JP4551148B2 - Sleep analyzer - Google Patents

Description

  The present invention relates to a sleep analyzer that detects human biological information and analyzes a sleep state from the biological information.

Conventionally, as a means of determining a person's sleep stage, the result of a sleep polysomnograph test (hereinafter abbreviated as PSG) has been performed based on the sleep stage international determination standard by Rechtschaffen & Kales (for example, non-patented) Reference 1). In this method, EEG analysis, presence / absence of rapid eye movement, persistence and phasic factors of mental electromyogram are evaluated based on signals obtained from EEG sensors, eyeball sensors, mental muscle sensors, etc. It was carried out by. The sleep stages of humans are classified into a total of 6 stages including wakefulness, REM sleep is classified as REM sleep, non-REM sleep is classified as non-REM sleep, and the brain wave status is as follows. Into 4 stages.
Awakening (stage W): 8-12 Hz alpha waves and 13-40 Hz low amplitude irregular beta waves.
-REM sleep stage: Rapid eye movement (REM) occurs when awake. In addition, anti-gravity muscles such as the gluteal muscles are significantly reduced in tension.
・ Non-REM first stage: Alpha wave disappears and low-amplitude slow wave of 2-7Hz appears. A characteristic wave called a vertex sharp wave appears in the center. Slow eye movements (sem) are observed in a state of half awakening and asleep, or a state of sleep onset.
Non-REM second stage: A clear sleep spindle wave with a duration of 0.5 second or longer at 12-14 Hz appears.
Non-REM third and fourth stages: A delta wave of 75 μV or more appears at 2 Hz or less. 50% or more is classified into the non-rem fourth stage and 20 to 50% is classified into the non-rem third stage according to the ratio of the delta wave in the determination section.

  FIG. 27 shows an example of a conventional sleep analyzer that determines the sleep stage as described above. In FIG. 27, the electroencephalogram electrode 101, the muscular muscle electrode 102, and the eye movement electrode 103 are sensors that are arranged around the brain, jaw, and eyes of the human body, respectively, and capture weak voltage changes. The sensor interfaces 104a, 104b, and 104c have a function of amplifying the electroencephalogram signal, the muscular myoelectric signal, and the eye movement signal to appropriate levels, and the A / D converters 105a, 105b, and 105c are arithmetic devices such as a microcomputer. It functions as a signal interface to 106.

FIG. 28 shows rules for determining the final sleep stage by calculating each signal such as an electroencephalogram signal, mental myoelectric signal, and eye movement signal obtained in FIG. This is based on the international criteria for sleep stages by Rechtschaffen & Kales.
Internet <URL: http: //www.sleep-ukiha.syslabo.co.jp/cont071.html>

  In the conventional sleep polysomnograph test, brain waves, eye movements, and genital muscles are used as input information as criteria for determining a person's sleep state, so it is necessary to attach an electroencephalogram sensor, eye sensor, and geniomuscular sensor directly to the human body. There are problems in that the sensor falls off due to the subject's rollover or body movement and does not function, or the continuity of the measurement data is questioned due to changes in sensor mounting conditions.

  Furthermore, since the sensor is directly attached to the human body, it is forced to have a sleep posture different from normal, the psychological tension state and the stress state continue, etc. there were.

  EEG analysis etc. cannot be judged without specialist knowledge like doctors, which means that sleep analysis is actually performed at a time much later than the time of sleep polysomnograph examination. Therefore, there is a problem that it cannot be used for the purpose of capturing the sleep state and controlling the device and the environment immediately and in real time.

  In view of the above-described problems, an object of the present invention is to provide a sleep analyzer that can analyze a sleep state without directly attaching a sensor to a subject.

  In order to solve the above-described problems, the present invention provides a sensor that detects biological information of a subject lying on an air mat, and a biological body that separates a respiratory circulatory system biological signal and a body motion signal from the sensor detection signal. A sleep state determination unit that determines a sleep stage from the signal separation unit, the separated biological signal of the respiratory circulatory system, and the body motion signal. The determination is made based on the circulatory system biological signal, and the distinction between the arousal stage and the REM sleep stage is performed based on the fluctuation rate of the body movement signal or the biological signal of the respiratory circulatory system.

  Preferably, the sleep state determination means performs frequency analysis on the biological signal of the respiratory circulatory system and compares the frequency fluctuation band of the biological signal of the respiratory circulatory system with a threshold value to determine sleep at the non-REM stage. It is characterized by that.

In order to solve the above-described problems, the present invention provides a sensor that detects biological information of a subject lying on an air mat, and a biological body that separates a respiratory circulatory system biological signal and a body motion signal from the sensor detection signal. A sleep state determination unit that determines a sleep stage from the signal separation unit, the separated biological signal of the respiratory circulatory system, and the body motion signal. The determination is made based on the biological signal of the circulatory system, and the distinction between the awakening stage and the REM sleep stage is performed based on the fluctuation rate of the body movement signal or the biological signal of the respiratory circulatory system . Further, the sleep state determination means performs amplitude analysis on the biological signal of the respiratory circulatory system, compares the amplitude dispersion value of the biological signal of the respiratory circulatory system with a threshold value, and determines sleep at a non-rem stage. To do.

The present invention also includes a sensor that detects biological information of a subject lying on the air mat, and a biological signal separation unit that separates a respiratory circulatory system biological signal and a body motion signal from the detection signal of the sensor. It has a sleep state determination means for determining a sleep stage from a biological signal of the respiratory circulatory system and a body motion signal. In the sleep state determination means, the sleep determination at the non-rem stage is determined by the biological signal of the respiratory circulatory system. The distinction between the awakening stage and the REM sleep stage is performed based on the fluctuation rate of the body motion signal or the biological signal of the respiratory circulatory system. Further, the sleep state determination means performs amplitude analysis on a respiratory circulatory system biological signal, compares the amplitude dispersion value of the respiratory circulatory system biological signal with a threshold value, and determines non-REM stage sleep. It is characterized by.

The present invention also includes a sensor that detects biological information of a subject lying on the air mat, and a biological signal separation unit that separates a respiratory circulatory system biological signal and a body motion signal from the detection signal of the sensor. It has a sleep state determination means for determining a sleep stage from a biological signal of the respiratory circulatory system and a body motion signal. In the sleep state determination means, the sleep determination at the non-rem stage is determined by the biological signal of the respiratory circulatory system. The wakefulness stage and the REM sleep stage are discriminated based on the fluctuation rate of the biological signal of the respiratory circulatory system. Further, the sleep state determination means performs a frequency analysis of the biological signal of the respiratory circulatory system, compares the fluctuation rate of the frequency fluctuation band of the biological signal of the respiratory circulatory system with a threshold value, and determines the awakening stage and the REM sleep stage. It is characterized by distinguishing.

The present invention also includes a sensor that detects biological information of a subject lying on the air mat, and a biological signal separation unit that separates a respiratory circulatory system biological signal and a body motion signal from the detection signal of the sensor. It has a sleep state determination means for determining a sleep stage from a biological signal of the respiratory circulatory system and a body motion signal. In the sleep state determination means, the sleep determination at the non-rem stage is determined by the biological signal of the respiratory circulatory system. The wakefulness stage and the REM sleep stage are discriminated based on the fluctuation rate of the biological signal of the respiratory circulatory system. Further, the sleep state determination means performs an amplitude analysis on the biological signal of the respiratory circulatory system, compares the fluctuation rate of the amplitude dispersion value of the biological signal of the respiratory circulatory system with a threshold value, and determines the awakening stage and the REM sleep stage. It is characterized by distinguishing.

  According to the present invention, a sensor for detecting biological information, a biological signal separation means for separating a biological signal of a respiratory circulatory system and a body motion signal from a detection signal of the sensor, and a separated respiratory circulatory system A sleep state determination means for determining a sleep stage from a biological signal and a body motion signal, wherein the sleep determination of the non-rem stage is determined by a biological signal of a respiratory circulatory system, The discrimination from the REM sleep stage is performed based on the fluctuation rate of the body motion signal or the biological signal of the respiratory circulatory system. For this reason, it is not necessary to attach a sensor directly to a human body. Therefore, the problem that the sensor falls off due to the subject's rollover or body movement and does not function or the continuity of the measurement data is doubted due to changes in the sensor mounting conditions does not occur. In addition, since the subject does not experience the stress that is measured by the sensor, the same psychological state sleep state as that at the time of non-measurement can be measured. This leads to improved reliability of measurement data. Furthermore, since the determination by a specialist is not required unlike the electroencephalogram analysis, the subject can be measured anywhere such as at home. Moreover, since the determination of the non-REM sleep stage and the determination of the awakening stage and the REM sleep stage are performed separately, the accuracy of the sleep state determination is improved.

  In the present invention, the sleep state determination means performs frequency analysis on the biological signal of the respiratory circulatory system, compares the frequency fluctuation band of the biological signal of the respiratory circulatory system with a threshold value, and determines sleep at the non-REM stage. ing. In the present invention, the sleep state determination means periodically analyzes the biological signal of the respiratory circulatory system, compares the periodic dispersion value of the biological signal of the respiratory circulatory system with a threshold value, and determines the sleep at the non-REM stage. I am doing so. Further, in the present invention, the sleep state determination means performs amplitude analysis on the biological signal of the respiratory circulatory system and compares the amplitude dispersion value of the biological signal of the respiratory circulatory system with a threshold value to determine non-REM stage sleep. I am doing so. For this reason, it is possible to determine the stage of non-REM sleep with a simple configuration and high accuracy.

  In the present invention, the sleep state determining means analyzes the body motion signal to determine the body motion frequency, compares the body motion frequency with a threshold value, and determines the awakening stage and the REM sleep stage. . In the present invention, the sleep state determination means performs frequency analysis on the biological signal of the respiratory circulatory system, compares the fluctuation rate of the frequency fluctuation band of the biological signal of the respiratory circulatory system with the threshold value, The sleep stage is discriminated. In the present invention, the sleep state determination means periodically analyzes the biological signal of the respiratory circulatory system, compares the fluctuation rate of the periodic dispersion value of the biological signal of the respiratory circulatory system with the threshold value, and The sleep stage is discriminated. In the present invention, the sleep state determination means performs amplitude analysis on the respiratory circulatory system biological signal, compares the fluctuation rate of the amplitude dispersion value of the respiratory circulatory system biological signal with the threshold value, The sleep stage is discriminated. For this reason, it is possible to determine the awakening stage and the REM sleep stage with a simple configuration and high accuracy.

  In the present invention, real-time processing is possible by determining a threshold based on a past reference value of the same subject and analyzing the sleep state in real time. Further, in the present invention, real-time processing is possible by determining a threshold based on information on the occupation ratio of the sleep stage with respect to age and analyzing the sleep state in real time. As a result of enabling real-time analysis of sleep, it can be used for the purpose of controlling devices and environments according to sleep states.

Hereinafter, embodiments of the present invention will be described with reference to the drawings.
(1) Configuration of sleep analyzer.
FIG. 1 shows a first embodiment of the present invention. In FIG. 1, 1 is an air mat. An air hose 2 is connected to the air mat 1, and a pressure sensor 3 is attached to the air hose 2. The pressure sensor 3 detects a signal due to a pressure change of the air mat 1 and converts it into an electric signal.

  While the subject 5 is lying on the air mat 1, various biological signals of the subject 5 are obtained from the pressure sensor 3. That is, the pressure change due to the heartbeat of the subject 5 is transmitted to the air mat 1 and detected by the pressure sensor 3. Further, the pressure change due to the breathing of the subject 5 is transmitted to the air mat 1 and detected by the pressure sensor 3. Further, when the subject 5 moves, the pressure change due to the body movement is transmitted to the air mat 1 and is detected by the pressure sensor 3. From the pressure sensor 3, it is detected in a state where biological signals such as a heartbeat signal, a respiratory signal, and a body motion signal are mixed.

  The output signal of the pressure sensor 3 is sent to the biological signal separation unit 6 via the sensor interface 4. The biological signal separation unit 6 separates the heartbeat signal, the respiratory signal, and the body motion signal from the output signal of the pressure sensor 3.

  The biological signal separation unit 6 separates each biological signal by frequency division, for example. The biological signal separation unit 6 includes bandpass filters 7a, 7b, and 7c. The heartbeat signal is separated by a band-pass filter 7a that extracts a signal of a frequency component of a basic heartbeat signal (around 60 Hz) or a heartbeat-related signal (10 Hz and its harmonics). The respiration signal is separated by a bandpass filter 7b that extracts a signal of a frequency component of a basic respiration signal (near 0.3 Hz). The body motion signal is separated by a band pass filter 7c that extracts a signal of (100 Hz to 300 Hz).

  The heartbeat signal, the respiration signal, and the body motion signal separated by the bandpass filters 7a, 7b, and 7c are sent to the sleep state determination unit 9 via the signal processing units 8a, 8b, and 8c that perform level detection and amplification.

  The sleep state determination unit 9 determines a sleep state using a respiratory circulatory system biological signal (heart rate signal, respiratory signal) and a body motion signal. That is, it is known that blood pressure, respiration, and pulse, which are biological information of the respiratory circulatory system, change according to the state of sleep. For example, in the REM sleep period, blood pressure and pulse rate fluctuate, and when a deep sleep state is entered, the blood pressure decreases and the pulse rate also decreases. Therefore, by analyzing the respiratory circulatory system biological signal (heart rate signal, respiratory signal) during sleep, the human sleep depth (non-REM sleep stage) can be estimated. In addition, the body motion frequency is high in the awake state, but the body motion frequency decreases when entering the REM sleep stage. Therefore, by analyzing the body motion signal, it can be determined whether it is an arousal stage or a REM sleep stage. In addition, in the awake state, the fluctuation rate of the biological signal of the respiratory circulatory system is small, but when entering the REM sleep stage, the fluctuation rate of the biological signal of the respiratory circulatory system increases. Therefore, it can be determined from the fluctuation rate of the biological signal of the respiratory circulatory system whether it is an awakening stage or a REM sleeping stage. The sleep state determination unit 9 determines the sleep state by dividing into six stages, for example, an awakening stage, a REM sleep stage, a non-REM first stage, a non-REM second stage, a non-REM third stage, and a non-REM fourth stage. This determination result is output from the output terminal 10.

  As described above, in the embodiment of the present invention, various biological signals of the subject 5 are detected by the pressure sensor 3, and a heartbeat signal, a respiratory signal, and a body are detected by the biological signal separation unit 6 from the biological signal from the pressure sensor 3. The sleep state determination unit 9 determines the sleep state using the separated heartbeat signal, respiratory signal, and body motion signal biological signal.

  In the embodiment of the present invention, the sleep state is detected only by the subject 5 sleeping on the air mat 1, and the sensor is not directly attached to the human body unlike the sleep polysomnograph test that is a conventional method. For this reason, the problem that the sensor falls off due to the subject's rollover or body movement and does not function or the continuity of the measurement data is doubted due to changes in the sensor mounting conditions does not occur.

  Moreover, since the test subject 5 does not receive the stress that is measured by the sensor, the same psychological state sleep state as that at the time of non-measurement can be measured. This leads to improved reliability of measurement data.

Furthermore, since the determination by a specialist is not required unlike the electroencephalogram analysis, the subject can be measured anywhere such as at home.
(2) Principle of sleep analysis.
Next, how the sleep state is determined by the sleep state determination unit 9 based on the heartbeat signal, the respiratory signal, and the body motion signal biological signals separated by the biological signal separation unit 6 will be described.

  As described above, it is known that blood pressure, respiration, and pulse, which are biological information of the respiratory circulatory system, change depending on the sleep state. For example, in the REM sleep period, blood pressure and pulse rate fluctuate, and when a deep sleep state is entered, the blood pressure decreases and the pulse rate also decreases. Therefore, if a respiratory circulatory system biological signal such as a heartbeat signal or a respiratory signal is captured and a change in the biological signal is analyzed, the sleep state of the person can be estimated.

Changes in respiratory circulatory system biological signals (heart rate signal, respiratory signal) can be captured by frequency analysis. In addition, changes in respiratory circulatory system biological signals can be captured by periodic analysis. In addition, changes in respiratory circulatory system biological signals can be captured by amplitude analysis.
(2-1) Sleep analysis using frequency fluctuation band.
First, an example in which the sleep state is determined by analyzing the frequency of the respiratory circulatory system biosignal will be described.

  As shown in FIG. 2A, it is assumed that a time-series respiratory circulatory system biological signal that changes with time is obtained. When this respiratory circulatory system biological signal is converted into a spectrum by FFT (Fast Fourier Transform), a frequency fluctuation band is detected as shown in FIG. This frequency fluctuation band Δf reflects the fluctuation of the respiratory circulatory system biological signal. That is, during the awakening stage or REM sleep, fluctuations in the respiratory circulatory system biological signal are large, and at this time, the frequency fluctuation band Δf is large. When deep sleep is entered, fluctuations in the respiratory circulatory system biological signal become smaller and the frequency fluctuation band Δf becomes smaller. Therefore, the sleep state can be determined by converting the respiratory circulatory system biological signal into a spectrum by FFT and measuring the frequency fluctuation band Δf from this spectrum.

  FIG. 3 is a relationship graph for calculating sleep depth (non-rem 1 stage, non-rem 2 stage, non-rem 3 stage, non-rem 4 stage) using respiratory circulatory system biosignals as input information. In FIG. 3, the horizontal axis represents the sleep depth, and the vertical axis represents the frequency fluctuation band of the respiratory circulatory system biological signal. As shown in FIG. 3, the frequency fluctuation band Δf of the respiratory circulatory system biological signal is large in the awakening stage and the REM sleep stage. In the sleep state, the frequency fluctuation band Δf of the respiratory circulatory system biological signal becomes smaller as sleep becomes deeper. From this relationship, threshold values SthR1, Sth12, Sth23, and Sth34 are set, and the frequency fluctuation band Δf of the respiratory circulatory system biological signal is compared with each threshold value SthR1, Sth12, Sth23, and Sth34, thereby providing four non-rem steps. It is possible to determine which sleep stage is in.

  As described above, the non-REM four stages can be determined from the frequency fluctuation band Δf of the respiratory circulatory system biological signal. However, in the wakefulness stage and the REM sleep stage, the respiratory circulatory system biological signal is largely fluctuated, and the determination of whether it is in the wakefulness stage or the REM sleep stage can be made only by the frequency fluctuation band Δf of the respiratory circulatory system biological signal Difficult to do.

  Whether it is in the wakefulness stage or the REM sleep stage can be determined from the fluctuation rate of the frequency fluctuation band of the body movement frequency or the respiratory circulatory system biological signal.

That is, FIG. 4 is a graph showing the relationship for calculating the sleep depth (wakefulness stage, REM sleep stage) using the body motion frequency and the fluctuation rate of the frequency fluctuation band of the respiratory circulatory system biosignal as input information. . In FIG. 4, the horizontal axis represents the sleep depth, and the vertical axis represents the body movement frequency and the fluctuation rate of the frequency fluctuation band of the respiratory circulatory system biological signal. As shown in FIG. 4, the body motion frequency is large at the awakening stage, and the body motion frequency is reduced when entering the REM sleep stage. Further, in the awakening stage, the fluctuation rate of the frequency fluctuation band of the respiratory circulatory system biological signal is small, but when entering the REM sleep stage, the fluctuation rate of the frequency fluctuation band of the respiratory circulatory system biological signal increases. From this graph, by comparing the body motion frequency with the threshold value SthWR, it is possible to determine whether it is in the awakening stage or the REM sleep stage. Further, by comparing the fluctuation rate of the frequency fluctuation band of the respiratory circulatory system biosignal with the threshold value SthWR, it is possible to determine whether it is in the awakening stage or the REM sleep stage.
(2-2) Sleep analysis using periodic dispersion values.
Next, an example in which a sleep state is determined by periodically analyzing respiratory circulatory system biological signals will be described.

  As shown in FIG. 5A, it is assumed that a time-series respiratory circulatory system biological signal that changes with time is obtained. As shown in FIG. 5 (B), the time for each cycle of this respiratory circulatory system biological signal is measured, and from the dispersion of the time for each cycle of this respiratory circulatory system biological signal, FIG. When a histogram as shown in the figure is created, the periodic dispersion value Δt of the respiratory circulatory system biological signal is obtained. This periodic dispersion value Δt reflects the fluctuation of the respiratory circulatory system biological signal. That is, during the wakefulness stage or REM sleep, the fluctuation of the respiratory circulatory system biological signal is large, and at this time, the periodic dispersion value Δt of the respiratory circulatory system biological signal is large. When deep sleep is entered, the fluctuation of the respiratory circulatory system biological signal becomes small, and the periodic dispersion value Δt of the respiratory circulatory system biological signal becomes small. From this, the sleep state can be determined by measuring the time for each cycle of the respiratory circulatory system biological signal and obtaining the periodic dispersion value Δt.

  FIG. 6 is a relationship graph for calculating sleep depth (non-rem 1 stage, non-rem 2 stage, non-rem 3 stage, non-rem 4 stage) using respiratory circulatory system biosignals as input information. In FIG. 6, the horizontal axis represents the sleep depth, and the vertical axis represents the periodic dispersion value Δt of the respiratory circulatory system biological signal. As shown in FIG. 6, the periodic dispersion value Δt of the respiratory circulatory system biological signal is large in the awakening stage and the REM sleep stage. In the sleep state, the periodic dispersion value Δt of the respiratory circulatory system biological signal becomes smaller as the sleep becomes deeper. From this relationship, threshold values SthR1, Sth12, Sth23, and Sth34 are set, and the periodic dispersion value Δt of the respiratory circulatory system biological signal is compared with each threshold value SthR1, Sth12, Sth23, and Sth34, thereby providing four non-rem steps. It is possible to determine which sleep stage is in.

  Whether it is in the wakefulness stage or the REM sleep stage can be determined from the fluctuation rate of the body motion frequency or the periodic dispersion value of the respiratory circulatory system biological signal.

FIG. 7 is a graph showing the relationship for calculating the sleep depth (wakefulness stage, REM sleep stage) using the body motion frequency and the fluctuation rate of the periodic dispersion value of the respiratory circulatory system biological signal as input information. In FIG. 7, the horizontal axis represents the sleep depth, and the vertical axis represents the body motion frequency and the fluctuation rate of the periodic dispersion value of the respiratory circulatory system biological signal. As shown in FIG. 7, the body motion frequency is large in the awakening stage, and the body motion frequency is reduced when entering the REM sleep stage. In the wakefulness stage, the fluctuation rate of the periodic dispersion value of the respiratory circulatory system biological signal is small, but when entering the REM sleep stage, the fluctuation range of the frequency fluctuation band of the respiratory circulatory system biological signal increases. From this graph, by comparing the body motion frequency with the threshold value SthWR, it is possible to determine whether it is in the awakening stage or the REM sleep stage. Further, by comparing the fluctuation rate of the frequency fluctuation band of the respiratory circulatory system biological signal with the threshold value SthWR, it is possible to determine whether it is in the awakening stage or the REM sleep stage.
(2-3) Sleep analysis using amplitude variance value.
Next, an example in which the sleep state is determined by analyzing the amplitude of the respiratory circulatory system biological signal will be described.

  As shown in FIG. 8A, it is assumed that a time-series respiratory circulatory system biological signal that changes with time is obtained. As shown in FIG. 8B, the amplitude of each respiratory circulatory biological signal is measured and the variance is obtained from the amplitude of each respiratory circulatory biological signal. When the histogram as shown in FIG. 2 is created, the amplitude dispersion value ΔA of the respiratory circulatory system biological signal is obtained. This amplitude dispersion value ΔA reflects fluctuations in the respiratory circulatory system biological signal. That is, during the awakening stage or REM sleep, the fluctuation of the respiratory circulatory system biological signal is large, and at this time, the amplitude dispersion value ΔA of the respiratory circulatory system biological signal is large. When deep sleep is entered, fluctuations in the respiratory circulatory system biological signal become smaller, and the amplitude dispersion value ΔA of the respiratory circulatory system biological signal becomes smaller. From this, the sleep state can be determined by measuring the amplitude for each cycle of the respiratory circulatory system biological signal and obtaining the amplitude dispersion value ΔA.

  FIG. 9 is a relationship graph for calculating sleep depth (non-rem 1 stage, non-rem 2 stage, non-rem 3 stage, non-rem 4 stage) using respiratory circulatory system biosignals as input information. In FIG. 9, the horizontal axis indicates the sleep depth, and the vertical axis indicates the amplitude dispersion value ΔA of the respiratory circulatory system biological signal. As shown in FIG. 9, the amplitude dispersion value ΔA of the respiratory circulatory system biological signal is large in the awakening stage and the REM sleep stage. In the sleep state, the amplitude dispersion value ΔA of the respiratory circulatory system biological signal becomes smaller as sleep becomes deeper. From this relationship, threshold values SthR1, Sth12, Sth23, and Sth34 are set, and the amplitude dispersion value ΔA of the respiratory circulatory system biological signal is compared with each threshold value SthR1, Sth12, Sth23, and Sth34, thereby providing four non-rem steps. It is possible to determine which sleep stage is in.

  Whether it is in the awakening stage or the REM sleep stage can be determined from the fluctuation rate of the body motion frequency or the amplitude dispersion value of the respiratory circulatory system biological signal.

FIG. 10 is a graph showing the relationship for calculating the sleep depth (wakefulness stage, REM sleep stage) using the body motion frequency and the fluctuation rate of the amplitude dispersion value of the respiratory circulatory system biological signal as input information. In FIG. 10, the horizontal axis represents the sleep depth, and the vertical axis represents the body motion frequency and the fluctuation rate of the amplitude dispersion value of the respiratory circulatory system biological signal. As shown in FIG. 10, the body motion frequency is large at the awakening stage, and the body motion frequency is reduced when entering the REM sleep stage. In the awakening stage, the fluctuation rate of the amplitude dispersion value of the respiratory circulatory system biological signal is small. However, when entering the REM sleep stage, the fluctuation range of the frequency fluctuation band of the respiratory circulatory system biological signal increases. From this graph, by comparing the body motion frequency and the threshold value SthWR, it is possible to determine whether it is in the awakening stage or the REM sleep stage. Further, by comparing the fluctuation rate of the frequency fluctuation band of the respiratory circulatory system biological signal with the threshold value SthWR, it is possible to determine whether it is in the awakening stage or the REM sleep stage.
(3) Configuration of sleep state determination unit.
As described above, the non-REM four-stage sleep stage is determined using the frequency fluctuation band of the respiratory circulatory system biological signal, the periodic dispersion value of the respiratory circulatory system biological signal, and the amplitude dispersion value of the respiratory circulatory system biological signal. it can. In addition, the frequency of body motion or the rate of fluctuation of the frequency fluctuation band of the respiratory circulatory system biological signal, the rate of fluctuation of the periodic dispersion value of the respiratory circulatory system biological signal, and the rate of fluctuation of the amplitude dispersion value of the respiratory circulatory system biological signal Thus, it can be determined whether it is awakening or REM sleep. Based on such a principle, the sleep state determination part 9 (FIG. 1) which determines a sleep state can be comprised.
(3-1) Sleep state determination unit using frequency fluctuation band.
FIG. 11 shows an example of the sleep state determination unit 9. In this example, the non-REM four-stage sleep stage is determined using the frequency fluctuation band of the respiratory circulatory system biosignal, and the body movement frequency is used to determine whether it is an awakening stage or a REM sleep stage.

  In FIG. 11, a respiratory circulatory system biological signal (heartbeat signal, respiratory signal) is supplied to the input terminal 21. A body motion signal is supplied to the input terminal 22.

  The respiratory circulatory system biological signal from the input terminal 21 is supplied to the FFT processing unit 23. The FFT processing unit 23 converts the respiratory circulatory system biological signal into a spectrum. The output signal of the FFT processing unit 23 is supplied to the variance value calculation unit 24. The variance value calculation unit 24 calculates the frequency fluctuation band Δf of the respiratory circulatory system biological signal. The frequency fluctuation band Δf calculated by the variance value calculation unit 24 is supplied to the comparators 25a, 25b, 25c, and 25d.

  The threshold values Sth34, Sth23, Sth12, and SthR1 are supplied from the threshold value generator 27 to the comparators 25a, 25b, 25c, and 25d, respectively. The comparators 25a, 25b, 25c, and 25d compare the frequency fluctuation band Δf of the respiratory circulatory system biological signal with the threshold values Sth34, Sth23, Sth12, and SthR1. The output signals of the comparators 25a, 25b, 25c, and 25d are supplied to the non-REM sleep stage determination unit 26. A non-REM sleep stage determination unit 26 determines a non-REM four-stage sleep state.

  The body motion signal from the input terminal 22 is supplied to the body motion level normalization unit 31. The body motion level normalization unit 31 detects the body motion frequency. The body motion frequency detected by the body motion level normalization unit 31 is supplied to the comparator 32. The threshold value SthWR is supplied from the threshold value generator 27 to the comparator 32. The comparator 32 compares the body motion frequency with the threshold value SthWR. The output signal of the comparator 32 is supplied to the awakening / REM sleep determination unit 33. The awakening / REM sleep determination unit 33 determines whether it is an awakening state or a REM sleeping state.

  The output signal of the non-REM sleep stage determination unit 26 and the output signal of the awake / REM sleep determination unit 33 are supplied to the total sleep determination unit 34. The total sleep determination unit 34 determines six stages of sleep states from the output signal of the non-REM sleep stage determination unit 26 and the output signal of the awake / REM sleep determination unit 33. The sleep state determination result is output from the output terminal 35.

  In this example, the determination of whether the state is awake or REM sleep is performed using the body motion frequency. However, as shown in FIG. 12, a fluctuation rate calculation unit 36 is provided, and an FFT processing unit 23 is provided. Is supplied to the fluctuation rate calculation unit 36, and the fluctuation rate calculation unit 36 calculates the rate of change of the frequency fluctuation band of the respiratory circulatory biological signal, and the change of the frequency fluctuation band of the respiratory circulatory biological signal The rate is supplied to the comparator 37, and the comparator 37 compares the rate of change of the frequency fluctuation band of the respiratory circulatory system biological signal with the threshold value SthWR to determine whether the state is awake or REM sleep. Also good.

Further, as shown in FIG. 13, both the body motion level normalization unit 31 and the fluctuation rate calculation unit 36 are provided, and it is determined whether the state of wakefulness or REM sleep is based on the body motion frequency and the respiratory circulatory system biological signal. You may make it carry out using both the change rate of a frequency fluctuation band.
(3-2) Sleep state determination unit using period analysis value.
FIG. 14 shows another example of the sleep state determination unit 9. In this example, the non-REM four-stage sleep stage is determined using the periodic dispersion value of the respiratory circulatory system biological signal, and the body movement frequency is used to determine whether it is the awakening stage or the REM sleep stage.

  In FIG. 14, a respiratory circulatory system biological signal (heartbeat signal, respiratory signal) is supplied to the input terminal 121. A body motion signal is supplied to the input terminal 122.

  The respiratory circulatory system biological signal from the input terminal 121 is supplied to the period measuring unit 123. The period measurement unit 123 measures the time for each period of the respiratory circulatory system biological signal. The output signal of the period measurement unit 123 is supplied to the variance value calculation unit 124. The dispersion value calculation unit 124 calculates a periodic dispersion value Δt of the respiratory circulatory system biological signal. The periodic dispersion value Δt calculated by the dispersion value calculation unit 124 is supplied to the comparators 125a, 125b, 125c, and 125d.

  The threshold values Sth34, Sth23, Sth12, and SthR1 are supplied from the threshold value generator 127 to the comparators 125a, 125b, 125c, and 125d, respectively. The comparators 125a, 125b, 125c, and 125d compare the frequency fluctuation band Δf of the respiratory circulatory system biological signal with the threshold values Sth34, Sth23, Sth12, and SthR1. The output signals of the comparators 125a, 25b, 125c, and 125d are supplied to the non-REM sleep stage determination unit 126. The non-REM sleep stage determination unit 126 determines the four-stage sleep state of the non-REM.

  The body motion signal from the input terminal 122 is supplied to the body motion level normalization unit 131. The body motion level normalization unit 131 detects the body motion frequency. The body motion frequency detected by the body motion level normalization unit 131 is supplied to the comparator 132. The threshold value SthWR is supplied from the threshold value generator 127 to the comparator 132. The comparator 132 compares the body motion frequency with the threshold value SthWR. The output signal of the comparator 132 is supplied to the awakening / REM sleep determination unit 133. The awakening / REM sleep determination unit 133 determines whether it is an awake state or a REM sleep state.

  The output signal of the non-REM sleep stage determination unit 126 and the output signal of the awake / REM sleep determination unit 133 are supplied to the total sleep determination unit 134. The total sleep determination unit 134 determines six stages of sleep states from the output signal of the non-REM sleep stage determination unit 126 and the output signal of the awake / REM sleep determination unit 133. The sleep state determination result is output from the output terminal 135.

  In this example, the determination of whether the state is awake or REM sleep is performed using the body motion frequency. However, as shown in FIG. 15, a fluctuation rate calculation unit 136 is provided, and the period measurement unit 123 is provided. The fluctuation rate calculation unit 136 calculates the change rate of the periodic dispersion value of the respiratory circulatory system biological signal, and changes the periodic dispersion value of the respiratory circulatory system biological signal. The rate is supplied to the comparator 137, and the comparator 137 compares the rate of change of the periodic dispersion value of the respiratory circulatory system biological signal with the threshold value SthWR to determine whether it is an awake state or a REM sleep state. Also good.

Also, as shown in FIG. 16, the body motion level normalization unit 131 and the fluctuation rate calculation unit 136 are both provided to determine whether the state is awake or REM sleep, the body motion frequency, and the respiratory circulatory system biological signal. It is also possible to use both of the change rate of the periodic dispersion value.
(3-3) Sleep determination unit using amplitude dispersion value.
FIG. 17 shows still another example of the sleep state determination unit 9. In this example, the non-REM four-stage sleep stage is determined using the amplitude dispersion value of the respiratory circulatory system biological signal, and the body movement frequency is used to determine whether it is an awakening stage or a REM sleep stage.

  In FIG. 17, a respiratory circulatory system biological signal (heart rate signal, respiratory signal) is supplied to the input terminal 221. A body motion signal is supplied to the input terminal 222.

  A respiratory circulatory system biological signal from the input terminal 221 is supplied to the amplitude measuring unit 223. The amplitude measurement unit 223 measures the amplitude of the respiratory circulatory system biological signal. An output signal of the amplitude measuring unit 223 is supplied to the variance value calculating unit 224. The variance value calculation unit 224 calculates the amplitude variance value ΔA of the respiratory circulatory system biological signal. The amplitude dispersion value ΔA calculated by the dispersion value calculation unit 224 is supplied to the comparators 225a, 225b, 225c, and 225d.

  The threshold values Sth34, Sth23, Sth12, and SthR1 are supplied from the threshold value generator 227 to the comparators 225a, 225b, 225c, and 225d, respectively. The comparators 225a, 225b, 225c, and 225d compare the frequency fluctuation band Δf of the respiratory circulatory system biological signal with the threshold values Sth34, Sth23, Sth12, and SthR1. Output signals of the comparators 225a, 25b, 225c, and 225d are supplied to the non-REM sleep stage determination unit 226. The non-REM sleep stage determination unit 226 determines a non-REM four-stage sleep state.

  The body motion signal from the input terminal 222 is supplied to the body motion level normalization unit 231. The body motion level normalization unit 231 detects the body motion frequency. The body motion frequency detected by the body motion level normalization unit 231 is supplied to the comparator 232. The threshold value SthWR is supplied from the threshold value generation unit 227 to the comparator 232. The comparator 232 compares the body movement frequency with the threshold value SthWR. The output signal of the comparator 232 is supplied to the awakening / REM sleep determination unit 233. The awakening / REM sleep determination unit 233 determines whether it is an awakening state or a REM sleeping state.

  The output signal of the non-REM sleep determination unit 226 and the output signal of the awake / REM sleep determination unit 233 are supplied to the total sleep determination unit 234. The total sleep determination unit 234 determines six stages of sleep states from the output signal of the non-REM sleep stage determination unit 226 and the output signal of the awake / REM sleep determination unit 233. The sleep state determination result is output from the output terminal 235.

  In this example, the determination of whether the state is awake or REM sleep is performed using the body movement frequency. However, as shown in FIG. 18, a fluctuation rate calculation unit 236 is provided, and an amplitude measurement unit 223 is provided. Is supplied to the fluctuation rate calculation unit 236, and the fluctuation rate calculation unit 236 calculates the change rate of the amplitude dispersion value of the respiratory circulatory system biological signal, and the change of the amplitude dispersion value of the respiratory circulatory system biological signal. The rate is supplied to the comparator 237, and the comparator 237 compares the change rate of the amplitude dispersion value of the respiratory circulatory system biological signal with the threshold value SthWR to determine whether it is awake or REM sleep. Also good.

Further, as shown in FIG. 19, a body motion level normalization unit 231 and a fluctuation rate calculation unit 236 are provided together to determine whether a state of wakefulness or a state of REM sleep is based on the frequency of body motion and a respiratory circulatory system biological signal. This may be performed using both of the change rate of the amplitude dispersion value.
The sleep analysis using the frequency fluctuation band, the sleep analysis using the periodic dispersion value, and the sleep analysis using the amplitude dispersion value have been described above. However, the sleep analysis may be performed by a combination of these. You may make it carry out using all. In addition, as a biological signal of the respiratory circulatory system, only a heartbeat signal or a respiratory signal may be used, or both a heartbeat signal and a respiratory signal may be used.
(4) Real-time processing.
As described above, the non-REM four-stage sleep state is determined by comparing the frequency fluctuation band Δf, the periodic dispersion value Δt, the amplitude dispersion value ΔA of the biological signal of the respiratory circulatory system with the threshold values St hR1, Sth12, Sth23, and Sth34. Is done. In addition, the determination of the arousal stage and the REM sleep is performed by comparing the body movement frequency and the threshold value SthWR, or the rate of change of the frequency fluctuation band Δf of the biological signal of the respiratory circulatory system, the periodic dispersion value This is performed by comparing the rate of change of Δt, the rate of change of the amplitude dispersion value ΔA, and the threshold value SthWR. In order to accurately determine the sleep stage of a person, these threshold values cannot be set accurately unless the sleep of the subject 5 is measured overnight.

However, if immediate or real-time sleep stage information is required, a temporary value can be used for this threshold. For example, when the subject is the same person and repeatedly measures biometric information, the variation value of the biometric information as shown in FIG. 20 can be regarded as almost constant, so that the threshold value can be regarded as almost constant. That is, when the subject is the same person, the sleep stage can be determined in real time by temporarily determining the threshold value based on the previous day value, the rule value or the average value in the past, or the like.
(5) About threshold setting.
When the subject 5 measures with the sleep analysis system according to the present invention, the threshold value can be narrowed down based on age-specific sleep stage occupancy.

  That is, FIG. 21 is a graph showing the occupation ratio of each sleep stage with respect to age. In FIG. 21, the horizontal axis indicates the age, and the vertical axis indicates the occupancy rate of each sleep stage. From this graph, it can be seen that during sleep, the occupancy rates of the awakening stage, REM sleep stage, non-REM first stage, non-REM second stage, non-REM third stage, and non-REM fourth stage change with age. . Simply put, sleep becomes shallower with age, and the occupancy rate at the awakening stage increases.

  Using the graph shown in FIG. 21, a method for obtaining a total of five threshold values necessary for determining six sleep stages will be described.

Each threshold is defined as follows.
SthR1: Arousal stage and REM stage and non-rem 1 stage thresholds as biological information
Sth12: Non-rem 2 stage and non-rem 3 stage threshold as biometric information
Sth23: Non-rem 3 stage and non-rem 2 stage thresholds as biometric information
Sth34: Threshold of non-REM 4 stage and non-REM 3 stage as biological information The sleep stage occupancy at each age is defined as follows.
R_W: occupancy rate of the arousal stage at the specified age
R_REM: occupancy rate of REM sleep stage at specified age
R_1: Non-REM one-stage occupancy at the specified age
R_2: Non-REM two-stage occupancy at the specified age
R_3: Non-REM three-stage occupancy at the specified age
R_4: Non-REM four-stage occupancy at the specified age (total from R_W to R_4 is 100%)
Assuming that the total sleep time is T, the non-REM 4-stage time at the specified age is R_4 * T. Therefore, the threshold value Sth34 for discriminating between the non-rem fourth stage and the non-rem third stage is calculated as follows.
Using Sth34 as a parameter, the total time T_Sleep4 for t that satisfies Sth34 ≧ S (t) is
T_Sleep4 = Σt = R_4 * T
When this condition is satisfied, Sth34 becomes a threshold value for discriminating between the non-rem fourth stage 4 and the non-rem third stage.

If the total sleep time is T, the non-REM three-stage time at the specified age is R_3 * T. Therefore, the threshold value Sth23 for discriminating between the non-rem third stage and the non-rem second stage is calculated as follows.
T total time satisfying Sth23 ≧ S (t)> Sth34 with Sth23 as a parameter
T_Sleep3
T_Sleep3 ＝ Σt ＝ R_3 * T
When this condition is satisfied, Sth23 becomes a threshold value for discriminating between the non-rem third stage and the non-rem second stage.

If the total sleep time is T, the non-rem two-stage time at the specified age is R_2 * T. Therefore, the threshold value Sth12 for discriminating between the non-rem second stage and the non-rem first stage is calculated as follows.
T total time satisfying Sth12 ≧ S (t)> Sth23 with Sth12 as parameter
T_Sleep2
T_Sleep2 ＝ Σt ＝ R_2 ＊ T
When the above condition is satisfied, Sth12 is a threshold for discriminating between sleep stages 2 and 1.

Assuming that the total sleep time is T, the non-REM one-stage time at the specified age is R_1 * T. The threshold value SthWR for discriminating the awakening stage and the REM sleep stage from the non-REM first stage is calculated as follows.
Using SthR1 as a parameter, the total time T_Sleep1 of t that satisfies SthR1 ≧ S (t)> Sth12 is
T_Sleep1 ＝ Σt ＝ R_1 ＊ T
When the above condition is satisfied, SthR1 becomes a threshold value for discriminating the awakening stage and the REM sleep stage from the sleep stage 1.

As a threshold for discriminating the wakefulness stage from the REM sleep stage, when calculating from the fluctuation rate of the respiratory circulatory system biological signal, the total time T_SleepREM satisfying SthWR ≧ S (t)> SthR1 with SthWR as a parameter is
T_SleepREM = Σt = R_REM * T
When the above condition is satisfied, SthWR becomes a threshold for discriminating between sleep stage awakening and sleep stage REM.

When calculating from the body movement frequency, the total time T_SleepW of t satisfying S (t)> SthWR is obtained using SthWR as a parameter.
T_SleepW = Σt = R_W * T
When the above condition is satisfied, SthWR becomes a threshold for discriminating between sleep stage awakening and sleep stage REM.
(6) Setting of the threshold value of the sleep state determination unit.
FIG. 22 adds the age-specific occupation ratio table 39 to the sleep state determination unit 9 shown in FIG. 11 so that threshold values can be set. In FIG. 22, the threshold value generation unit 27 receives the frequency fluctuation band Δf of the respiratory circulatory system biological signal from the FFT processing unit 23 and the body motion signal. When the age of the subject 5 is input to the input terminal 38, the sleep stage occupancy for each age is output from the occupancy ratio table 39 by age, and the sleep stage occupancy for each age is sent to the threshold value generator 27. It is done. As described above, the threshold generation unit 27 generates the non-REM four-stage thresholds SthR1, Sth12, Sth23, and Sth34, and the threshold SthWR between the awakening stage and the REM sleep stage. The thresholds SthR1, Sth12, Sth23, and Sth34 of the non-REM four stages are sent to the comparators 25a to 25d, and the threshold SthWR of the wakefulness stage and the REM sleep stage is sent to the comparator 32.

Similarly, the sleep state determination unit 9 shown in FIGS. 12 to 19 can also be set by adding an age-specific occupation rate table 39.
(7) Application device.
FIG. 23 and FIG. 25 show an application device using a sleep stage as a sleep analysis result. The example of this application apparatus is intended to control a hardware device (for example, a rice cooker, a water heater, a telephone, etc.) suitable for a sleep state by knowing a person's sleep stage.

  There are cases where a person's wake-up time should be determined by the so-called time, and cases where the person's wake-up time may be determined inversely from the person's sleep state. For example, when the next day is a holiday, sleep for the purpose of recovery from fatigue, or a person capable of free time behavior that is not restrained by a person, it is ideal to wake up at a so-called awake time . This application example shows an application example in accordance with this gist.

  In FIG. 23, the sleep stage input interface 51 receives a total of six stages of sleep estimation results. The arithmetic processing block 52 is means for determining stop / start / function change of an external device to be controlled using the sleep stage result. The remote control interface 53 is an interface for transmitting a command to the learning type remote control unit 54. The hardware 55 is controlled by the remote control unit 54. The hardware 55 is, for example, a telephone.

  When the phone is ringing, etc., if the person is in a deep sleep state, the calling voice itself disturbs the sleep. Therefore, the sleep itself is stopped at the deep sleep stage of one or more stages of non-REM sleep. Real-time control. The periods of T1 to T2, T3 to T4, and T5 to T6 in FIG. 24 are ringing stop times. Next, in the case of a telephone having an additional function for transmitting a response message or the like to the caller, it is possible to notify the caller of an ideal call time while stopping ringing. It has been medically found that this process repeats a light sleep and a deep sleep with a sleep cycle of about 90 minutes. (Ultradian Rhythm) By applying this ultradian rhythm, it becomes possible to accurately predict the next state from a sleeping state. For example, if the time interval between the previous awakening (T7) and the previous awakening (T8) is Δt1, if a call is received at T10, the ringing is stopped so as not to disturb sleep, and the next The awakening time T9 = T8 + Δt1 = T10 + Δt2 can be predicted as the ideal time. In this case, “Please call at time T9. Or "Please call me after Δt2." Can be transmitted.

  Next, FIG. 25 uses a rice cooker or a water heater as the hardware 56. In the case of a device such as a rice cooker or a water heater that can provide a convenient and energy-saving effect when its role is completed when waking up, an example is shown in which the operation is started by calculating backward from the awake state. It has been found medically that a person's sleep cycle repeats a light sleep and a deep sleep in a cycle of about 90 minutes. (Ultradian Rhythm) By applying this ultradian rhythm, it is possible to accurately predict the next state from a sleep state.

  In FIG. 26, for example, when the time interval between the previous awakening (T1) and the previous awakening (T2) is Δt1, it is expected that the next awakening is T4 at the time of T2. For example, in the case of a device such as a rice cooker in which the time from the start of the function until the completion of the function is Δt2, the optimal time to start the function at the time of T2 can be calculated as T2 + Δt1−Δt2 = T4−Δt2.

  The present invention is not limited to the above-described embodiments, and various modifications and applications can be made without departing from the gist of the present invention.

  INDUSTRIAL APPLICABILITY The present invention can be used to analyze a person's sleep state and use it medically as well as to control hardware according to the sleep state.

It is a block diagram which shows the structure of the sleep analyzer to which this invention was applied. It is a wave form diagram used for description of judgment of a sleep stage by frequency analysis. It is a graph used for description of determination of the sleep stage by frequency analysis. It is a graph used for description of determination of the sleep stage by frequency analysis. It is a wave form diagram used for description of determination of the sleep stage by a period analysis. It is a graph used for description of determination of the sleep stage by period analysis. It is a graph used for description of determination of the sleep stage by period analysis. It is a wave form diagram used for description of judgment of a sleep stage by amplitude analysis. It is a graph used for description of determination of the sleep stage by amplitude analysis. It is a graph used for description of determination of the sleep stage by amplitude analysis. It is a block diagram which shows an example of a structure of the sleep state determination part by frequency analysis. It is a block diagram which shows the other example of a structure of the sleep state determination part by frequency analysis. It is a block diagram which shows the further another example of a structure of the sleep state determination part by frequency analysis. It is a block diagram which shows an example of a structure of the sleep state determination part by a period analysis. It is a block diagram which shows the other example of a structure of the sleep state determination part by a period analysis. It is a block diagram which shows the further another example of a structure of the sleep state determination part by a period analysis. It is a block diagram which shows an example of a structure of the sleep state determination part by an amplitude analysis. It is a block diagram which shows the other example of a structure of the sleep state determination part by an amplitude analysis. It is a block diagram which shows the further another example of a structure of the sleep state determination part by an amplitude analysis. It is a graph used for description of the change of biometric information. It is explanatory drawing of the setting of the threshold value using the sleep stage occupation rate. It is a block diagram which shows an example of a structure of the sleep state determination part which performs the setting of the threshold value using a sleep stage occupation rate. It is a block diagram of an example of the application apparatus using the sleep analyzer by this invention. It is a graph used for description of an example of the application apparatus using the sleep analyzer by this invention. It is a block diagram of an example of the application apparatus using the sleep analyzer by this invention. It is a graph used for description of an example of the application apparatus using the sleep analyzer by this invention. It is a block diagram of an example of the conventional sleep analyzer. It is explanatory drawing of an example of the conventional sleep analyzer.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Air mat 2 Air hose 3 Pressure sensor 5 Subject 6 Biosignal separation part 7a, 7b, 7c Band pass filter 8a. 8b, 8c Signal processing unit 9 Sleep state determination unit 21 Respiratory circulatory system biological signal input terminal 22 Body motion signal input terminal 23 FFT processing unit 24 Variance calculation unit 25a, 25b, 25c, 25d Comparator 26 Non-REM sleep stage Determination unit 27 Threshold generation unit 31 Body motion level normalization unit 32 Comparator 33 REM sleep determination unit 34 Total sleep determination unit 35 Output terminal 36 Fluctuation rate calculation unit 37 Comparator 38 Age input terminal 39 Occupancy rate table by age 51 Sleep stage Input interface 52 Arithmetic processing block 53 Remote control interface 54 Remote control unit 55 Hardware 56 Hardware 101 Electroencephalogram electrode 102 Genital muscle electrode 103 Eye movement electrodes 104a, 104b, 104c Sensor interfaces 105a, 105b, 105c A / D converter 106 Arithmetic device 121 Respiratory circulatory system biological signal input terminal 122 Body motion signal input terminal 123 Period measurement unit 124 Variance calculation unit 125a, 125b, 125c, 125d Comparator 126 Non-REM sleep stage determination unit 127 Threshold generation unit 131 Body motion Level normalization unit 132 Comparator 133 Rem sleep determination unit 134 Total sleep determination unit 135 Output terminal 136 Fluctuation rate calculation unit 137 Comparator 221 Respiratory circulatory system biological signal input terminal 222 Body movement signal input terminal 223 Amplitude measurement unit 224 Variance calculation unit 225a, 225b, 225c, 225d Comparator 226 Non-REM sleep determination unit 226 Non-REM sleep stage determination unit 227 Threshold generation unit 231 Body movement level normalization unit 232 Comparator 233 REM sleep determination unit 234 Total sleep determination unit 235 Output Terminal 236 Fluctuation rate calculation unit 237 Comparator

Claims (6)

A sensor for detecting biological information of a subject lying on the air mat;
A biological signal separation means for separating a respiratory circulatory system biological signal and a body motion signal from the detection signal of the sensor;
A sleep state determination means for determining a sleep stage from the separated biological signal of the respiratory circulatory system and a body motion signal;
In the sleep state determination means, the sleep determination in the non-REM stage is performed based on the biological signal of the respiratory circulatory system, and the discrimination between the awakening stage and the REM sleep stage is the rate of change of the body motion signal or the biological signal of the respiratory circulatory system There line by,
The sleep state determination means frequency-analyzes the biological signal of the respiratory circulatory system and compares the frequency fluctuation band of the biological signal of the respiratory circulatory system with a threshold value to determine sleep at a non-REM stage.
Sleep analysis device comprising a call. A sensor for detecting biological information of a subject lying on the air mat;
A biological signal separation means for separating a respiratory circulatory system biological signal and a body motion signal from the detection signal of the sensor;
A sleep state determination means for determining a sleep stage from the separated biological signal of the respiratory circulatory system and a body motion signal;
In the sleep state determination means, the sleep determination in the non-REM stage is performed based on the biological signal of the respiratory circulatory system, and the discrimination between the awakening stage and the REM sleep stage is the rate of change of the body motion signal or the biological signal of the respiratory circulatory system There line by,
The sleep state determination means performs amplitude analysis on the biological signal of the respiratory circulatory system and compares the amplitude dispersion value of the biological signal of the respiratory circulatory system with a threshold value to determine sleep at a non-REM stage.
Sleep analysis device comprising a call. A sensor for detecting biological information of a subject lying on the air mat;
A biological signal separation means for separating a respiratory circulatory system biological signal and a body motion signal from the detection signal of the sensor;
A sleep state determination means for determining a sleep stage from the separated biological signal of the respiratory circulatory system and a body motion signal;
In the sleep state determining means, sleep determination of non-REM stage is carried out by the biological signal of the cardiorespiratory, the discrimination between awakening phase and REM sleep stages have rows by fluctuation rate of the biological signal before Symbol cardiorespiratory,
The sleep state determination means performs frequency analysis of the biological signal of the respiratory circulatory system, compares the variation rate of the frequency fluctuation band of the biological signal of the respiratory circulatory system with a threshold value, and awakening stage and REM sleep stage, To discriminate
Sleep analysis device comprising a call. A sensor for detecting biological information of a subject lying on the air mat;
A biological signal separation means for separating a respiratory circulatory system biological signal and a body motion signal from the detection signal of the sensor;
A sleep state determination means for determining a sleep stage from the separated biological signal of the respiratory circulatory system and a body motion signal;
In the sleep state determining means, sleep determination of non-REM stage is carried out by the biological signal of the cardiorespiratory, the discrimination between awakening phase and REM sleep stages have rows by fluctuation rate of the biological signal before Symbol cardiorespiratory,
The sleep state determination means performs an amplitude analysis on the biological signal of the respiratory circulatory system, compares the fluctuation rate of the amplitude dispersion value of the biological signal of the respiratory circulatory system with a threshold value, and awakening stage and REM sleep stage To discriminate
Sleep analysis device comprising a call. And determining the threshold based on the reference value of the past data obtained by statistically processing the same subject, and to analyze the sleep state in real time
Claim 1, wherein the this sleep analyzer according to claim 2, claim 3, or claim 4. And determining the threshold based on the information of occupancy sleep stages for age, it was to analyze the sleep state in real time
Claim 1, wherein the this sleep analyzer according to claim 2, claim 3, or claim 4.

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