve the problems related to sleep stage measurement, many sleep stage estimation methods that do not require diagnosis by a specialist have been proposed.
For example, Patent Document 1 describes a method called Database-based Compact Genetic Algorithm, which is an improved learning method using a genetic algorithm, and a technology for estimating sleep stages from detection data of a mattress type pressure sensor. there is The technique described in this Patent Document 1 estimates a sleep stage based on the subject's body motion and heart rate detected by a mattress-type pressure sensor. According to the technique described in Patent Literature 1, by estimating the sleep stage using the mattress-type pressure sensor, it is possible to estimate the sleep state of the subject without imposing a burden on the subject.
特開2014-239789号公報JP 2014-239789 A
 上述したように、無呼吸症候群の患者が睡眠時に無呼吸になることで、息が苦しくなって眠りが浅くなり、睡眠段階が覚醒の状態になることが多々ある。このため、マットレス型圧力センサの検出データ、つまり体動の検出データを使って睡眠段階を検出することは、無呼吸症候群の判定の一つの指標になる。 As mentioned above, when patients with apnea syndrome develop apnea during sleep, they often find it difficult to breathe, their sleep becomes shallow, and their sleep stage becomes awake. For this reason, detecting sleep stages using the detection data of the mattress-type pressure sensor, that is, the body movement detection data, is an index for determining apnea syndrome.
 睡眠中の睡眠段階が覚醒になることが多い場合に、無呼吸症候群になる可能性があるという点は、例えば後述する図5Aの健常者の睡眠段階の変化例と、図5Bの無呼吸症候群の患者の睡眠段階の変化例とを比較すると分かる。すなわち、図5から分かるように、無呼吸症候群の患者の方が、健常者と比べて覚醒の睡眠段階が多く発生している。
 しかしながら、健常者であっても、状況によっては深い睡眠ができない場合も多々あり、睡眠中の睡眠段階が覚醒になる回数が多いからと言って、それだけで無呼吸症候群と判断することはできない。このため、無呼吸症候群をより的確な判定することが急務となっていた。
The point that there is a possibility of apnea syndrome when the sleep stage during sleep often becomes arousal is, for example, the change example of the sleep stage of a healthy person in FIG. 5A described later and the apnea syndrome in FIG. 5B This can be seen by comparing the changes in sleep stages of patients with That is, as can be seen from FIG. 5, the apnea syndrome patients have more awakening sleep stages than healthy subjects.
However, even in healthy people, there are many cases in which deep sleep is not possible depending on the situation, and it is not possible to judge that apnea syndrome is present simply because of the large number of awakenings in the sleep stage during sleep. Therefore, there is an urgent need to more accurately determine apnea syndrome.
 以上説明したように、被測定者への負担をかけずに睡眠時無呼吸症候群の判定を行うことが可能な無呼吸症候群判定装置、無呼吸症候群判定方法およびプログラムの開発が望まれていた。 As described above, it has been desired to develop an apnea syndrome determination device, an apnea syndrome determination method, and a program that can determine sleep apnea syndrome without imposing a burden on the subject.
 本発明の睡眠時無呼吸症候群判定装置は、被測定者の睡眠中の心拍、呼吸、及び体動による生体振動データを取得する生体データ取得部と、生体データ取得部が取得した生体振動データを周波数解析して、睡眠中の覚醒と判定された箇所の周波数スペクトルの平均、覚醒以外と判定された箇所の周波数スペクトルの平均、及び覚醒と判定された箇所とそれ以外と判定された箇所の双方の周波数スペクトルの平均のいずれかを取得する生体データ処理部と、生体データ取得部で得られた周波数スペクトルの平均の対数値に対する近似曲線を取得すると共に、生体データ取得部で得られた周波数スペクトルの平均の対数値が、取得した近似曲線から正の方向または負の方向に外れる量を検出し、検出した近似曲線から正の方向または負の方向に外れた量の大きさに基づいて、被測定者が睡眠時無呼吸症候群であるかの判定を行う判定部と、を備える。 The sleep apnea syndrome determination device of the present invention includes a biological data acquisition unit that acquires biological vibration data due to heartbeat, respiration, and body movement during sleep of the subject, and the biological vibration data acquired by the biological data acquisition unit. By frequency analysis, the average of the frequency spectrum of the location determined to be arousal during sleep, the average of the frequency spectrum of the location determined to be other than arousal, and the location determined to be awake and the location determined to be other than that a biological data processing unit that acquires one of the averages of the frequency spectrum of the biological data acquisition unit, acquires an approximate curve for the logarithm of the average of the frequency spectra obtained by the biological data acquisition unit, and obtains the frequency spectrum obtained by the biological data acquisition unit Detects the amount by which the logarithm of the average deviates from the fitted curve in the positive or negative direction a determination unit that determines whether the subject has sleep apnea syndrome.
 また、本発明の睡眠時無呼吸症候群判定方法は、被測定者の睡眠中の心拍、呼吸、及び体動による生体振動データを取得する生体データ取得処理と、生体データ取得処理により取得した生体振動データを周波数解析して、睡眠中の覚醒と判定された箇所の周波数スペクトルの平均、覚醒以外と判定された箇所の周波数スペクトルの平均、及び覚醒と判定された箇所とそれ以外と判定された箇所の双方の周波数スペクトルの平均のいずれかを取得する生体データ処理と、生体データ処理により得られた周波数スペクトルの平均の対数値に対する近似曲線を取得する近似曲線取得処理と、生体データ処理により得られた周波数スペクトルの平均の対数値が、近似曲線取得処理で取得した近似曲線から正の方向または負の方向に外れる量を検出し、検出した近似曲線から正の方向または負の方向に外れた量の大きさに基づいて、前記被測定者が睡眠時無呼吸症候群であるかの判定を行う判定部判定処理と、を含む。 Further, the sleep apnea syndrome determination method of the present invention includes a biological data acquisition process for acquiring biological vibration data due to heartbeat, respiration, and body movement during sleep of the subject, and biological vibration acquired by the biological data acquisition process. Frequency analysis of the data, the average of the frequency spectrum of the location determined to be arousal during sleep, the average of the frequency spectrum of the location determined to be other than awake, and the location determined to be awake and other locations Biological data processing for obtaining one of the averages of both frequency spectra, Approximate curve acquisition processing for obtaining an approximated curve for the logarithm of the average of the frequency spectra obtained by the biological data processing, and obtained by the biological data processing Detects the amount by which the average logarithm value of the frequency spectrum deviates from the approximated curve acquired in the approximated curve acquisition process in the positive or negative direction, and the amount by which the detected approximated curve deviates in the positive or negative direction and a determining unit determining process for determining whether the subject has sleep apnea syndrome based on the magnitude of the measurement.
 また、本発明のプログラムは、上記の睡眠時無呼吸症候群判定方法が行う各処理を手順としてコンピュータに実行させるプログラムである。 Further, the program of the present invention is a program that causes a computer to execute each process performed by the sleep apnea syndrome determination method as a procedure.
本発明の一実施の形態例による睡眠時無呼吸症候群判定装置の構成例を示すブロック図である。BRIEF DESCRIPTION OF THE DRAWINGS It is a block diagram which shows the structural example of the sleep apnea syndrome determination apparatus by one embodiment of this invention. 本発明の一実施の形態例による睡眠時無呼吸症候群の判定状態の例を示す図である。FIG. 4 is a diagram showing an example of sleep apnea syndrome determination state according to an embodiment of the present invention; 本発明の一実施の形態例の睡眠時無呼吸症候群判定装置のハードウェア構成例を示すブロック図である。1 is a block diagram showing a hardware configuration example of a device for determining sleep apnea syndrome according to an embodiment of the present invention; FIG. 本発明の一実施の形態例による睡眠中のパワースペクトルを一定期間ごとに得る処理の例を示す図である。FIG. 5 is a diagram showing an example of processing for obtaining a power spectrum during sleep at regular intervals according to an embodiment of the present invention; 図5Aは、健常者の睡眠中の睡眠段階の変化例を示す。 図5Bは、睡眠時無呼吸症候群の患者の睡眠中の睡眠段階の変化例を示す。FIG. 5A shows an example of changes in sleep stages during sleep of a healthy subject. FIG. 5B shows an example of sleep stage changes during sleep of a patient with sleep apnea syndrome. 本発明の一実施の形態例による睡眠時無呼吸症候群を判定するための処理の流れを示すフローチャートである。4 is a flow chart showing the flow of processing for determining sleep apnea syndrome according to one embodiment of the present invention. 図7Aは、健常者の睡眠中の振動の周波数に対する寄与度の例を示す。 図7Bは、健常者の睡眠中の覚醒(W)の区間の振動の周波数の平均の例を示す。 図7Cは、健常者の睡眠中の覚醒以外の区間の振動の周波数の平均の例を示す。 図7Dは、健常者の睡眠中の覚醒(W)の区間の振動の周波数の平均の対数値の例を示す。 図7Eは、健常者の睡眠中の覚醒以外の区間の振動の周波数の平均の対数値の例を示す図である。FIG. 7A shows an example of contribution to vibration frequency during sleep of a healthy subject. FIG. 7B shows an example of the average vibration frequency in the wakefulness (W) section during sleep of a healthy subject. FIG. 7C shows an example of the average frequency of vibrations in the non-awakening period during sleep of a healthy subject. FIG. 7D shows an example of the average logarithmic value of the vibration frequency in the wakefulness (W) period during sleep of a healthy subject. FIG. 7E is a diagram showing an example of the average logarithmic value of the vibration frequency in the non-awakening period during sleep of a healthy subject. 図8Aは、睡眠時無呼吸症候群の患者の睡眠中の振動の周波数に対する寄与度の例を示す。 図8Bは、睡眠時無呼吸症候群の患者の睡眠中の覚醒(W)の区間の振動の周波数の平均の例を示す。 図8Cは、睡眠時無呼吸症候群の患者の睡眠中の覚醒以外の区間の振動の周波数の平均の例を示す。 図8Dは、睡眠時無呼吸症候群の患者の睡眠中の覚醒(W)の区間の振動の周波数の平均の対数値の例を示す。 図8Eは、睡眠時無呼吸症候群の患者の睡眠中の覚醒以外の区間の振動の周波数の平均の対数値の例を示す図である。FIG. 8A shows an example of the contribution to the frequency of oscillations during sleep of a patient with sleep apnea. FIG. 8B shows an example of the average frequency of oscillations in the arousal (W) interval during sleep of a patient with sleep apnea syndrome. FIG. 8C shows an example of the average frequency of vibrations in the non-awakening period during sleep of a patient with sleep apnea syndrome. FIG. 8D shows an example of the average logarithmic value of the oscillation frequency in the wakefulness (W) interval during sleep of a patient with sleep apnea syndrome. Fig. 8E is a diagram showing an example of the average logarithmic value of the frequency of the vibration in the interval other than the awakening period during sleep of a patient with sleep apnea syndrome. 睡眠時無呼吸症候群の患者の覚醒以外の区間の振動の周波数の平均の対数演算値を近似曲線と重ねた代表例を示す図である。FIG. 10 is a diagram showing a representative example in which the average logarithmically calculated value of the frequency of vibrations in a section other than awakening of a patient with sleep apnea syndrome is superimposed on an approximate curve. 図10Aは、健常者の覚醒以外の区間の振動の周波数の平均の対数演算値を近似曲線と重ねた一人目の例を示す図である。 図10Bは、健常者の覚醒以外の区間の振動の周波数の平均の対数演算値を近似曲線と重ねた二人目の例を示す図である。 図10Cは、健常者の覚醒以外の区間の振動の周波数の平均の対数演算値を近似曲線と重ねた三人目の例を示す図である。 図10Dは、健常者の覚醒以外の区間の振動の周波数の平均の対数演算値を近似曲線と重ねた四人目の例を示す図である。 図10Eは、健常者の覚醒以外の区間の振動の周波数の平均の対数演算値を近似曲線と重ねた五人目の例を示す図である。FIG. 10A is a diagram showing a first example in which the average logarithmically calculated value of the frequency of vibration in a section other than awakening of a healthy person is superimposed on an approximate curve. FIG. 10B is a diagram showing an example of the second person in which the average logarithmically calculated value of the vibration frequency in the non-awakening section of the healthy person is superimposed on the approximate curve. FIG. 10C is a diagram showing an example of a third person in which the average logarithmically calculated value of the vibration frequency in the non-awakening section of the healthy person is superimposed on the approximation curve. FIG. 10D is a diagram showing an example of a fourth person in which the average logarithmically calculated value of the vibration frequency in the section other than the awakening of the healthy person is superimposed on the approximate curve. FIG. 10E is a diagram showing an example of the fifth person in which the average logarithmically calculated value of the vibration frequency in the section other than the awakening of the healthy person is superimposed on the approximation curve. 図11Aは、睡眠時無呼吸症候群の患者の覚醒以外の区間の振動の周波数の平均の対数演算値を近似曲線と重ねた一人目の例を示す図である。 図11Bは、睡眠時無呼吸症候群の患者の覚醒以外の区間の振動の周波数の平均の対数演算値を近似曲線と重ねた二人目の例を示す図である。 図11Cは、睡眠時無呼吸症候群の患者の覚醒以外の区間の振動の周波数の平均の対数演算値を近似曲線と重ねた三人目の例を示す図である。 図11Dは、睡眠時無呼吸症候群の患者の覚醒以外の区間の振動の周波数の平均の対数演算値を近似曲線と重ねた四人目の例を示す図である。 図11Eは、睡眠時無呼吸症候群の患者の覚醒以外の区間の振動の周波数の平均の対数演算値を近似曲線と重ねた五人目の例を示す図である。FIG. 11A is a diagram showing a first example in which an average logarithmically calculated value of the frequency of vibrations in a section other than awakening of a patient with sleep apnea syndrome is superimposed on an approximate curve. FIG. 11B is a diagram showing a second example in which the average logarithmically calculated value of the vibration frequency in the interval other than the awakening period of the patient with sleep apnea syndrome is superimposed on the approximation curve. FIG. 11C is a diagram showing a third example in which the average logarithmically calculated value of the vibration frequency in the interval other than the awakening period of the patient with sleep apnea syndrome is superimposed on the approximation curve. FIG. 11D is a diagram showing a fourth example in which the average logarithmically calculated value of the vibration frequency in the interval other than the awakening period of the patient with sleep apnea syndrome is superimposed on the approximate curve. FIG. 11E is a diagram showing a fifth example in which the average logarithmically calculated value of the vibration frequency in the interval other than the awakening period of the patient with sleep apnea syndrome is superimposed on the approximate curve. 本発明の一実施の形態例による判定結果の例を示す図である。It is a figure which shows the example of the determination result by one embodiment of this invention.
 以下、本発明の一実施の形態例(以下、「本例」と称する)について、図面を参照して説明する。なお、以下の説明や図面では、睡眠時無呼吸症候群をSAS、睡眠時無呼吸症候群の患者をSAS患者と称する。また、以下の説明で健常者と述べた場合には、SAS患者でない者を示す。
[1.睡眠時無呼吸症候群判定装置の構成]
 図1は、本例の睡眠時無呼吸症候群判定装置10の構成を示すブロック図である。
 図2は、本例の睡眠時無呼吸症候群判定装置10を使って睡眠時無呼吸症候群の判定を行う状態の例を示す図である。
An embodiment of the present invention (hereinafter referred to as "this example") will now be described with reference to the drawings. In the following description and drawings, the sleep apnea syndrome is referred to as SAS, and the sleep apnea syndrome patient is referred to as the SAS patient. In addition, when referring to healthy subjects in the following description, it means those who are not SAS patients.
[1. Configuration of sleep apnea syndrome determination device]
FIG. 1 is a block diagram showing the configuration of a sleep apnea syndrome determination device 10 of this example.
FIG. 2 is a diagram showing an example of a state in which sleep apnea syndrome determination is performed using the sleep apnea syndrome determination device 10 of this example.
 本実施の形態例の睡眠時無呼吸症候群判定装置10は、被測定者の生体振動をマットレスセンサ2で圧力データとして取得する。この生体振動には、被測定者の体動による振動成分の他に、心拍や呼吸による振動成分が含まれる。マットレスセンサ2は、被測定者Aの睡眠中の上半身の生体振動を圧力の変化として検出する。マットレスセンサ2は、例えば図2に示すように、被測定者Aが睡眠を行うベッド1のマットレスの上あるいは下に敷いて用いられる。
 なお、被測定者Aの下側になるマットレスの上にマットレスセンサ2を配置するのは一例であり、例えばマットレスの中にマットレスセンサ2を内蔵させてもよい。
The sleep apnea syndrome determination device 10 of the present embodiment acquires the body vibration of the person to be measured as pressure data with the mattress sensor 2 . The bio-vibration includes vibration components due to heartbeat and respiration in addition to vibration components due to the body motion of the person being measured. The mattress sensor 2 detects the bio-vibration of the upper body of the subject A during sleep as a change in pressure. The mattress sensor 2 is used by laying it on or under the mattress of the bed 1 on which the subject A sleeps, as shown in FIG. 2, for example.
It should be noted that the arrangement of the mattress sensor 2 on the mattress under the subject A is an example, and the mattress sensor 2 may be incorporated in the mattress, for example.
 図2では、ベッド1の脇に睡眠時無呼吸症候群判定装置10を設置し、マットレスセンサ2と睡眠時無呼吸症候群判定装置10をケーブルで接続した例を示すが、例えばマットレスセンサ2が取得した圧力データ(生体振動データ)を、無線伝送で別の部屋の睡眠時無呼吸症候群判定装置10に伝送するようにしてもよい。
 以下の説明では、マットレスセンサ2が出力する圧力データを、生体振動データと称する。
 なお、生体振動データを圧力センサから得るのは一例であり、その他のセンサを使ってもよい。例えば、赤外線センサやレーザなどを使って、非接触で睡眠中の被測定者の振動を測定してもよい。
FIG. 2 shows an example in which the sleep apnea syndrome determination device 10 is installed beside the bed 1 and the mattress sensor 2 and the sleep apnea syndrome determination device 10 are connected with a cable. The pressure data (biological vibration data) may be wirelessly transmitted to the sleep apnea syndrome determination device 10 in another room.
In the following description, the pressure data output by the mattress sensor 2 will be referred to as biological vibration data.
It should be noted that obtaining biological vibration data from a pressure sensor is an example, and other sensors may be used. For example, an infrared sensor, a laser, or the like may be used to measure the vibration of the subject during sleep without contact.
 図1に示すように、睡眠時無呼吸症候群判定装置10は、生体データ取得部11、生体データ処理部12、睡眠段階判定部13、睡眠時無呼吸症候群判定部(以下、「SAS判定部」と称する)14、および出力部15を備える。
 生体データ取得部11は、マットレスセンサ2が出力する生体振動データを取得する生体データ取得処理を行う。生体データ取得部11が取得した生体振動データは、生体データ処理部12に供給される。
As shown in FIG. 1, the sleep apnea syndrome determination device 10 includes a biological data acquisition unit 11, a biological data processing unit 12, a sleep stage determination unit 13, a sleep apnea syndrome determination unit (hereinafter, "SAS determination unit"). ) 14 and an output unit 15 .
The biometric data acquisition unit 11 performs biodata acquisition processing for acquiring biovibration data output from the mattress sensor 2 . The biological vibration data acquired by the biological data acquisition section 11 is supplied to the biological data processing section 12 .
 生体データ処理部12は、供給される生体振動データをサンプリングしてデジタルデータ化し、デジタルデータ化された生体振動データの周波数のパワースペクトルを算出する。この生体振動データの周波数のパワースペクトルを算出する処理は、30秒周期で行われる。但し、本例では、実際には1回の算出を32秒間行い、その32秒間の算出を30秒周期で、つまり2秒間だけ重なって行うようにしている。 The biological data processing unit 12 samples the supplied biological vibration data, converts it into digital data, and calculates the frequency power spectrum of the digitalized biological vibration data. The process of calculating the power spectrum of the frequency of this biovibration data is performed in a cycle of 30 seconds. However, in this example, one calculation is actually performed for 32 seconds, and the calculation for the 32 seconds is performed in a cycle of 30 seconds, that is, overlapped by 2 seconds.
 なお、生体データ処理部12が30秒周期でパワースペクトルを算出するのは一例であり、30秒よりも短い周期、または30秒よりも長い周期でパワースペクトルを算出してもよい。例えば、生体データ処理部12は、60秒周期でパワースペクトルを算出してもよい。1回の算出において、2秒間重なるようにした点も一例であり、重なる期間がないようにしてもよい。
 そして、生体データ処理部12は、睡眠中の一定期間ごとのパワースペクトルの算出結果を、睡眠段階判定部13とSAS判定部14に供給する。
It should be noted that the calculation of the power spectrum by the biological data processing unit 12 at a cycle of 30 seconds is an example, and the power spectrum may be calculated at a cycle shorter than 30 seconds or longer than 30 seconds. For example, the biological data processing unit 12 may calculate the power spectrum in 60-second cycles. The overlapping of two seconds in one calculation is also an example, and there may be no overlapping period.
Then, the biological data processing unit 12 supplies the calculation result of the power spectrum for each fixed period during sleep to the sleep stage determination unit 13 and the SAS determination unit 14 .
 睡眠段階判定部13は、一定期間ごとのパワースペクトルの算出結果に基づいて、その期間の被測定者の睡眠段階を判定する。この睡眠段階を判定する際には、パワースペクトルだけでなく、生体振動データを算出して得られる様々な特徴量を使って判定してもよい。また、機械学習の1つであるランダムフォレストを用いて、特徴量のデータから睡眠段階を判定してもよい。 The sleep stage determination unit 13 determines the subject's sleep stage for each period based on the calculation result of the power spectrum for each certain period. When determining this sleep stage, not only the power spectrum but also various feature amounts obtained by calculating the biological vibration data may be used for determination. Alternatively, a random forest, which is one of machine learning, may be used to determine a sleep stage from feature amount data.
 睡眠段階としては、睡眠段階が浅い方から順に、覚醒(WAKE)、レム睡眠(R)、ノンレム睡眠(NR)があり、ノンレム睡眠については、ステージ1からステージ4の4段階(NR1-NR4)に分けられる。したがって、睡眠段階は、合計で6段階に分けられることになる。6段階の睡眠段階の中では、ステージ4のノンレム睡眠(NR4)が最も深い睡眠段階である。但し、実際の睡眠でステージ4のノンレム睡眠のような深い睡眠段階になることは稀である。
 なお、本例の睡眠段階判定部13は、このような6段階の睡眠段階すべてを判定する必要はなく、少なくとも覚醒(WAKE)か否かを判定すればよい。
 また、本例の睡眠段階判定部13は、生体振動データから睡眠段階を判定するようにしたが、睡眠段階判定部13は、脳波などのその他の生体データから睡眠段階を判定してもよい。
As sleep stages, there are awakening (WAKE), REM sleep (R), and non-REM sleep (NR) in order from the lightest sleep stage. Non-REM sleep has four stages from stage 1 to stage 4 (NR1-NR4). divided into Therefore, sleep stages are divided into six stages in total. Among the six sleep stages, stage 4 non-REM sleep (NR4) is the deepest sleep stage. However, in actual sleep, it is rare to enter a deep sleep stage such as stage 4 non-REM sleep.
It should be noted that the sleep stage determination unit 13 of this example does not need to determine all of these six sleep stages, and may at least determine whether or not it is wakefulness (WAKE).
In addition, although the sleep stage determination unit 13 of this example determines sleep stages from biological vibration data, the sleep stage determination unit 13 may determine sleep stages from other biological data such as electroencephalograms.
 SAS判定部14は、一定期間ごとのパワースペクトルの算出結果と、睡眠段階判定部13での睡眠段階の判定結果から、被測定者が睡眠時無呼吸症候群(SAS)か否かを判定する。なお、SAS判定部14がSASか否かを判定する際に、睡眠段階を利用するのはあくまでも一例であり、SASの判定に睡眠段階の判定結果を使用しなくてもよい。
 SAS判定部14が、一定期間ごとのパワースペクトルの算出結果などからSASを判定する処理手順の詳細は後述する。
The SAS determination unit 14 determines whether or not the subject has sleep apnea syndrome (SAS) based on the calculation result of the power spectrum for each fixed period and the sleep stage determination result of the sleep stage determination unit 13 . It should be noted that the use of the sleep stage when the SAS determination unit 14 determines whether or not it is SAS is merely an example, and it is not necessary to use the determination result of the sleep stage for determination of SAS.
The details of the processing procedure for the SAS determination unit 14 to determine the SAS based on the calculation results of the power spectrum for each fixed period will be described later.
 出力部15は、SAS判定部14が判定した睡眠時無呼吸症候群か否かの結果を出力する。出力部15は、例えば表示装置により構成され、睡眠時無呼吸症候群の判定結果を表示する。あるいは、出力部15を記録装置として構成して、一晩の睡眠状態などと共に睡眠時無呼吸症候群の判定結果を記録するようにしてもよい。また、出力部15は、表示または記録を行う際には、睡眠時無呼吸症候群の判定結果だけでなく、睡眠段階の判定結果の表示または記録を同時に行うようにしてもよい。 The output unit 15 outputs the result of sleep apnea syndrome determined by the SAS determination unit 14 . The output unit 15 is configured by, for example, a display device, and displays the determination result of sleep apnea syndrome. Alternatively, the output unit 15 may be configured as a recording device to record the determination result of the sleep apnea syndrome together with the sleeping state of the night. Further, when displaying or recording, the output unit 15 may display or record not only the sleep apnea syndrome determination result but also the sleep stage determination result at the same time.
 さらに、出力部15を外部の別の端末として、ネットワーク経由で判定結果を伝送するようにしてもよい。例えば、出力部15を予め登録されたスマートフォンとして、判定結果を伝送してもよい。
 なお、本例の睡眠時無呼吸症候群判定装置10は、睡眠時のデータ取得と判定をリアルタイムで行うようにしてもよい。一方、睡眠時無呼吸症候群判定装置10は、睡眠時には生体データ取得部11が生体振動データの取得のみを行い、取得したデータを記録しておき、後日、記録データを使って判定までの処理を行うようにしてもよい。
Further, the output unit 15 may be another external terminal to transmit the determination result via the network. For example, the output unit 15 may be a pre-registered smartphone, and the determination result may be transmitted.
In addition, the sleep apnea syndrome determination apparatus 10 of this example may perform data acquisition and determination during sleep in real time. On the other hand, in the sleep apnea syndrome determination device 10, the biological data acquisition unit 11 only acquires biological vibration data during sleep, records the acquired data, and uses the recorded data to perform processing up to determination at a later date. You can do it.
[2.睡眠時無呼吸症候群判定装置のハードウェア構成例]
 図3は、睡眠時無呼吸症候群判定装置10をコンピュータ装置で構成した場合のハードウェア構成例を示す。
 コンピュータ装置Cは、バスC8に接続されたCPU(Central Processing Unit:中央処理装置)C1、ROM(Read Only Memory)C2、およびRAM(Random Access Memory)C3を備える。さらに、コンピュータ装置Cは、不揮発性ストレージC4、ネットワークインターフェイスC5、入力装置C6、および表示装置C7を備える。
[2. Hardware configuration example of sleep apnea syndrome determination device]
FIG. 3 shows a hardware configuration example when the sleep apnea syndrome determination device 10 is configured by a computer device.
The computer device C includes a CPU (Central Processing Unit) C1, a ROM (Read Only Memory) C2, and a RAM (Random Access Memory) C3 connected to a bus C8. Further, computer device C comprises non-volatile storage C4, network interface C5, input device C6, and display device C7.
 CPU C1は、睡眠時無呼吸症候群判定装置10の生体データ処理部12、睡眠段階判定部13、SAS判定部14が備える各機能を実現するソフトウェアのプログラムコードをROM C2から読み出して実行する。圧力データを周波数解析する処理、睡眠段階を判定する処理、SASと判定する処理についても、これらの処理を実行するプログラムをROM C2から読み出して、CPU C1が実行する。RAM C3には、演算処理の途中に発生した変数やパラメータ等が一時的に書き込まれる。 The CPU C1 reads from the ROM C2 the program code of the software that realizes each function of the biological data processing unit 12, the sleep stage determination unit 13, and the SAS determination unit 14 of the sleep apnea syndrome determination device 10 and executes it. For the process of frequency analysis of pressure data, the process of judging sleep stages, and the process of judging SAS, the programs for executing these processes are also read out from ROM C2 and executed by CPU C1. Variables, parameters, etc. generated during arithmetic processing are temporarily written to RAM C3.
 不揮発性ストレージC4としては、例えば、HDD(Hard disk drive)、SSD(Solid State Drive)、フレキシブルディスク、光ディスク、光磁気ディスク、CD-ROM、不揮発性のメモリ等が用いられる。この不揮発性ストレージC4には、OS(Operating System)、各種のパラメータの他に、コンピュータ装置Cを睡眠時無呼吸症候群判定装置10として機能させるためのプログラムが記録され、プログラムを記録する記録媒体としている。また、睡眠段階判定部13が判定した睡眠段階や、SAS判定部14が判定したSASの判定結果についてのデータも、不揮発性ストレージC4に記録される。 As the non-volatile storage C4, for example, HDD (Hard disk drive), SSD (Solid State Drive), flexible disk, optical disk, magneto-optical disk, CD-ROM, non-volatile memory, etc. are used. In addition to the OS (Operating System) and various parameters, the nonvolatile storage C4 stores a program for causing the computer device C to function as the sleep apnea syndrome determination device 10, and as a recording medium for recording the program there is In addition, data about the sleep stages determined by the sleep stage determination unit 13 and the SAS determination results determined by the SAS determination unit 14 are also recorded in the nonvolatile storage C4.
 ネットワークインターフェイスC5には、例えば、NIC(Network Interface Card)等が用いられ、端子が接続されたLAN(Local Area Network)、専用線等を介して各種のデータを送受信することが可能である。例えば、コンピュータ装置Cは、マットレスセンサ2が出力する圧力データを、ネットワークインターフェイスC5を介して取得する。
 入力装置C6は、例えばキーボード等の機器で構成され、この入力装置C6により、睡眠時無呼吸症候群判定装置10で睡眠時無呼吸症候群を判定する期間の設定や、判定結果の表示形態の指示等が行われる。表示装置C7には、睡眠時無呼吸症候群判定装置10で睡眠時無呼吸症候群の判定結果が表示される。
For the network interface C5, for example, a NIC (Network Interface Card) or the like is used, and various data can be transmitted and received via a LAN (Local Area Network) to which terminals are connected, a dedicated line, or the like. For example, the computer device C acquires pressure data output by the mattress sensor 2 via the network interface C5.
The input device C6 is configured by, for example, a device such as a keyboard, and the input device C6 is used to set the period for determining sleep apnea syndrome by the sleep apnea syndrome determination device 10, to instruct the display format of the determination result, etc. is done. The result of sleep apnea syndrome determination by the sleep apnea syndrome determination device 10 is displayed on the display device C7.
 なお、睡眠時無呼吸症候群判定装置10を、記録媒体に記録されたプログラム(ソフトウェア)の実行で判定装置として機能するコンピュータ装置から構成するのは一例であり、睡眠時無呼吸症候群判定装置10の一部または全ての処理を実行する専用のハードウェアを用意してもよい。 Note that the sleep apnea syndrome determination device 10 is an example of a computer device that functions as a determination device by executing a program (software) recorded on a recording medium, and the sleep apnea syndrome determination device 10 Dedicated hardware that executes some or all of the processing may be prepared.
[3.周波数のパワースペクトルの算出状態と睡眠段階の判定例]
 次に、本例の睡眠時無呼吸症候群判定装置10の各部で行われる処理について説明する。
 まず、図4を参照して、生体データ処理部12が、一定期間に睡眠中の生体振動の周波数のパワースペクトルを算出する例を説明する。既に説明したように、本例の場合には、生体データ処理部12は、32秒間のデータを、30秒間隔で処理している。
 すなわち、図4に示すように、生体データ取得部11は、最初に入眠(0秒)から32秒までのマットレスセンサ2のセンサ値を取得し、以下、30秒周期で32秒間のセンサ値を取得していく。生体データ取得部11は、この生体振動データの取得を就寝から起床まで連続して行い、取得した生体振動データを、生体データ処理部12に供給する。
[3. Calculation state of frequency power spectrum and determination example of sleep stage]
Next, processing performed in each part of the sleep apnea syndrome determination device 10 of this example will be described.
First, with reference to FIG. 4, an example in which the biological data processing unit 12 calculates the power spectrum of the frequencies of biological vibrations during sleep for a certain period of time will be described. As already explained, in the case of this example, the biological data processing unit 12 processes data for 32 seconds at intervals of 30 seconds.
That is, as shown in FIG. 4, the biometric data acquisition unit 11 first acquires the sensor values of the mattress sensor 2 from falling asleep (0 seconds) to 32 seconds, and thereafter acquires sensor values for 32 seconds at intervals of 30 seconds. I will get. The biological data acquisition unit 11 continuously acquires the biological vibration data from going to bed to waking up, and supplies the acquired biological vibration data to the biological data processing unit 12 .
 そして、生体データ処理部12は、32秒間の周波数のパワースペクトルを図4の右下に示すように算出する。このパワースペクトルを算出する周波数解析は、例えば高速フーリエ変換(FFT:fast fourier transform)により行われる。
 本例のような睡眠中の生体振動のパワースペクトルを算出したとき、高い密度を示す周波数は、心拍による生体振動の成分と、呼吸による生体振動の成分である。通常、心拍の周波数は1Hz前後であり、呼吸による生体振動の成分は2Hz前後である。但し、SAS患者の場合には、無呼吸となる区間があるため、呼吸による生体振動が生じていない期間がある。また、寝返りなどの大きな体動がある区間では、心拍や呼吸に比べて非常に大きな体動による生体振動の成分が生じる。
Then, the biometric data processing unit 12 calculates the frequency power spectrum for 32 seconds as shown in the lower right of FIG. Frequency analysis for calculating this power spectrum is performed by, for example, fast Fourier transform (FFT).
When the power spectrum of the bio-vibration during sleep is calculated as in this example, the frequencies exhibiting a high density are the bio-vibration component due to heartbeat and the bio-vibration component due to respiration. Normally, the heartbeat frequency is around 1 Hz, and the component of bio-vibration due to respiration is around 2 Hz. However, in the case of an SAS patient, there is an apnea section, and therefore there is a period during which no biological vibration due to respiration occurs. In addition, in sections with large body movements such as rolling over, a bio-vibration component is generated due to body movements that are much larger than heartbeats and respirations.
 睡眠段階判定部13は、この図4に示すような生体振動のパワースペクトルから、30秒間で主となる睡眠段階を判定する。生体振動のパワースペクトルから睡眠段階を判定する手法については、本願の発明者らが既に提案した手法が適用可能である。ここでは説明を省略するが、この手法によれば、例えば睡眠段階ごとに異なる特徴量の発生状況から睡眠段階を判定することができる。図4の例では、入眠から最初の30秒間は覚醒(W)と判定し、次の30秒間はステージ2のノンレム(N2)と判定している。 The sleep stage determination unit 13 determines the main sleep stage in 30 seconds from the power spectrum of biological vibrations as shown in FIG. The method already proposed by the inventors of the present application can be applied as a method for determining sleep stages from the power spectrum of biological vibrations. Although description is omitted here, according to this method, for example, the sleep stage can be determined from the occurrence of different feature amounts for each sleep stage. In the example of FIG. 4, the first 30 seconds after falling asleep are determined to be wakefulness (W), and the next 30 seconds are determined to be stage 2 non-REM (N2).
 図5は、ある睡眠期間での、健常者の睡眠段階を測定した例(図5A)と、睡眠時無呼吸症候群の患者(SAS患者)の睡眠段階を測定した例(図5B)を比較した図である。 Figure 5 compares an example of measuring the sleep stages of a healthy subject (Figure 5A) and an example of measuring the sleep stages of a patient with sleep apnea syndrome (SAS patient) during a certain sleep period (Figure 5B). It is a diagram.
 図5では、横軸が睡眠時間を示し、縦軸が睡眠段階を示す。縦軸の睡眠段階は、最も上側が、最も睡眠段階が浅い覚醒(WAKE)を示し、下側に行くほど、レム睡眠(REM)、ステージ1のノンレム睡眠(NREM1)、ステージ2のノンレム睡眠(NREM2)、ステージ3のノンレム睡眠(NREM3)、ステージ4のノンレム睡眠(NREM4)と順に変化して、深い睡眠段階になることを示す。
 但し、図5の例では、図5Aに示す健常者と、図5Bに示すSAS患者のいずれの場合も、最も深い睡眠段階は、ステージ3のノンレム睡眠(NREM3)であり、ステージ4のノンレム睡眠(NREM4)には到達していない。
In FIG. 5, the horizontal axis indicates sleep time, and the vertical axis indicates sleep stages. The sleep stage on the vertical axis shows the lightest wakefulness (WAKE) at the top, and REM sleep (REM), stage 1 non-REM sleep (NREM1), stage 2 non-REM sleep (NREM1) toward the bottom. NREM 2), stage 3 non-REM sleep (NREM 3), and stage 4 non-REM sleep (NREM 4), indicating a deep sleep stage.
However, in the example of FIG. 5, in both the healthy subject shown in FIG. 5A and the SAS patient shown in FIG. (NREM4) has not been reached.
 図5Aの健常者と図5BのSAS患者とを比較すると判るように、SAS患者は無呼吸が原因での覚醒(WAKE)が多発している。
 なお、SAS患者の場合、覚醒(WAKE)時には、舌が気道を塞ぐことにより無呼吸が起こり、その影響が拍動(心拍)の変化に現れる。
As can be seen by comparing the healthy subject in FIG. 5A and the SAS patient in FIG. 5B, the SAS patient frequently wakes up due to apnea (WAKE).
In the case of an SAS patient, when the patient wakes up (WAKE), the tongue blocks the airway, causing apnea, and its influence appears in changes in pulsation (heart rate).
 ここで、本例の睡眠時無呼吸症候群判定装置10では、主として覚醒(WAKE)以外の区間での周波数スペクトルに現れる特徴から、以下に説明する処理手順でSASの判定が行われる。但し、覚醒(WAKE)以外の区間の周波数スペクトルから、SASの判定を行うのは一例であり、後述するように覚醒(WAKE)の区間の周波数スペクトルを使って、あるいは覚醒の区別を行わずに全ての区間の周波数スペクトルを使って、SASの判定を行うことも可能である。 Here, in the sleep apnea syndrome determination device 10 of this example, SAS is determined according to the processing procedure described below, mainly from the features that appear in the frequency spectrum in intervals other than wake (WAKE). However, it is an example to determine the SAS from the frequency spectrum of the interval other than wakefulness (WAKE). It is also possible to determine SAS using the frequency spectrum of all sections.
[4.睡眠時無呼吸症候群(SAS)の判定処理の流れ]
 図6は、本例の睡眠時無呼吸症候群判定装置10でSASの判定を行う処理の流れを示すフローチャートである。
 まず、生体データ取得部11は、睡眠中の生体振動データを取得する(ステップS11)。この生体振動データは、睡眠中に取得するリアルタイムのデータでもよいが、記録された生体振動データであってもよい。
[4. Flow of sleep apnea syndrome (SAS) determination processing]
FIG. 6 is a flow chart showing the flow of processing for determining SAS by the sleep apnea syndrome determination device 10 of this example.
First, the biometric data acquisition unit 11 acquires biovibration data during sleep (step S11). The biological vibration data may be real-time data acquired during sleep, or may be recorded biological vibration data.
 次に、生体データ処理部12は、一定期間ごとに生体振動データの特徴量を算出する(ステップS12)。そして、睡眠段階判定部13は、生体データ処理部12が算出した特徴量に基づいて、一定期間(30秒)ごとに睡眠段階を設定し、設定した睡眠段階を該当する区間の生体振動データにラベリングする(ステップS13)。 Next, the biometric data processing unit 12 calculates feature amounts of biovibration data at regular intervals (step S12). Then, the sleep stage determination unit 13 sets the sleep stage at regular intervals (30 seconds) based on the feature amount calculated by the biological data processing unit 12, and converts the set sleep stage into the biological vibration data of the corresponding section. Label (step S13).
 次に、SAS判定部14は、睡眠段階判定部13で覚醒(WAKE)以外と判定した区間の生体振動データについての周波数解析結果である周波数スペクトルを、生体データ処理部12から取得する(ステップS14)。なお、ここでは覚醒(WAKE)以外の区間の生体振動データを取得して、以降の処理を行う例を説明するが、本例の処理を行う上では、取得する生体振動データとして、覚醒(WAKE)と判定した区間の生体振動データを取得する場合と、覚醒以外と判定した区間の生体振動データを取得する場合と、覚醒と判定した区間と覚醒以外と判定した区間の双方の生体振動データを取得する場合の3つケースがある。
 SAS判定部14は、取得した覚醒(WAKE)以外の区間の全ての周波数スペクトルの平均を算出し、さらにその周波数スペクトルの平均について、対数計算(例えばlog2の計算)を行って、対数値(log演算値)を得る(ステップS15)。
Next, the SAS determination unit 14 acquires, from the biological data processing unit 12, the frequency spectrum, which is the frequency analysis result of the biological vibration data in the interval determined to be other than wakefulness (WAKE) by the sleep stage determination unit 13 (step S14 ). Here, an example will be described in which biological vibration data in a section other than wakefulness (WAKE) is acquired and the subsequent processing is performed. ), when acquiring biovibration data in the interval determined as other than arousal, and in both the interval determined as arousal and the interval determined as other than arousal. There are three cases for acquisition.
The SAS determination unit 14 calculates the average of all frequency spectra in the interval other than the acquired wakefulness (WAKE), and further performs logarithmic calculation (for example, calculation of log2) on the average of the frequency spectrum to obtain a logarithmic value (log calculated value) is obtained (step S15).
 そして、SAS判定部14は、周波数スペクトルの平均の対数値の変化の近似曲線を算出して、近似曲線を取得する処理(近似曲線取得処理)を行う(ステップS16)。SAS判定部14は、近似曲線を算出する手法として、例えば最小二乗法を採用する。なお、この近似曲線を算出する際には、最も低い周波数などの一部の値を除外して算出してもよい。 Then, the SAS determination unit 14 calculates an approximated curve of changes in the average logarithmic value of the frequency spectrum, and performs a process of acquiring the approximated curve (approximate curve acquisition process) (step S16). The SAS determination unit 14 employs, for example, the method of least squares as a method of calculating the approximate curve. When calculating this approximated curve, it may be calculated by excluding some values such as the lowest frequency.
 次に、SAS判定部14は、算出した近似曲線と、周波数スペクトルの平均の対数値とを比較して、近似曲線よりも平均の対数値の方が上回っている箇所の、近似曲線よりも上回った箇所の大きさを算出する(ステップS17)。本例の場合には、近似曲線よりも上回った箇所の大きさの算出方法としては、次の図7で説明するグラフ上での近似曲線よりも平均の対数値の方が大きい箇所の面積を算出する方法を採用している。 Next, the SAS determination unit 14 compares the calculated approximated curve with the average logarithm value of the frequency spectrum, and determines that the average logarithm value exceeds the approximated curve. Then, the size of the portion where the image is drawn is calculated (step S17). In the case of this example, as a method of calculating the size of the portion exceeding the approximate curve, the area of the portion where the average logarithm value is larger than the approximate curve on the graph described in FIG. 7 is calculated. A calculation method is adopted.
 そして、SAS判定部14は、ステップS17で算出した大きさ(面積)と、予め用意された判定用の閾値を比較することにより、SASであるか否かを判定する(ステップS18)。すなわち、ステップS17で算出した面積が閾値以上であるときSASであると判定し、ステップS17で算出した面積が閾値未満であるときSASでないと判定する。
判定結果は、出力部15から出力される。
Then, the SAS determination unit 14 compares the size (area) calculated in step S17 with a threshold value for determination prepared in advance to determine whether or not it is SAS (step S18). That is, when the area calculated in step S17 is equal to or larger than the threshold value, it is determined to be SAS, and when the area calculated in step S17 is less than the threshold value, it is determined not to be SAS.
A determination result is output from the output unit 15 .
 図7は、健常者の1回の睡眠で取得した周波数スペクトルと、その周波数スペクトルを処理したデータを示す。また、図8は、SAS患者の1回の睡眠で取得した周波数スペクトルと、その周波数スペクトルを処理したデータを示す。
 図7および図8において、図7A及び図8Aは、1回の睡眠で取得した周波数スペクトルの全体の周波数に対する寄与度を示す。図7B及び図8Bは、覚醒の区間(WAKE)の全ての周波数スペクトルの平均を示す。図7C及び図8Cは、覚醒以外の区間(NONーWAKE)の全ての周波数スペクトルの平均を示す。さらに、図7D及び図8Dは、図7B及び図8Bの覚醒の区間の全ての周波数スペクトルの平均から、対数演算を行った値を示し、図7E及び図8Eは、図7C及び図8Cの覚醒以外の区間の全ての周波数スペクトルの平均から、対数演算を行った値を示す。
 図7A及び図8Aは、縦軸を周波数、横軸を寄与度で示し、図7B~図7Eと図8B~図8Eは、それぞれ横軸を周波数、縦軸を密度で示している。
FIG. 7 shows a frequency spectrum acquired in one sleep of a healthy subject and data obtained by processing the frequency spectrum. Moreover, FIG. 8 shows a frequency spectrum acquired in one sleep of an SAS patient and data obtained by processing the frequency spectrum.
7 and 8, FIGS. 7A and 8A show the contribution to the overall frequency of the frequency spectrum acquired in one sleep. Figures 7B and 8B show the average of all frequency spectra for the wake interval (WAKE). FIGS. 7C and 8C show averages of all frequency spectra in the non-wake interval (NON-WAKE). Furthermore, FIGS. 7D and 8D show logarithmically calculated values from the average of all frequency spectra of the arousal interval of FIGS. 7B and 8B, and FIGS. Shows the value obtained by logarithmically calculating the average of all the frequency spectra in the interval other than .
7A and 8A show the frequency on the vertical axis and the degree of contribution on the horizontal axis, and FIGS. 7B to 7E and 8B to 8E show the frequency on the horizontal axis and the density on the vertical axis, respectively.
 図7Aと、図8Aとを比較すると分かるように、健常者の周波数スペクトルの分布とSAS患者の周波数スペクトルの寄与度には、それなりに相違があるが、各図の平均を示す図7Bと図8Bや、図7Cと図8Cでは、健常者とSAS患者とに見かけ上、明確な差は現れていない。
 一方、周波数スペクトルの平均から対数値を求める演算を行った値を示す図7Dと図8Dや、図7Eと図8Eでは、健常者とSAS患者とで、3Hz付近の周波数で、比較的大きな相違が現れている。すなわち、図7Dと図8Dに示す覚醒の区間(WAKE)の平均の対数値と、図7Eと図8Eに示す覚醒以外の区間(NONーWAKE)の平均の対数値のいずれについても、3Hz付近の周波数で、図8のSAS患者の方が、図7に示す健常者よりも平均の対数値の値が高くなっている。特に、覚醒以外の区間(NONーWAKE)の平均の対数値の方が、より顕著にSAS患者の値が高いことが示されている。
 本例のSAS判定部14は、この平均の対数値の健常者とSAS患者との相違から、SAS患者であることを判定している。
As can be seen by comparing FIG. 7A and FIG. 8A, there are some differences in the distribution of the frequency spectrum of the healthy subject and the contribution of the frequency spectrum of the SAS patient, but the average of each figure is shown in FIG. In 8B and in FIGS. 7C and 8C, no apparent clear difference appears between the healthy subject and the SAS patient.
On the other hand, in FIGS. 7D and 8D, and in FIGS. 7E and 8E, which show the values obtained by calculating the logarithmic value from the average of the frequency spectrum, there is a relatively large difference at frequencies around 3 Hz between healthy subjects and SAS patients. is appearing. That is, both the average logarithmic value of the arousal interval (WAKE) shown in FIGS. 7D and 8D and the average logarithmic value of the non-awakening interval (NON-WAKE) shown in FIGS. 8, the average logarithmic values of the SAS patients shown in FIG. 8 are higher than those of the healthy subjects shown in FIG. In particular, it is shown that the average logarithmic value of the non-awakening interval (NON-WAKE) is significantly higher in SAS patients.
The SAS determination unit 14 of this example determines that the patient is an SAS patient from the difference in the average logarithmic value between the healthy subject and the SAS patient.
 ここで、3Hz付近の周波数からSAS患者であることを判定できる点をより詳しく説明する。
 体の表面上では微細振動と呼ばれる振動現象が観察され、眠くなった低い覚醒水準時に3~4Hzの成分が強くなる。この結果から、SAS患者は頻繁に覚醒-睡眠を繰り返し、覚醒から睡眠に遷移するときに低い覚醒水準に陥るため、一晩分の対数スペクトルの平均をとると、SAS患者の場合は3Hzの周波数成分が強く出ると想定される。
 一方、健常者の場合は、頻繁に覚醒-睡眠を繰り返えさないため、一晩分の対数スペクトルの平均をとると、全体的には3Hzの周波数成分が現れるのが少ないと想定される。したがって、3Hz付近の周波数からSAS患者であることを適切に判定することができる。
Here, the fact that the SAS patient can be determined from the frequency around 3 Hz will be described in more detail.
A vibration phenomenon called microvibration is observed on the surface of the body, and the component of 3 to 4 Hz becomes stronger at a low wakefulness level when one becomes sleepy. From this result, SAS patients have frequent wake-sleep cycles and fall into a low wakefulness level when transitioning from wakefulness to sleep. It is assumed that the component will come out strongly.
On the other hand, in the case of a healthy subject, since wakefulness and sleep are not repeated frequently, it is assumed that the frequency component of 3 Hz appears less as a whole when the logarithmic spectrum for one night is averaged. Therefore, it is possible to appropriately determine that the patient is an SAS patient from the frequency around 3 Hz.
 次に、図9を参照して、図6のフローチャートのステップS16で近似曲線を求める例を説明する。
 図9は、SAS患者の覚醒以外の区間(NON-WAKE)の平均の対数値NW1に対して、最小二乗法で近似曲線C1を求めた例を示す。
 なお、SAS判定部14は、近似曲線C1を求める際に、平均の対数値NW1の内で、最も低い周波数から数点(ここでは2点)の値(図9の範囲xの値)を除外して、最小二乗法で算出を行うようにしている。
 このようにして求まる近似曲線は、どの被測定者のデータであっても、低い周波数が最も高い値となり、周波数が高くなるごとに緩い曲線を描いて徐々に低い値に低下している。
Next, an example of obtaining an approximate curve in step S16 of the flow chart of FIG. 6 will be described with reference to FIG.
FIG. 9 shows an example of an approximation curve C1 obtained by the least-squares method for the average logarithmic value NW1 of the non-awakening interval (NON-WAKE) of the SAS patient.
Note that when obtaining the approximated curve C1, the SAS determination unit 14 excludes several (here, two) values from the lowest frequency in the average logarithmic value NW1 (the values in the range x in FIG. 9). Then, calculation is performed by the method of least squares.
The approximation curve obtained in this manner has the highest value at a low frequency and gradually decreases to a lower value as the frequency increases, drawing a gentle curve.
 そして、SAS判定部14は、近似曲線C1と、平均の対数値NW1とを比較する。図9に示すSAS患者の場合には、3Hzの近傍の周波数帯で、近似曲線C1よりも対数値NW1の方が比較的大きい状態が連続する区間がある。
 このため、グラフ上で近似曲線C1よりも対数値NW1の方が大きくなる領域の面積の値Supperは、比較的大きな値になる。なお、近似曲線C1は、基本的には平均の対数値NW1の状態を反映(近似)しているため、近似曲線C1よりも対数値NW1の方が小さくなる領域の面積の値Sunderと、近似曲線C1よりも対数値NW1の方が大きくなる領域の面積の値Supperとの差分は小さい。
 したがって、近似曲線C1よりも対数値NW1の方が小さくなる領域の面積の値Sunderも、近似曲線C1よりも対数値NW1の方が大きくなる領域の面積の値Supperに対応して大きな値になる。
The SAS determination unit 14 then compares the approximated curve C1 with the average logarithmic value NW1. In the case of the SAS patient shown in FIG. 9, in the frequency band near 3 Hz, there is a continuous interval in which the logarithmic value NW1 is relatively larger than the approximate curve C1.
Therefore, the area value Supper of the region in which the logarithmic value NW1 is larger than the approximate curve C1 on the graph becomes a relatively large value. Since the approximated curve C1 basically reflects (approximates) the state of the average logarithmic value NW1, the value Sunder of the area of the region where the logarithmic value NW1 is smaller than that of the approximated curve C1 and the approximation The difference from the area value Supper of the region where the logarithmic value NW1 is larger than the curve C1 is small.
Therefore, the area value Sunder of the region where the logarithmic value NW1 is smaller than the approximate curve C1 also becomes a large value corresponding to the area value Super of the region where the logarithmic value NW1 is larger than the approximate curve C1. .
 ここで、本例のSAS判定部14は、図9に示す近似曲線C1と対数値NW1とを比較するとわかるように、3Hz付近の周波数で近似曲線C1よりも対数値NWの方が大きくなる箇所が、単峰性を有する形状であるとき、SAS患者と判定することができる。一方、3Hz付近の周波数で近似曲線C1よりも対数値NWの方が大きくなる箇所が存在しても、単峰性を有していない場合には、SAS患者でないと判定する。近似曲線C1よりも対数値NWの方が大きくなる単峰性を有した箇所の大きさに応じて、つまり正の方向に外れる量の大きさに基づいて、被測定者がSAS患者であるかの判定を行うようにしてもよい。単峰性を有した箇所が正の方向に外れる量の大きさが設定した閾値以上のとき、SAS患者であると判定し、設定した閾値よりも小さいとき、SAS患者でないと判定する。これによって、SAS患者と判定する精度を上げることができる。 Here, as can be seen by comparing the approximated curve C1 and the logarithmic value NW1 shown in FIG. is a unimodal shape, it can be determined as an SAS patient. On the other hand, even if there is a portion where the logarithmic value NW is larger than the approximation curve C1 at a frequency around 3 Hz, if it does not have a single peak, it is determined that the patient is not an SAS patient. Depending on the size of the unimodal portion where the logarithmic value NW is larger than the approximate curve C1, that is, based on the size of the amount deviating in the positive direction, whether the subject is an SAS patient may be determined. When the magnitude of the deviation of the unimodal portion in the positive direction is greater than or equal to a set threshold value, the patient is determined to be an SAS patient, and when it is smaller than the set threshold value, the patient is determined not to be an SAS patient. As a result, it is possible to improve the accuracy of determination as an SAS patient.
 なお、ここでは3Hz付近の周波数で近似曲線C1よりも対数値NWの方が大きくなる箇所が、単峰性を有する形状であるとき、SAS患者と判定したが、3Hz付近の周波数から検出するのは一例であり、3Hzから外れた周波数であっても、対数値NWの方が大きくなる箇所が、単峰性を有する形状であるとき、SAS患者と判定してもよい。但し、3Hz付近の周波数で検出した場合の方が、判定精度がより高くなる。 It should be noted that here, when the portion where the logarithmic value NW is larger than the approximated curve C1 at a frequency around 3 Hz has a unimodal shape, it is determined to be an SAS patient, but it is detected from the frequency around 3 Hz. is an example, and even if the frequency deviates from 3 Hz, if the location where the logarithmic value NW is larger has a unimodal shape, it may be determined as an SAS patient. However, detection accuracy is higher when detection is performed at a frequency near 3 Hz.
 図9は、一人のSAS患者の例を示すが、図10および図11に、複数人(五人)の健常者およびSAS患者の例を示す。
 図10A、図10B、図10C、図10D、図10Eは、5人の健常者の覚醒以外の区間(NON-WAKE)の平均の対数値NW11~NW15と、その近似曲線C11~C15を示す。
 図11A、図11B、図11C、図11D、図11Eは、5人のSAS患者の覚醒以外の区間(NON-WAKE)の平均の対数値NW21~NW25と、その近似曲線C11~C15を示す。
FIG. 9 shows an example of a single SAS patient, while FIGS. 10 and 11 show examples of multiple (five) healthy subjects and SAS patients.
FIGS. 10A, 10B, 10C, 10D, and 10E show the average logarithmic values NW11 to NW15 of the non-awakening interval (NON-WAKE) of five healthy subjects and their approximate curves C11 to C15.
FIGS. 11A, 11B, 11C, 11D, and 11E show average logarithmic values NW21 to NW25 of non-awakening intervals (NON-WAKE) of five SAS patients and their approximate curves C11 to C15.
 図10A~図10Eの5人の健常者の対数値NW11~NW15と、その近似曲線C11~C15とから分かるように、健常者の場合には、いずれの例でも対数値NW11~NW15が近似曲線C11~C15から大きく外れる周波数がほとんどない。
 一方、図11A~図11Eの5人のSAS患者の対数値NW21~NW25と、その近似曲線C21~C25とから分かるように、SAS患者の場合には、3Hz近傍の周波数帯で、対数値NW21~NW25の方が近似曲線C21~C25よりも大きくなる領域が発生している。また、1Hzから2Hz程度の周波数帯では、対数値NW21~NW25の方が近似曲線C21~C25よりも小さくなる領域が発生している。しかも、いずれのSAS患者の対数値NW21~NW25についても、近似曲線C21~C25より大きくなる箇所が単峰性を有する形状である。
As can be seen from the logarithmic values NW11 to NW15 of five healthy subjects and their approximate curves C11 to C15 in FIGS. There are almost no frequencies greatly deviating from C11 to C15.
On the other hand, as can be seen from the logarithmic values NW21 to NW25 of five SAS patients in FIGS. ˜NW25 has a larger area than the approximation curves C21 to C25. Also, in the frequency band of about 1 Hz to 2 Hz, there is a region where the logarithmic values NW21 to NW25 are smaller than the approximation curves C21 to C25. Moreover, the logarithmic values NW21 to NW25 of any of the SAS patients have a unimodal shape where they are larger than the approximation curves C21 to C25.
 ここでは5人の健常者と5人のSAS患者のみを比較した例を示すが、本願の発明者が実験した結果では、ほとんどの被測定者について、健常者の場合とSAS患者の場合とでほぼ同様な傾向を示している。 Here, an example of comparing only 5 healthy subjects and 5 SAS patients is shown, but the results of experiments conducted by the inventors of the present application show that for most subjects, healthy subjects and SAS patients Almost the same tendency is shown.
 図12は、18人の被測定者a~rの1回の睡眠中の振動データから得た対数値とその近似曲線から求めた対数値の方が近似曲線よりも大きくなる領域の面積の値Supperと、対数値の方が近似曲線よりも小さくなる領域の面積の値Sunderとを示す。図12の縦軸が面積の値を示す。各測定者a~rの棒グラフの内で、それぞれ左側が値Supper、右側が値Sunderである。 FIG. 12 shows the logarithmic value obtained from the vibration data during one sleep of 18 subjects a to r and the area value of the area where the logarithmic value obtained from the approximate curve is larger than the approximate curve. Supper and the value Sunder of the area of the region whose logarithmic value is smaller than the approximate curve. The vertical axis in FIG. 12 indicates the area value. In the bar graphs of each measurer a to r, the left side is the value Supper and the right side is the value Sunder.
 18人の被測定者a~rの内で、9人の被測定者a~iはSAS患者の測定値であり、残りの9人の被測定者j~rは健常者の測定値である。値Supper、値Sunderのいずれについても、健常者j~rの測定値とSAS患者a~iの測定値とは大きく離れている。 Of the 18 subjects a to r, 9 subjects a to i are measurements of SAS patients, and the remaining 9 subjects j to r are measurements of healthy subjects. . Regarding both the value Supper and the value Sunder, the measurement values of the healthy subjects j to r and the measurement values of the SAS patients a to i are greatly different.
 したがって、SAS判定部14は、図12に示すように、健常者j~rの測定値とSAS患者a~iの測定値との間に判定用の閾値TH1を設定し、値Supperまたは値Sunderと閾値TH1との比較を行っている。これにより、SAS判定部14は、閾値TH1以上であるときSAS患者と判定し、閾値TH1未満であるときSAS患者でないと判定することで、睡眠時無呼吸症候群を患っているか否かを正確に診断することができる。 Therefore, as shown in FIG. 12, the SAS determination unit 14 sets a threshold value TH1 for determination between the measured values of the healthy subjects j to r and the measured values of the SAS patients a to i, and the value Supper or the value Sunder is compared with the threshold value TH1. As a result, the SAS determination unit 14 determines that the patient is an SAS patient when the threshold TH is equal to or greater than TH1, and determines that the patient is not an SAS patient when the threshold TH is less than TH1, thereby accurately determining whether the patient suffers from sleep apnea syndrome. can be diagnosed.
 以上説明したように、本例の睡眠時無呼吸症候群判定装置10によると、被測定者の睡眠中の体動または圧力変化に基づいた生体振動データから、精度の高い睡眠時無呼吸症候群の患者の判別が可能になる。しかも、本例の睡眠時無呼吸症候群判定装置10が判定するデータは、マットレスセンサ2などで測定ができる生体振動データであり、マットレス型などの圧力センサを敷いた状態で睡眠するだけで、簡単に精度の高い睡眠時無呼吸症候群の判定を行うことが可能になる。
 このため、本例の睡眠時無呼吸症候群判定装置10では、被測定者の体に電極などを取り付けて判定する従来手法に比べて、被測定者の負担が極めて少ないという作用効果を奏することができる。
As described above, according to the sleep apnea syndrome determination device 10 of the present example, from the biological vibration data based on the body movement or pressure change during sleep of the subject, the patient with sleep apnea syndrome with high accuracy can be determined. Moreover, the data determined by the sleep apnea syndrome determination device 10 of this example is biological vibration data that can be measured by the mattress sensor 2 or the like. It is possible to determine sleep apnea syndrome with high accuracy.
Therefore, the apparatus 10 for determining sleep apnea syndrome according to the present embodiment can produce an effect that the burden on the person to be measured is extremely small compared to the conventional method of making determination by attaching electrodes or the like to the body of the person to be measured. can.
 なお、本例の睡眠時無呼吸症候群判定装置で判定を行った場合、少ない比率ではあるが、SAS患者でない健常者を、SAS患者と誤って判定することがある。しかしながら、誤って判定した健常者について、健康状態を詳細に見たとき、いずれの健常者も体重が平均より多いなどの、SAS患者に近い特徴を持っている。したがって、SAS患者に近い傾向を持った者をスクリーニングした可能性を示しており、SASの兆候を早期に発見できることもできる。 When the sleep apnea syndrome determination device of this example is used for determination, healthy subjects who are not SAS patients may be erroneously determined to be SAS patients, albeit in a small percentage. However, when the health conditions of the erroneously determined healthy subjects are examined in detail, all of the healthy subjects have characteristics similar to those of SAS patients, such as being heavier than the average. Therefore, it indicates the possibility of screening those who have a tendency similar to SAS patients, and it is also possible to detect signs of SAS at an early stage.
[5.変形例]
 なお、上述した実施の形態例で説明した処理は、好適な一例を示したものであり、実施の形態例で説明したものに限定されるものではない。
 例えば、SAS判定部14では、平均の対数値と近似曲線とを比較して、平均の対数値が近似曲線よりも大きくなるか、あるいは小さくなる面積を算出して、その面積を閾値と比較するようにした。これに対して、図9や図11で分かるように、SAS判定部14は、SAS患者に特有の特徴である3Hzの近傍で、平均の対数値が近似曲線から大きく外れることの有無を判定してもよい。
 また、上述した実施の形態例では、最小二乗法により近似曲線を算出する処理を行ったが、最小二乗法に代えてその他の演算手法で同様の近似曲線を算出してもよい。
[5. Modification]
It should be noted that the processing described in the above-described embodiment is a preferred example, and the processing is not limited to that described in the embodiment.
For example, the SAS determination unit 14 compares the average logarithmic value with the approximated curve, calculates the area where the average logarithmic value is larger or smaller than the approximated curve, and compares the area with the threshold value. I made it On the other hand, as can be seen from FIGS. 9 and 11, the SAS determination unit 14 determines whether or not the average logarithmic value greatly deviates from the approximate curve in the vicinity of 3 Hz, which is a characteristic feature of SAS patients. may
In addition, in the above-described embodiment, the approximated curve is calculated by the method of least squares, but similar approximated curves may be calculated by other calculation methods instead of the method of least squares.
 また、上述した実施の形態例では、被測定者の睡眠中の心拍、呼吸、及び体動による生体振動データを取得する生体データ取得部として、マットレスセンサからの生体振動データを取得するようした。これに対して、その他のセンサで同様に被測定者の睡眠中の心拍、呼吸、及び体動による生体振動データを取得可能であれば他のセンサを使用してもよい。 In addition, in the above-described embodiment, the biological vibration data is obtained from the mattress sensor as the biological data acquisition unit that acquires the biological vibration data due to the heartbeat, respiration, and body movement of the subject during sleep. On the other hand, other sensors may be used as long as they can similarly acquire biological vibration data due to heartbeat, respiration, and body movement of the subject during sleep.
 1…ベッド、2…マットレスセンサ、10…睡眠時無呼吸症候群判定装置、11…生体データ取得部、12…生体データ処理部、13…睡眠段階判定部、14…睡眠時無呼吸症候群判定部(SAS判定部)、15…出力部、A…被測定者、C…コンピュータ装置、C1…CPU、C2…ROM、C3…RAM、C4…不揮発性ストレージ、C5…ネットワークインターフェイス表示部、C6…入力装置、C7…表示装置、C8…バス 1 ... bed, 2 ... mattress sensor, 10 ... sleep apnea syndrome determination device, 11 ... biological data acquisition unit, 12 ... biological data processing unit, 13 ... sleep stage determination unit, 14 ... sleep apnea syndrome determination unit ( SAS determination unit), 15... output unit, A... subject, C... computer device, C1... CPU, C2... ROM, C3... RAM, C4... non-volatile storage, C5... network interface display unit, C6... input device , C7... display device, C8... bus

Claims (9)

  1.  被測定者の睡眠中の心拍、呼吸、及び体動による生体振動データを取得する生体データ取得部と、
     前記生体データ取得部が取得した前記生体振動データを周波数解析して、睡眠中の覚醒と判定された箇所の周波数スペクトルの平均、覚醒以外と判定された箇所の周波数スペクトルの平均、及び覚醒と判定された箇所とそれ以外と判定された箇所の双方の周波数スペクトルの平均のいずれかを取得する生体データ処理部と、
     前記生体データ取得部で得られた周波数スペクトルの平均の対数値の単峰性に基づいて、前記被測定者が睡眠時無呼吸症候群であるかの判定を行う睡眠時無呼吸症候群判定部と、を備える
     睡眠時無呼吸症候群判定装置。
    a biometric data acquisition unit that acquires biovibration data due to heartbeat, respiration, and body movement of the subject during sleep;
    The biological vibration data obtained by the biological data obtaining unit is frequency-analyzed, and the average of the frequency spectrum of the location determined to be awake during sleep, the average of the frequency spectrum of the location determined to be other than wakefulness, and the determination of wakefulness a biological data processing unit that acquires either the average of the frequency spectrum of both the location where the
    a sleep apnea syndrome determination unit that determines whether the subject has sleep apnea syndrome based on the unimodality of the average logarithmic value of the frequency spectrum obtained by the biological data acquisition unit; A device for determining sleep apnea syndrome.
  2.  前記睡眠時無呼吸症候群判定部は、前記周波数スペクトルの平均の対数値に対する近似曲線を算出し、前記周波数スペクトルの平均の対数値と、前記近似曲線とを比較することで前記周波数スペクトルの平均の対数値の単峰性を判断する
     請求項1に記載の睡眠時無呼吸症候群判定装置。
    The sleep apnea syndrome determination unit calculates an approximated curve for the average logarithm of the frequency spectrum, and compares the average logarithm of the frequency spectrum with the approximated curve to determine the average of the frequency spectrum. The device for determining sleep apnea syndrome according to claim 1, which determines unimodality of logarithmic values.
  3.  近似曲線の情報を入力する入力部を更に備え、
     前記生体データ処理部は、前記入力部より入力された前記近似曲線の情報に基づいて前記近似曲線を算出する
     請求項2に記載の睡眠時無呼吸症候群判定装置。
    Further comprising an input unit for inputting information on the approximate curve,
    The sleep apnea syndrome determination device according to claim 2, wherein the biological data processing unit calculates the approximate curve based on the information of the approximate curve input from the input unit.
  4.  前記近似曲線を算出する際には、前記生体振動データに含まれる最も低い周波数の成分を除外して取得する
     請求項2に記載の睡眠時無呼吸症候群判定装置。
    The apparatus for determining sleep apnea syndrome according to claim 2, wherein the lowest frequency component included in the biological vibration data is excluded when calculating the approximate curve.
  5.  さらに、前記生体振動データを周波数解析した結果に基づいて、一定期間ごとに被測定者の睡眠段階を判定する睡眠段階判定部を備え、
     前記睡眠時無呼吸症候群判定部は、前記睡眠段階判定部が判定した睡眠段階が、覚醒以外の睡眠段階の区間、覚醒の睡眠段階の区間、並びに覚醒以外及び覚醒の双方の睡眠段階の区間のいずれかについて、周波数ごとの平均の対数値を取得して判定する
     請求項1または2に記載の睡眠時無呼吸症候群判定装置。
    Further, a sleep stage determination unit that determines the sleep stage of the subject at regular intervals based on the results of frequency analysis of the biological vibration data,
    The sleep apnea syndrome determination unit determines that the sleep stage determined by the sleep stage determination unit is a sleep stage interval other than arousal, an arousal sleep stage interval, and both non-awakening and awake sleep stage intervals. The apparatus for determining sleep apnea syndrome according to claim 1 or 2, wherein determination is made by obtaining an average logarithmic value for each frequency.
  6.  前記睡眠時無呼吸症候群判定部は、前記生体データ取得部で得られた周波数スペクトルの平均の対数値に対する近似曲線を算出すると共に、前記生体データ取得部で得られた周波数スペクトルの平均の対数値が、算出した近似曲線から正の方向または負の方向に外れる量を検出し、検出した近似曲線から正の方向または負の方向に外れた量の大きさに基づいて、前記被測定者が睡眠時無呼吸症候群であるかの判定を行う
     請求項1に記載の睡眠時無呼吸症候群判定装置。
    The sleep apnea syndrome determination unit calculates an approximate curve for the average logarithm of the frequency spectrum obtained by the biological data acquisition unit, and calculates the average logarithm of the frequency spectrum obtained by the biological data acquisition unit. detects the amount of deviation from the calculated approximate curve in the positive direction or the negative direction, and based on the magnitude of the amount of deviation from the detected approximate curve in the positive direction or the negative direction, the subject sleeps The device for determining sleep apnea syndrome according to claim 1, which determines whether or not there is an occasional apnea syndrome.
  7.  前記睡眠時無呼吸症候群判定部は、3Hzの付近の平均の対数値が、前記近似曲線から正の方向に外れる量の大きさに基づいて、前記被測定者が睡眠時無呼吸症候群であるかの判定を行う
     請求項6に記載の睡眠時無呼吸症候群判定装置。
    The sleep apnea syndrome determination unit determines whether the subject has sleep apnea syndrome based on the magnitude of the deviation of the average logarithmic value around 3 Hz from the approximate curve in the positive direction. The apparatus for determining sleep apnea syndrome according to claim 6.
  8.  被測定者の睡眠中の心拍、呼吸、及び体動による生体振動データを取得する生体データ取得処理と、
     前記生体データ取得処理により取得した前記生体振動データを周波数解析して、睡眠中の覚醒と判定された箇所の周波数スペクトルの平均、覚醒以外と判定された箇所の周波数スペクトルの平均、及び覚醒と判定された箇所とそれ以外と判定された箇所の双方の周波数スペクトルの平均のいずれかを取得する生体データ処理と、
     前記生体データ処理により得られた周波数スペクトルの平均の対数値に対する近似曲線を取得する近似曲線取得処理と、
     前記生体データ処理により得られた周波数スペクトルの平均の対数値の単峰性に基づいて、前記被測定者が睡眠時無呼吸症候群であるかの判定を行う判定処理と、を含む
     睡眠時無呼吸症候群判定方法。
    biometric data acquisition processing for acquiring biovibration data due to heartbeat, respiration, and body movement of the subject during sleep;
    Frequency analysis of the biological vibration data acquired by the biological data acquisition process, the average of the frequency spectrum of the location determined to be awake during sleep, the average of the frequency spectrum of the location determined to be other than wakefulness, and determination of wakefulness biometric data processing to acquire either the average of the frequency spectrum of both the location where it was determined and the location determined as other;
    an approximated curve acquisition process for acquiring an approximated curve for the average logarithm of the frequency spectrum obtained by the biological data processing;
    a determination process for determining whether the subject has sleep apnea syndrome based on the unimodality of the average logarithmic value of the frequency spectrum obtained by the biological data processing. Syndrome determination method.
  9.  被測定者の睡眠中の心拍、呼吸、及び体動による生体振動データを取得する生体データ取得手順と、
     前記生体データ取得手順により取得した前記生体振動データを周波数解析して、睡眠中の覚醒と判定された箇所の周波数スペクトルの平均、覚醒以外と判定された箇所の周波数スペクトルの平均、及び覚醒と判定された箇所とそれ以外と判定された箇所の双方の周波数スペクトルの平均のいずれかを取得する生体データ処理手順と、
     前記生体データ処理手順により得られた周波数スペクトルの平均の対数値に対する近似曲線を取得する近似曲線取得手順と、
     前記生体データ処理手順により得られた周波数スペクトルの平均の対数値の単峰性に基づいて、前記被測定者が睡眠時無呼吸症候群であるかの判定を行う判定手順と、
    をコンピュータに実行させるプログラム。
    a biometric data acquisition procedure for acquiring biovibration data due to heartbeat, respiration, and body movement of the subject during sleep;
    Frequency analysis of the biological vibration data acquired by the biological data acquisition procedure, the average of the frequency spectrum of the location determined to be awake during sleep, the average of the frequency spectrum of the location determined to be other than wakefulness, and determination of wakefulness a biological data processing procedure for obtaining either the average of the frequency spectrum of both the location where the
    an approximated curve acquisition procedure for acquiring an approximated curve for the average logarithm of the frequency spectrum obtained by the biological data processing procedure;
    a determination procedure for determining whether the subject has sleep apnea syndrome based on the unimodality of the average logarithmic value of the frequency spectrum obtained by the biological data processing procedure;
    A program that makes a computer run
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