JP4461388B2 - Sleep stage determination method and determination apparatus - Google Patents

Description

【Technical field】
  The present invention relates to a sleep stage determination method and a determination apparatus for determining a sleep stage from a biological signal detected by a biological signal detection means, wherein the sleep stage is determined without being affected by a difference in age of a subject or a change in physical condition. The present invention relates to a sleep stage determination method and a determination apparatus that accurately determine.
Technical background
  When examining an individual's health condition, sleep is often used as a determination index, and it is well known that sleep and health are closely related. If the depth and quality of health and nighttime sleep are closely related to the mood and energy of the next day, while mental stress and physical condition are poor, the transition pattern of sleep depth and sleep stage Change occurs and comfortable sleep is not obtained.
  In healthy sleep, the non-REM sleep stage and the REM sleep stage appear repeatedly at regular intervals after falling asleep, but it is known that the rhythm is disturbed when the patient feels sick or is stressed. It has been. Therefore, it is possible to know the subject's mental stress and poor physical condition by monitoring the sleep stage and its generation pattern during nighttime sleep.
  In particular, many elderly people complain of poor sleep such as light sleep, and the quality of sleep is a problem. In order to know the quality of sleep, it becomes possible to find countermeasures and measures that improve by knowing the transition of the sleep stage.
  As a conventional method for knowing a sleep stage, a method using a sleep polysomnograph (PSG) is generally used. In the method using PSG, the activity of the cranial nervous system during sleep can be estimated from brain waves, surface myoelectric potential, eye movement, etc., but much information about sleep can be obtained, but many electrodes are attached to the face and body of the subject Therefore, it is difficult to obtain natural sleep, and it takes several days to one week before getting used. Therefore, the physical and physical burden imposed on the subject is very large, and this measurement needs to be performed by a specialist who is familiar with specific facilities such as hospitals and handling. Therefore, the cost required for this also becomes large.
  For this reason, PSG is difficult to use for daily health management even if it can be an effective treatment method for patients who have obvious sleep disorders.
  Therefore, there is a high demand for a method for easily knowing the sleep stage without using PSG in order to know the daily health condition of the subject. However, some proposed methods for knowing the sleep stage require changing the judgment criteria due to differences in age, differences in individual differences in autonomic nerve components, etc., and judgment results may vary due to variations in individual differences. However, there is a problem of lack of accuracy, and it has not been put into practical use.
  SUMMARY OF THE INVENTION An object of the present invention is to provide a sleep stage determination method and a determination apparatus that can determine a stable sleep stage regardless of differences in the age of a subject or changes in physical condition.
  Furthermore, the present invention provides a method and an apparatus that are easy to handle, can be used on a daily basis in terms of price and maintenance cost, and can determine the sleep stage of a subject without putting a physical and mental burden on the subject. The purpose is that.
DISCLOSURE OF THE INVENTION
The present invention was created for the purpose of solving these problems in view of the above circumstances,The autonomic component sympathetic nerve component (LF) and parasympathetic nerve component (HF) are calculated from the heartbeat signal and respiratory signal extracted from the signal detected by the biological signal detection means, and the sympathetic nerve component (LF) and parasympathetic nerve component (HF). ) And parameters derived from these signals are used as index signals, and a threshold value is calculated from the following equation using an average value m and a variance value s of at least one index signal of these index signals for a predetermined time,The sleep stage is determined using this threshold value.Sleep stage determination deviceIt is.
Among the heartbeat signal or respiratory signal extracted from the signal detected by the biological signal detection means, and parameters derived from these signals, at least one signal is used as an index signal,Using an average value and a variance value of the index signal for a predetermined timeA sleep stage determination method characterized by calculating a threshold for determining a sleep stage and determining the sleep stage using the threshold.
The threshold value of the index signal can be calculated using an average value and a variance value of the index signal for a predetermined time, and since the statistical value of the latest index signal is used, the threshold value corresponding to the state of the subject It becomes possible to avoid the influence by the difference in age and physical condition.
In Claim 1, at least 1 signal can be made into an index signal from the signal strength value of the signal detected by the said biological signal detection means.
In Claim 2, in the determination of each stage of the sleep stage, determination can be made by using a logical product of determination information of at least two or more index signals among the index signals, thereby improving the reliability of the determination. Can be improved.
As a parameter serving as an index signal in claim 1The ratio of LF and HF (LF / HF) or its logarithmic value and the formula (LF / (HF + LF) or its logarithmic value can be used.
3. The signal according to claim 2, wherein the signal intensity value signal is obtained by gain control of the signal detected by the biological signal detection means.Signal set to be inversely proportional toIt can be.
Claim 1In the aboveThe parameters are the sympathetic nerve component (LF), the parasympathetic nerve component (HF), and signals derived from these signals.The signal of the difference between the long-time moving average and the short-time moving average can be obtained.
Claim 1The biological signal detection means comprises a pressure detection tube, a pressure detection sensor, and a biological signal extraction means, and can extract a biological signal from pressure fluctuations detected by the pressure detection sensor.
Claim 1The biological signal detecting means may be a heartbeat signal detecting means such as an electrocardiograph or a pulse meter and a respiratory state detecting means for detecting a respiratory rate or a respiratory state.
[Brief description of the drawings]
  FIG. 1 (A) is a block diagram showing a flow of determining a sleep stage in the sleep stage determination method of the present invention, and (B) is an XX cross-sectional view of FIG. 1 (A).
  FIG. 2 is an explanatory diagram showing the power spectral density when the sympathetic nerve is dominant.
  FIG. 3 is an explanatory diagram showing the power spectral density when the parasympathetic nerve is dominant.
  FIG. 4 is a flowchart for determining a sleep stage using one index signal.
  FIG. 5 is a flowchart for determining a sleep stage using two index signals.
  FIG. 6 is a flowchart showing processing results of determination parameters for determining the awake / REM sleep stage and the non-REM sleep stage.
  FIGS. 7A and 7B are output graphs of the index signal used for the determination of the awakening / REM sleep stage and the non-REM sleep stage.
  FIG. 8 is a graph showing the determination results of the awakening / REM sleep stage and the non-REM sleep stage.
  FIGS. 9A and 9B are output graphs of the index signal used for the determination of the awake state and the REM sleep stage.
  FIG. 10 is a graph showing the determination results of the awake state and the REM sleep stage.
  FIGS. 11A and 11B are output graphs of index signals used for the determination of the shallow non-REM sleep stage and the deep non-REM sleep stage.
  FIG. 12 is a graph showing determination results of a shallow non-REM sleep stage and a deep non-REM sleep stage.
  FIGS. 13A and 13B are graphs comparing the sleep determination result of the present embodiment with the result of the method using the conventional sleep polysomnograph (PSG).
BEST MODE FOR CARRYING OUT THE INVENTION
  Embodiments of the present invention will be described in detail with reference to the drawings.
  FIG. 1 (A) is a block diagram showing a flow for determining a sleep stage according to an embodiment of the present invention, and FIG. 1 (B) is a partial cross-sectional view of the biological signal detection means 1 shown in the block diagram. It is the side view seen from the arrow direction shown.
  A non-invasive sensor 1 shown in FIG. 1 (A) is a biological signal detecting means for detecting a minute biological signal of a sleeping subject, and a filter is used in the heartbeat signal detecting means 2 and the respiratory signal detecting means 7 from this biological signal. The respiratory signal and the heartbeat signal are extracted through the above.
  The noninvasive sensor 1 includes a pressure sensor 1a and a pressure detection tube 1b. The pressure sensor 1a is a sensor that detects minute fluctuations in pressure. In this embodiment, a low-frequency condenser microphone type is used. However, the pressure sensor 1a is not limited to this, and has an appropriate resolution and dynamic range. Anything is acceptable.
  The low-frequency condenser microphone used in the present embodiment is replaced with a general acoustic microphone that does not consider the low-frequency region, and a low-frequency region characteristic is provided by providing a chamber behind the pressure-receiving surface. And is suitable for detecting minute pressure fluctuations in the pressure detection tube 1b. In addition, it is excellent for measuring minute differential pressure, has a resolution of 0.2 Pa and a dynamic range of about 50 Pa, and is several times the performance of a fine differential pressure sensor using a ceramic that is normally used. It is suitable for detecting a minute pressure applied to the pressure detection tube 1b through a biological signal through the body surface. The frequency characteristic shows an almost flat output value between 0.1 Hz and 20 Hz, and is suitable for detecting minute biological signals such as heartbeat and respiration rate.
  As the pressure detection tube 1b, a tube having an appropriate elasticity so that the internal pressure fluctuates corresponding to the pressure fluctuation range of the biological signal is used. Further, in order to transmit the pressure change to the fine differential pressure sensor 1a at an appropriate response speed, it is necessary to appropriately select the volume of the hollow portion of the tube. When the pressure detection tube 1b cannot satisfy the appropriate elasticity and the volume of the hollow portion at the same time, the hollow portion of the pressure detection tube 1b is loaded with a core wire having an appropriate thickness over the entire length of the tube, and the volume of the hollow portion is appropriately set. Can take.
  The pressure detection tube 1b is disposed on a hard sheet 16 laid on a bed 15, and a cushion sheet 17 having elasticity is laid on the pressure detection tube 1b. A subject lies on the pressure detection tube 1b. Note that the pressure detection tube 1b may have a structure in which the position of the pressure detection tube 1b is stabilized by being incorporated in the cushion sheet 17 or the like.
  In the present embodiment, two sets of non-invasive sensors 1 are provided, one of which detects a biological signal of a part of the subject's chest and the other of which detects a part of the subject's buttocks, A biological signal is detected regardless of the posture.
  The biological signal detected by the non-invasive sensor 1 is a signal in which various vibrations emitted from a human body are mixed, and includes a heartbeat signal, a respiratory signal, a turn signal, and the like. Therefore, a biological signal such as a heartbeat signal and a respiratory signal is extracted by the biological signal detection means using means such as a filter and statistical processing. Needless to say, it is also possible to detect a turning signal.
  In the present embodiment, the heartbeat signal is extracted from the detection signal of the non-invasive sensor 1. However, the present invention is not limited to this. For example, the heartbeat signal can be obtained by wearing a dedicated heart rate monitor or detecting the pulse. It is possible to obtain information on breathing or turning over by using a microphone or imaging means.
  The heart rate is detected by the heart rate detection unit 3 from the heart rate signal detected by the biological signal detection unit 1 and the R-R interval signal calculation unit 4 detects the interval between adjacent peaks of the R wave of the heart rate signal, that is, the R-R interval. Detect the signal.
  The above-described RR interval signal is a signal having the interval of a waveform (R wave) in the vicinity where the intensity of the heartbeat signal reaches a peak as a variable, and is often used for heartbeat fluctuation analysis. The RR interval signal detected by the RR interval signal calculation means 4 is sent to the power spectrum density calculation means 5.
  Here, the power spectral density of the RR interval signal will be described.
  FIG. 2 shows the power spectral density when the sympathetic nerve is dominant, and FIG. 3 shows the power spectral density when the parasympathetic nerve is dominant. As can be seen, the power spectral density shows different aspects depending on the state of the autonomic nervous system.
  That is, remarkable maximum values appear in a band of about 0.05 to 0.15 Hz and a band of about 0.2 to 0.4 Hz. Here, the integral value signal of the maximum value band that appears on the low frequency range side of about 0.05 to 0.15 Hz is called the LF value signal, and the maximum value that appears on the high frequency range side of about 0.2 to 0.4 Hz. The integrated value signal in the value band is called an HF value signal. When the LF value is large and the HF value is small, it indicates that the sympathetic nerve is active and in tension, and when the LF value is small and the HF value is large, the parasympathetic nerve is active.
  During sleep, the heart rate decreases due to a decrease in sympathetic nerve activity that becomes active during tension and an increase in parasympathetic nerve activity that becomes active during relaxation. That is, the values of HF and LF vary significantly depending on the state of sleep depth.
  The HF / LF detection means 6 is means for detecting the above HF and LF values from the power spectral density, and the HF value and LF value detected by the HF / LF detection means 6 vary depending on the sleep stage. . This data is sent to the determination parameter generation means 13 for determining the sleep stage.
  The respiration signal detection means 7 is a means for extracting a respiration signal from the signal detected by the biological signal detection means. The respiration rate detection means 8 detects the respiration rate and the respiration interval signal calculation means 9 calculates the respiration interval. The value is used as a breath interval value signal.
  Both respiration and signals are signals that are significantly affected by the sympathetic and parasympathetic nervous systems, which are the autonomic nervous system, and have a close correlation with the sleep stage.
  Next, the processing of the signal strength of the biological signal will be described.
  The signal amplification shaping unit 10 amplifies only the main frequency band of the biological signal and sets the characteristics of the amplification circuit so as to reduce the frequency band other than the noise, and further includes a band-pass filter to further reduce noise. It is good also as a structure to reduce.
  The automatic gain control unit 11 is a so-called AGC circuit that automatically performs gain control so that the output of the signal control shaping unit 10 falls within a predetermined signal level range. The gain value at this time is converted into a signal strength calculation unit 12. Output to. For gain control, for example, the gain is set so that the amplitude of the output signal decreases when the peak value of the signal exceeds the upper threshold, and the gain is increased when the peak value falls below the lower threshold. is doing.
  The signal strength calculation unit 12 calculates the signal strength from the gain control coefficient applied to the biological signal in the automatic gain control unit 11. The gain value obtained from the above AGC circuit is set to be small when the signal size is large and large when the signal size is small. It is preferable to set a function indicating the signal intensity so as to be inversely proportional to the gain value.
  On the other hand, when the output value of the non-invasive sensor 1 continuously exceeds the upper limit of the automatic gain control within a predetermined time, it can be determined that there has been a body movement such as turning over.
  As described above, the strength of the biological signal including the body movement is considered to be closely related to the sleep state, and is used as a parameter for determining the sleep stage.
  The determination parameter generation means 13 calculates and uses parameters used for determination using the heartbeat signal or the HF value signal and the LF value signal. As the determination parameter, parameters such as a heart rate signal, an HF value signal and an LF value signal, and a signal of a ratio value of the HF value signal and the LF value signal are generated by calculation.
  In the sleep stage determination means 14, using the parameters generated by the determination parameter generation means 13, determination of wakefulness / REM sleep and non-REM sleep, determination of wakefulness and REM sleep, and deep sleep stage and shallow sleep stage of non-REM sleep The sleep stage is determined by performing the determination.
  In the non-rem determining means, it is determined whether or not the non-REM sleep state. That is, when it is confirmed that the sleep state is not a non-REM sleep state, it is found that the state is either a REM sleep state or an awake state.
  The REM sleep determination means is a means for determining the REM sleep state or the awake state after confirming that it is not in the non-REM state, that is, after confirming the REM sleep state or the awake state.
  The non-REM sleep stage is usually classified into four sleep stages from first to fourth. The first non-REM sleep stage is the shallowest, the deepest in order, and the fourth sleep stage is the deepest sleep stage. Here, the first and second sleep stages are shallow sleep stages, and the third and fourth stages are deep sleep stages. The non-REM sleep depth determination means determines whether the sleep state is shallow or deep after the non-REM sleep state is confirmed.
  With the above three-stage determination, it is possible to determine four stages of sleep, which are an awake state, a REM sleep state, a shallow non-REM sleep state, and a deep non-REM sleep stage.
  Next, an HF value signal and an LF value signal will be described as an example of generation of an index signal.
  Based on the heartbeat signal sent from the heartbeat signal detection means 2, the RR interval signal calculation means 4 detects the RR interval signal. The detected RR interval signal is Fourier expanded to determine the power spectral density of the RR interval signal.
  HF and LF are detected momentarily by the HF / LF detection means 6 from the power spectral density signal of the RR interval signal. Using these HF and LF, a sleep stage determination parameter effective for sleep stage determination can be generated.
  Next, other parameters that can be used as the index signal will be described.
  The heart rate signal employs the heart rate extracted by the heart rate detecting means 3 as it is, and is affected by changes in the sympathetic nerve and the parasympathetic nerve. Similarly, since the respiratory rate signal is also affected by changes in the sympathetic nerve and the parasympathetic nerve, it can be adopted as an index signal for determining the sleep stage. Further, the RNLF signal is obtained by taking the value of the LF value as it is. RNLFR is a ratio between the LF value and the HF value. Furthermore, RNLOG is a logarithmic value of a value indicated by LF / HF.
  Next, an actual sleep stage determination procedure will be described.
  FIG. 4 is a flowchart for determining a sleep stage using one index signal among index signals derived from a biological signal. One of the parameters generated by the determination parameter generation unit 13 is selected as an index function, and the sleep stage determination unit 14 performs determination according to this flowchart.
  Since the index signal includes many high-frequency components, that is, minute fluctuations, a high-frequency component is removed by performing a moving average process for a predetermined time. Considering the case where the index signal has a long-term fluctuation, the difference between the long-term moving average and the short-term moving average is taken to obtain a pure fluctuation value of the index signal. That is, this is to correct the long-term fluctuation of the parameter signal and extract a pure fluctuation. The number of moving average data used for this operation is 500 points for the short-term moving average and 1000 points for the long-term moving average. However, the number of moving average data is not limited to this. .
  A threshold for determining the sleep stage is set for the adopted index signal. At this time, different threshold values are set for different sleep stages.
  FIG. 5 shows an example of determining the sleep stage using two index signals. About the combination to employ | adopt, it can select according to a sleep stage. For example, in the determination of the sleep stage of the awakening / REM sleep stage or the non-REM sleep stage, if an LF value signal is adopted as one of the index signals, a good determination result can be obtained. However, the present invention is not limited to this, and suboptimal results can be obtained using other parameters.
  Combining the two signals is a means for making the determination accuracy more reliable, and depending on the purpose of use, only one parameter may be used as the index signal.
  The threshold value generation for determining the sleep stage in FIGS. 4 and 5 is performed as follows. A moving average process of the index signal is performed, and an average m and standard deviation s (variance) of data for a predetermined time are obtained for a signal obtained by correcting a long-term variation of the index signal and extracting a pure variation.
  Then, using this value, the difference signal of the index signal is binarized in order to determine the wakefulness / REM sleep state and the non-REM sleep state, and the threshold value is obtained by, for example, the following equation (A).
          α ・ m + β ・ s (I)
  Here, m is an average value, s is a standard deviation, and α and β are obtained using a large number of experimental data, and the coincidence rate between the determination of the sleep stage of this embodiment and the determination of the sleep stage by PSG is maximized. It is determined by calculating the optimum value so that
  The average value m is not limited to the arithmetic average value, and a median value or the like may be used. On the other hand, the standard deviation s is used as a parameter indicating variation, but a value indicating variation such as dispersion can be used instead.
  The constants α and β in the formula (a) differ depending on which sleep stage the parameter used as an index is used. For example, different values are used when used for determination of the awakening / REM sleep stage and non-REM sleep stage and when used for determination of shallow non-REM sleep and deep non-REM sleep.
  In the sleep stage determination method and determination apparatus according to the present invention, the threshold value corresponding to the condition at the time of the test of the subject is employed in order to use the parameter average value m and the standard deviation s to determine the parameter threshold value used for the determination. In other words, it is possible to make a determination that is not influenced by individual differences, age differences, or the test subject's state.
  FIG. 6 shows a procedure for determining each sleep stage and determining the sleep stage. In the sleep stage determination, the sleep stage determination means 14 uses the parameters generated by the determination parameter generation means 13 to determine wakefulness / REM sleep and non-REM sleep, wakefulness and REM sleep, and deep sleep among non-REM sleep. The sleep stage is determined by determining the stage and the shallow sleep stage. That is, it is determined which sleep stage it belongs to by performing the above three types of determination. The procedure first determines whether or not it is a non-REM sleep stage. At this time, when it is determined that it is non-REM sleep, it is determined whether the non-REM sleep is a shallow non-REM sleep stage or a deep non-REM sleep stage. When it is determined that it is not non-REM sleep, it is determined whether it is a REM sleep stage or an awake state. With the above three determination steps, it can be determined whether the sleep stage at each time point corresponds to one of the four stages of the awakening stage, the REM sleep stage, the shallow non-REM sleep stage, and the deep sleep stage.
  FIG. 7 shows the measurement results of the RNLF signal and the RNLOG signal as signals for determining whether the wakefulness / REM sleep state or the non-REM sleep state. FIG. 7 (A) shows the data obtained by differential processing after taking the short-term and long-term moving averages of the RNLF signal, and FIG. 7 (B) shows the data after taking the short-term and long-term moving averages of the RNLOG signal. A differentially processed signal is shown. Further, the threshold value calculated by the equation (a) is also displayed at the same time and is binarized by this threshold value. As described with reference to the flowchart of FIG. 6, whether the wakefulness / REM sleep state or the non-REM sleep state is determined is the wakefulness / REM sleep state if both signals are equal to or greater than the respective threshold values. Is determined.
  FIG. 8 shows the result of determining whether it is awake / REM sleep state or non-REM sleep according to the above procedure together with the result of the method using the conventional sleep polysomnograph (PSG). If the waveform indicates a high position, it is an awakening / REM sleep state, and if the waveform indicates a low position, it indicates non-REM sleep.
  FIG. 9 shows the parameters for determining whether the patient is awake or REM sleep, and the measurement results of the RNLF signal and the RNLFR signal. FIG. 9A shows data that has been subjected to the moving average processing of the RNLF signal, and FIG. 9B shows a signal that has been subjected to the moving average processing of the RNLFR signal. Further, the threshold value calculated by the equation (a) is also displayed at the same time, and binarized by this threshold value. As described with reference to the flowchart of FIG. 4, binarization is performed using this threshold value, and the logical product of the two signals is taken to determine whether the person is awake or REM sleep. FIG. 10 shows the result of determining whether the patient is awake or REM sleep according to the above procedure.
  FIG. 11 shows the parameters for determining whether the sleep is shallow non-REM sleep or deep non-REM sleep, and the measurement results of the RNLF signal and the RNLOG signal. FIG. 11 (A) shows the difference-processed data after taking the short-term and long-term moving averages of the RNLF signal, and FIG. 11 (B) shows the data after taking the short-term and long-term moving averages of the RNLOG signal. The differentially processed data is shown. Further, the threshold value calculated by the equation (a) is also displayed at the same time and is binarized by this threshold value. As described with reference to the flowchart of FIG. 6, after binarization using this threshold value, the logical product of the two signals is taken to determine whether the person is awake or REM sleep. FIG. 12 shows the result of determining whether it is shallow non-REM sleep or deep non-REM sleep according to the above procedure.
  FIG. 13 is a graph showing the transition of the sleep stage by integrating the above three-stage determinations determined using the sleep stage determination method and determination apparatus of the present embodiment. At the same time, the results are shown together with the results of a method using a conventional sleep polysomnograph (PSG).
  By the way, the sleep stage determination method and determination apparatus according to the present invention are a determination method and determination apparatus from the viewpoint of the behavior of the autonomic nervous system. It is the determination method and determination apparatus used. The electroencephalogram determines the sleep stage using the neural activity of the cerebral cortex and the fact that a characteristic waveform appears according to various sleep stages depending on the degree of synchronization. On the other hand, the sleep stage determination method and determination device of this method is the determination of sleep depth from the viewpoint of the behavior of the autonomic nervous system, which is the brainstem nerve activity. The behavior of the autonomic nervous system has a profound effect on heart rate and respiration Yes. As a result, the conventional sleep determination mainly using an electroencephalogram and the sleep determination from the autonomic nervous system are slightly different in time. Therefore, it should be noted that the coincidence rate of the determination results described below is not the temporal coincidence rate, but the coincidence rate of the ratios of the sleep depth stages of the day.
  FIG. 13 (A) shows the result of comparison between the awake state, the REM sleep stage, and the non-REM sleep stage, and the determination result shows a 93.3% agreement rate with the method using the conventional sleep polysomnograph (PSG). ing. FIG. 13B is a result of comparison in four stages of awake state, REM sleep stage, shallow non-REM sleep stage, and deep non-REM sleep stage, and the determination result is a method using a conventional sleep polysomnograph (PSG) and 90. .3% match rate. For this reason, the sleep stage determination apparatus of the present invention has a determination accuracy with no practical problem.
  In this embodiment, the detection method capable of detecting a biological signal of a subject non-invasively is adopted as a method of detecting pressure fluctuation by combining a tube and a pressure sensor. However, the present invention is not limited to this. Any means may be used. For example, detection means attached to a part of the body such as an electrocardiograph and a pulse meter can be used as long as they do not disturb sleep.
[Industrial applicability]
  It is well known that it is effective to know the daily health status of the subject by knowing the sleep status and its quality, but it can be easily used for the purpose of personal health management. The current situation is that no device is found.
  The sleep stage determination method and determination apparatus of the present invention detect a heartbeat signal by using an appropriate detection means, and determine the sleep stage of the subject by calculating the output of the heartbeat signal. Since the power spectral density obtained from the RR interval signal detected from the above is a good index indicating the state of the autonomic nerve, it is public as a sleep stage index during sleep and has high reliability.
  Further, the sleep stage determination method and determination apparatus of the present invention can determine the sleep stage as long as even a heartbeat signal can be detected, and the cost for use and the cost for maintenance are low and are used on a daily basis. It is possible to provide a suitable sleep stage determination apparatus.

Claims (8)

Calculating the sympathetic nerve component (LF) and the parasympathetic nerve component (HF) of the autonomic nerve component from the heartbeat signal and the respiratory signal extracted from the signal detected by the biological signal detection means;
The sympathetic nerve component (LF) and the parasympathetic nerve component (HF) and parameters derived from these signals are used as index signals,
A threshold value is calculated from the following equation using an average value m and a variance value s of a predetermined time of at least one of these indicator signals:
A sleep stage determination apparatus characterized by determining a sleep stage using this threshold value.
Threshold = α · m + β · s
However, α and β are coefficients determined according to the sleep stage. The sleep stage determination device according to claim 1, wherein at least one signal is used as an index signal from among signal intensity values of signals detected by the biological signal detection means. The sleep stage determination device according to claim 2, wherein the determination of each stage of the sleep stage is performed using a logical product of determination information of at least two or more index signals among the index signals. 2. The sleep stage determination according to claim 1, wherein the parameter is at least one of the ratio of LF and HF (LF / HF) or a logarithmic value thereof and an expression (LF / (HF + LF) or a logarithmic value thereof. apparatus. The parameter claim 1, characterized in that a signal of the difference between the long moving average and a short moving average of the derived signal sympathetic component and (LF) from the parasympathetic component (HF) and these signals The sleep stage determination device described. 3. The sleep stage determination apparatus according to claim 2, wherein the signal intensity value signal is a signal set to be inversely proportional to a coefficient obtained by gain control of the signal detected by the biological signal detection means. The sleep stage determination according to claim 1, wherein the biological signal detection means includes a pressure detection tube, a pressure detection sensor, and a biological signal extraction means, and extracts a biological signal from pressure fluctuation detected by the pressure detection sensor. apparatus. 2. The sleep stage determination device according to claim 1, wherein the biological signal detection means is a heart rate signal detection means such as an electrocardiograph or a pulse meter and a respiratory state detection means for detecting a respiratory rate or a respiratory state.

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