JP2017164397A - Sleep stage determination method, sleep stage determination device, and sleep stage determination program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To further easily and precisely estimate a sleep stage on the basis of acquired body data.SOLUTION: The device acquires heart beat data of a subject, converts the heart beat data to the components of a frequency region, extracts frequency components in a range over periods of 2.5 seconds to 150 minutes from data of the frequency region to convert it to data of a time region, and determines as a REM sleep stage a region exceeding a predetermined threshold out of the data of the time region.SELECTED DRAWING: Figure 6

Description

  The present invention relates to a technique for determining a sleep stage.

  In the Rechtschaffen & Kales (hereinafter referred to as “R & K method”) method, which is an international standard for sleep stage determination, sleep states are classified into six stages as shown in FIG. Awakening stage (indicated as “W”), REM (Rapid Eye Movement) sleeping stage (indicated as “R”), non-REM sleeping stages 1 through 4 (“1”, “2”, “3” and “4”), and the non-REM stage 4 is the deepest sleep. Estimating the sleep stage is useful not only for medical diagnosis of dementia and the like, but also in the field of nursing care. For example, by planning the REM sleep cycle, it is possible to predict the behavior management of elderly people and the occurrence of sleep disorders and the like.

  The sleep stage data in Fig. 1 was created based on the experience of specialists by wearing a special instrument on the subject and measuring brain waves (EEG: Electroencephalogram), eye movements (EOG: Electro-oculogram), etc. It is. This sleep stage data is precise data mainly used for medical judgment such as mental illness. When the sleep stage determination is applied to a nursing care site, it is unrealistic to have an elderly person or a handicapped person wear an instrument such as an electrode all night. In addition, since it does not predict the occurrence of sleep disorders in the cared person, it is not necessary to perform precise sleep analysis, but it is desirable to estimate the sleep stage as accurately as possible.

  On the other hand, a technique for acquiring body data without restriction and determining a sleep stage has been proposed (for example, see Non-Patent Document 1). This method measures body data such as heartbeat and body movement using an unconstrained air mattress type sensor to which a piezoelectric film or the like is applied, and estimates a sleep stage from the obtained data. Since the subject does not wear an instrument such as an electrode, body data can be acquired without applying stress to the subject.

  FIG. 2 is a diagram illustrating a conventional unconstrained sleep stage determination method. The heart rate data is extracted from the body information obtained by the unconstrained sensor to determine the sleep stage. This is based on the general view that the lower the heart rate, the deeper the sleep, and the higher the heart rate, the closer to awakening. In this conventional method, heart rate data is converted into frequency domain data by FFT (Fast Fourier Transform), and a medium frequency component of a period from half the period of the peak value of the power spectrum to 135 minutes is extracted by a band pass filter. . The extracted intermediate frequency component is subjected to IFFT (inverse Fourier transform) to return to the time domain heart rate data, and then discretized in six stages. The method shown in FIG. 2 is referred to as a “conventional method”.

  Moreover, in order to estimate a sleep stage from a heart rate, the filter for taking out the data of an appropriate frequency component from biometric data is proposed (for example, refer patent document 1).

JP 2014-239789 A

T. Watanabe and K. Watanabe, "Non-contact Method for Sleep Stage Estimation" IEEE Transaction on Biomedical Engineering, No. 10, Vol. 51, pp. 1735-1748, 2004

  Although the conventional method of FIG. 2 can grasp a large rhythm (cycle) of sleep, it cannot determine a sudden transition to an arousal state or a fine heartbeat vibration that is characteristic of REM sleep.

  In addition, although the sleep cycle (heartbeat cycle) itself varies, the sleep stage is estimated while being fixed to an average period, and thus there is a possibility that a deviation from the actual sleep stage occurs.

  When the sleep stage determination is applied to a care site for an elderly person or the like, it is desirable that the transition to the REM sleep or wakefulness state can be determined as accurately as possible from the data acquired without restriction.

  Then, this invention makes it a subject to provide the method and structure which can estimate a sleep stage simply and with high precision from the acquired body data.

  In order to solve the above-mentioned problem, in the present invention, the estimation accuracy is improved by estimating the sleep stage using the body data optimal for the estimation of the sleep stage according to the sleep stage.

In the first aspect of the present invention, the sleep stage determination device comprises:
A data acquisition unit for acquiring heart rate data of the subject;
A determination unit that estimates a REM sleep stage from the heartbeat data, wherein the heartbeat data is converted into a frequency domain component, and a period of 2.5 seconds to 150 minutes related to REM sleep is obtained from the frequency domain data. And a determination unit that extracts a period component in a range over time and converts it into time domain data, and determines a region exceeding a predetermined threshold among the time domain data as a REM sleep stage.

In the second aspect of the present invention, the sleep stage determination device comprises:
A data acquisition unit for acquiring the body movement data of the subject;
A total average and a standard deviation of the body movement data are calculated, a value obtained by multiplying the standard deviation by a predetermined coefficient is added to the total average as a threshold value, and an area exceeding the threshold value is determined as a wakefulness stage. A determination unit to perform,
Have

In the third aspect of the present invention, the sleep stage determination device comprises:
A data acquisition unit for acquiring respiration data of the subject;
Based on the respiration data, calculate the overall average and standard deviation of the respiration rate per unit time, set a value obtained by adding a value obtained by multiplying the standard deviation by a predetermined coefficient to the overall average as a threshold, A determination unit that determines a region below a threshold as a non-REM sleep stage;
Have

In the fourth aspect of the present invention, the sleep stage determination device comprises:
A data acquisition unit for acquiring physical data of the subject;
Heart rate data, respiratory data, and body motion data are separated from the body data, determination of a non-REM sleep stage using the respiratory data, determination of a REM sleep stage using the heart rate data, and the body motion data A determination unit for determining the sleep stage by individually or sequentially performing the determination of the arousal stage using
Have

  The sleep stage can be estimated easily and with higher accuracy from the measured body data. In addition, individual estimation is possible for each stage of sleep.

It is a figure which shows the sleep stage by R & K international classification. It is a figure which shows the conventional sleep stage determination method using heart rate data. It is a figure explaining the basic concept of the sleep stage determination method of this invention. It is a flowchart of the sleep stage determination method of embodiment. It is a figure explaining determination of a non-REM sleep stage. It is a figure explaining determination of a REM sleep stage. It is a figure explaining determination of an awakening stage. It is a figure explaining the determination result which totaled determination of each step. It is a figure which shows the rem determination result of embodiment compared with the conventional method. It is a figure which shows the arousal determination result of embodiment compared with the conventional method. It is a figure which shows the coincidence degree of the total determination result of embodiment, and the determination by R & K method. It is a figure which shows the coincidence rate of the sleep stage determination result and R & K method of embodiment compared with the coincidence rate of the conventional method and the R & K method. It is a figure which shows the coincidence rate of the sleep stage determination result and R & K method of embodiment compared with the coincidence rate of the conventional method and the R & K method. It is a schematic block diagram of the sleep stage determination apparatus of embodiment.

  FIG. 3 is a diagram for explaining the basic concept of sleep stage determination according to the present invention. In the embodiment, the estimation accuracy is improved by using body data most suitable for estimating the sleep stage according to the sleep stage. Specifically, each sleep stage is determined using body movement data for estimating the awakening stage, heartbeat data for estimating the REM sleep stage, and respiratory data for estimating the non-REM sleep stage. This is based on the following findings.

  The heart rate is the same or slightly higher in the REM sleep stage than in the non-REM sleep stage, and lower than in the awake stage. The fluctuation of the heartbeat becomes extremely large in the REM sleep stage as compared with the non-REM sleep stage, and is equal to or slightly larger than the wakefulness stage. That is, in REM sleep, the heart rate increases to the same level as when awakened, and heart rate fluctuations are most noticeable. Therefore, heart rate and heart rate fluctuations are used to determine the REM sleep stage.

  The body movement is smaller as the sleep is deeper and larger and more frequent as the sleep is shallower. Therefore, body motion data is used for determination of the arousal stage.

  Since respiration is stable in deep sleep (non-REM sleep) that does not involve rapid eye movement, the standard deviation (dispersion) of respiration is reduced. Therefore, the non-REM sleep stage is estimated using the respiratory data.

  With such an approach, it is possible to estimate individual sleep stages individually, and it is also possible to perform comprehensive estimation combining any sleep stage estimation. In addition, the measurement of the present invention can be approximated to the estimation result of the R & K method using brain wave information or the like, regardless of whether the measurement is constrained or unconstrained.

  FIG. 4 is a flowchart of the sleep stage determination method according to the embodiment. First, body data including a subject's heartbeat data, body movement data, and respiration data is acquired by a biometric sensor (S101). Respiratory data, heart rate data, and body movement data are separated from the acquired body data (S102). The output of the biosensor includes many types of waveform data, which are separated into data by a technique such as frequency separation.

  Next, each sleep stage is determined. For example, one or more first threshold values are set in the respiratory data, and the non-REM sleep stage is determined by comparing the threshold values (S103). Details of the estimation of non-REM sleep based on respiratory data will be described later. Also, a second threshold value is set for the heartbeat data, and the REM sleep stage is determined by comparing the threshold values (S104). Further, a third threshold value is set in the body motion data, and the awakening stage is determined by threshold determination (S105). Steps S103 to S105 may be executed sequentially in this order, or if the priority is reflected in the determination result in the order of determination of the awakening stage, determination of the REM sleep stage, and determination of the non-REM sleep stage, the determination itself May be implemented individually in parallel or in random order. For example, if the determination of the wakefulness stage and the determination of the REM sleep stage compete, if the determination of the wakefulness stage is reflected in the determination result in preference to the determination of the REM sleep stage, the same result regardless of the determination order Can be obtained. Similarly, in the case of the determination of the awakening stage and the determination of the non-REM sleep stage, the determination of the awakening stage is reflected with priority over the determination of the non-REM sleep stage. When the determination of the REM sleep stage and the determination of the non-REM sleep stage compete, the determination of the REM sleep stage is reflected with priority over the determination of the non-REM sleep stage. Thereby, the same result can be obtained regardless of the order of determination.

  The determination result is output as data (S106). As a data output format, data of all sleep stages described in time series as shown in FIG. 1 may be output, or notification such as an alarm may be output preferentially when arousal determination is made. Good. Further, when the REM determination is made, an alarm of a different type from the awakening determination may be generated.

  FIG. 5 is a diagram for explaining the determination of the non-REM sleep stage. In the embodiment, a non-REM sleep stage is estimated using a value obtained by dividing the variance (or standard deviation) of respiration by an average value as a coefficient of variation (variation coefficient = standard deviation / average). First, respiration data is separated from body data acquired by a biosensor (procedure 1). From the separated respiration data, for example, every 5 minutes, the standard deviation and the average value of the respiration rate per unit time are calculated to obtain a 5-minute variation coefficient. The coefficient of variation indicates the deviation from the average respiratory rate. Divide the standard deviation by the average value to reduce variability among subjects. The time for obtaining the variation coefficient is not limited to 5 minutes. Since it is only necessary to have about 100 breaths to calculate the coefficient of variation, the coefficient of variation may be calculated every 4 minutes or every 8 minutes depending on the subject.

  Next, at least one first threshold value for non-rem determination is set using a variation coefficient. In the example of FIG. 2, two threshold values TH1-a and TH1-b are set for non-rem determination. TH1-a is a value obtained by multiplying the overall average of the coefficient of variation by 0.6, and TH1-b is a value obtained by multiplying the overall average of the coefficient of variation by 0.2. Coefficients 0.6 and 0.2 multiplied by the overall average of the coefficient of variation for setting the threshold are experimentally obtained values that are the values that maximize the correct answer rate.

  The region where the 5-minute variation coefficient exceeds TH1-a is determined as non-REM sleep stage 1 (shallow sleep), and the region where the 5-minute variation coefficient is TH1-a or less is determined as non-REM sleep stage 2-4 (deep sleep). The region exceeding TH1-a includes the REM sleep stage and the wakefulness stage in addition to the non-REM sleep stage 1, but the process of FIG. 5 is a process for appropriately estimating the non-REM sleep stage. Treat one or more as non-rem 1.

  The second threshold TH1-b may be used to distinguish between the non-REM sleep stage 2 and the non-REM sleep stage 3 or lower. A region exceeding TH1-b and not more than TH1-a is estimated as non-REM sleep stage 2. The region below TH1-b is estimated as non-REM sleep stage 3 or 4.

  By this method, it is possible to accurately estimate whether at least the subject is in a shallow sleep state of the non-REM sleep stage 1 or higher or a deep sleep state of the non-REM sleep stage 2 or lower. Further, by setting two or more thresholds for non-REM determination, it is possible to distinguish whether the subject is in non-REM sleep stage 2 or 3 or less. Non-REM sleep stages 3 and 4 are both deep sleep, and there is a classification method that treats these two as the same stage. Therefore, the estimation of “non-REM sleep stage 3 or less” is sufficient, but if necessary, a third threshold, for example, TH1-c obtained by multiplying the overall variation coefficient by 0.1 is set to set the non-REM sleep stage 3 And 4 may be distinguished.

  In the above-described discrimination method using respiratory data, the non-REM sleep stage can be accurately estimated, but the non-REM sleep stage and the REM sleep stage are not distinguished. Therefore, the REM sleep stage and the non-REM sleep stage are distinguished by separately using heart rate data. The REM sleep stage can be individually determined from the heart rate data. However, when estimating using the above-described non-REM sleep estimation result, the REM sleep stage is selected from a wide frame of “non-REM sleep stage 1 or higher”. You may call it "REM correction" in the meaning of specifying.

  FIG. 6 is a diagram for explaining determination of the REM sleep stage. In the embodiment, the REM sleep stage is estimated using fluctuation of the heart rate. First, heart rate data is separated from body data acquired by a biosensor (procedure 1). The heartbeat data changes slowly with a large period of about 90 minutes, but a large number of fine vibration components are included in the large period. Next, FFT is applied to the separated heartbeat data to convert it into frequency domain components, a predetermined periodic component is extracted by a band-pass filter, and then returned to time domain data by IFFT (procedure 2).

  As a feature of the embodiment, periodic components from 2.5 seconds to 135 minutes are extracted from the frequency components obtained by FFT. This extraction method is different from the conventional method of Patent Document 1 and other general sleep stage estimation in which a period component of 26 to 135 minutes is extracted. The conventional method does not consider the fine fluctuations included in the sleep cycle itself. On the other hand, in the embodiment, in order to take into account fluctuations (fluctuations) in a fine cycle of 2.5 to 25 minutes that occur in the heartbeat, a cycle component from 2.5 to 135 minutes is selected from the FFT components. And apply IFFT. As a result, a wave representing heartbeat fluctuation is restored in the time domain (procedure 3). The extraction of the periodic component is not limited to a period from 2.5 seconds to 135 minutes. Since there are individual differences in heart rate fluctuation, the extraction range may be adjusted within a period of 2 seconds to 150 minutes depending on the subject. Since it is sufficient that a component having a fine vibration period of 25 minutes or less to a component having a long change period of 120 minutes or more is included, a periodic component from 2 seconds to 120 minutes may be extracted, and from 5 seconds to 150 minutes. These periodic components may be extracted.

  Next, an average of 5 minutes is calculated for the waveform data after IFFT, and a threshold value TH2 for determining the REM sleep stage is set (procedure 4). The threshold value TH2 set here is a value obtained by adding a value obtained by multiplying the average of the entire data after IFFT by 0.2 to the standard deviation (overall average + standard deviation × 0.2). The coefficient 0.2 is a value obtained experimentally, and is a value that maximizes the correct answer rate.

  A section exceeding the threshold TH2 is determined as a REM sleep stage (procedure 5). The section indicated by the bold line is the section determined as the REM sleep stage. When the non-rem determination data acquired in FIG. 5 is used as background data, the section of the REM sleep stage is specified from the sections estimated as the non-REM sleep stage 1. In this sense, the determination result obtained in step 5 of FIG. 6 may be referred to as a sleep stage determination result after “REM correction”.

  FIG. 7 is a diagram for explaining determination of the arousal stage. The arousal stage is estimated based on body movement data. First, body motion data is separated from body data acquired by a biosensor (procedure 1). An average value every minute is calculated from the separated body motion data, and a threshold value TH3 is set (procedure 2). The threshold value TH3 is a value obtained by adding a value obtained by multiplying the average of the whole sleep data of overnight sleep by 0.2 to the standard deviation (overall average + standard deviation × 0.2). The average value calculation interval is not limited to one minute, and may be every two minutes or every three minutes. However, since the magnitude and frequency of body movement increase at the awakening stage, it is desirable to set an averaging time shorter than the averaging time of heartbeat data and respiratory data.

  A section exceeding the threshold value TH3 is determined as the awakening stage (procedure 3). A section indicated by a black circle is a section determined as the awakening stage. 6 is used as background data, among the sections estimated as the non-REM sleep stage 1, the section of the awakening stage including the section estimated as the REM sleep stage is specified. In this sense, the determination result obtained in step 3 of FIG. 7 may be referred to as “WAKE correction”. When both the non-REM sleep stage determination data of FIG. 5 and the REM determination data of FIG. 6 are used as background data, the data that comprehensively estimates the sleep stage as shown in the lowermost diagram of FIG. Can be generated.

  FIG. 8 is a diagram illustrating a usage mode of non-rem determination, rem determination, and arousal determination according to the embodiment. As described with reference to FIG. 4, since the estimation using the optimum body data is performed according to the sleep stage, the determination is prioritized and reflected in the determination result in the order of wakefulness determination, rem determination, and non-rem determination. For example, the determination of the non-REM sleep stage, the determination of the REM sleep stage, and the determination of the awakening stage may be performed individually or sequentially. The same result can be obtained regardless of the order of determination.

  In the example of FIG. 8, first, the non-REM sleep stage is estimated using the thresholds TH1-a and TH1-b (processing (a)). By this estimation, a shallow sleep section including the non-REM sleep stage 1, the REM sleep stage, and the awakening stage is distinguished from a deep sleep section of the non-REM sleep stages 2 to 4. For example, when there is a possibility that the cared person may get up in the night and hesitate, it is possible to prevent wrinkles in advance by checking the section exceeding TH1-a. In addition, the timing of excretion care can be specified more accurately at a care facility or the like.

  Based on the result of the process (a), the REM sleep stage may be narrowed down more accurately (process (b)). In the embodiment, since the REM sleep stage is estimated based on the fluctuation of the heart rate, the REM sleep stage can be estimated more accurately as compared with the conventional example.

  Based on the result of the process (a), the awakening stage may be estimated (process (c)). Furthermore, after the process (b), the process (c) is performed, and the data obtained by the process (b) and the process (c) are added together to finally generate the data of the process (d). Good.

  FIG. 9 is a diagram illustrating the effect of REM determination by the method of the embodiment. The gray line is the precise sleep determination result by the R & K method. The aim of the embodiment is to make the estimation result of the sleep stage from the body data acquired by the sensor as close as possible to the estimation result by the R & K method. As a comparative example, the rem determination result by the conventional method (refer FIG. 2) of patent document 1 is shown with a dotted line. In the figure, the portion indicated by the bold line is the estimation result by the rem determination of the embodiment.

  In the conventional method, since the fine fluctuation component included in the sleep cycle is not considered, the REM sleep stage is not accurately estimated. On the other hand, since the method of the embodiment incorporates the fine frequency fluctuation component included in the heartbeat change period together with the period of the heartbeat change, it can be close to the estimation result of the REM sleep stage by the R & K method.

  FIG. 10 is a diagram illustrating the effect of arousal determination by the method of the embodiment. The gray line is the sleep determination result by the R & K method. As a comparative example, the awakening determination result by the conventional method (see FIG. 2) of Patent Document 1 is indicated by a dotted line. In the figure, the portion indicated by the bold line is the estimation result by the arousal determination (also referred to as WAKE correction) of the embodiment. In the conventional method, since the sleep stage is mainly determined using heart rate data, the REM sleep stage and the awakening stage are not clearly distinguished. On the other hand, in the method of the embodiment using body motion data for arousal determination, the closeness to the determination result by the R & K method is high.

  FIG. 11 is a diagram showing a matching rate of the technique of the embodiment in the entire sleep stage with respect to the R & K method. This data is obtained from a comparison result obtained by performing sleep stage estimation by the R & K method and sleep stage estimation by the method of the embodiment for one subject. In the figure, the dotted line is the estimation result by the R & K method, and the solid line is the estimation result by the method of the embodiment. A portion indicated by a thick solid line is a section in which complete coincidence with the R & K method is obtained. The oblique line is a section where the estimation result of the embodiment is shifted by one stage from the sleep stage estimation by the R & K method.

  According to the comparison between the same subjects, the perfect match is 57.8%, and the match rate focusing on the estimation of the REM sleep stage is 60.9%. If one-stage deviation is allowed, the coincidence rate is 86.4%.

  FIG. 12 is a diagram showing the coincidence rate with the R & K method in the average data of 26 men and women from the 20s to the 70s. 9 to 11 compare using estimated data for one subject, whereas FIG. 12 uses average estimated data of a plurality of subjects whose age and gender are different over a wide range. As a comparative example, the concordance rate between the conventional method of Patent Document 1 and the R & K method is shown. The perfect match rate of the sleep stage estimation results is 32% in the conventional method, but is improved to 42% in the method of the embodiment.

  FIG. 13 shows the coincidence rate for each sleep stage in the same average data of 26 people as FIG. As a feature of the embodiment, the coincidence rate of the awakening stage is improved by 27%. This is because body motion data is used for determination of the awakening stage. Moreover, the agreement rate of the REM sleep stage is improved by 6%. Conventionally, estimation of the REM sleep stage has been difficult, and the 6% increase effect is extremely large. Furthermore, the coincidence rate of non-rem stage 2 is also improved by 7%. By improving the estimation accuracy in the non-REM stage 2, for example, urination care can be performed during deep sleep of an elderly person, so that appropriate care with priority on sleep can be performed.

  FIG. 14 is a schematic configuration diagram of the sleep stage estimation apparatus 10. The sleep stage estimation device 10 includes a processor 11, a user interface 16, a memory 17, a communication interface 18, and a drive 19. The user interface 16 includes an input operation unit such as a keyboard, a mouse, and a touch panel, and a display / output unit such as a monitor display and a speaker. The memory 17 includes a ROM (Read Only Memory), a RAM (Random Access Memory), a hard disk drive, and the like. The communication interface 18 communicates with other devices and sensors via a network. The drive 19 drives them when a removable storage medium (not shown) is inserted.

  The processor 11 includes a data acquisition unit 12, a determination unit 20, and a synthesis unit 21, and the determination unit 20 includes a non-rem determination unit 13, a rem determination unit 14, and an arousal determination unit 15. The data acquisition unit 12 acquires body data input from an external biosensor via the communication interface 18. The non-rem determining unit 13 separates the respiratory data from the body data acquired by the data acquiring unit 12, and calculates a coefficient of variation of the respiratory rate every predetermined time. The coefficient of variation is calculated by dividing the standard deviation of the respiratory rate per unit time by the average value (variation coefficient = standard deviation / average). Further, the first average threshold for non-REM determination is set by multiplying the overall average value of the coefficient of variation by a predetermined coefficient (for example, 0.6), and the area below the first threshold is estimated as non-REM sleep stages 2 to 4. To do.

  The REM determination unit 14 separates the heart rate data from the body data acquired by the data acquisition unit 12 and converts it into frequency domain data. The REM determination unit 14 converts a period component (for example, 2) from a vibration period of 25 minutes or less to a period of 120 minutes or more. Extract periodic components from 5 seconds to 135 minutes). The extracted periodic component is returned to the heartbeat data in the time domain and averaged every predetermined time. Next, an overall average value and a standard deviation are obtained, and a value obtained by multiplying the standard deviation by a predetermined coefficient (for example, 0.2) is added to the overall average value to set a second threshold for REM determination. A region exceeding the second threshold is estimated as a REM sleep stage.

  The awakening determination unit 15 separates the body motion data from the body data acquired by the data acquisition unit 12, and averages the body motion every predetermined time (for example, every minute). Next, the average value and standard deviation of the whole body movement are obtained, and a value obtained by multiplying the standard deviation by a predetermined coefficient (for example, 0.2) is added to the overall average value to set a third threshold value for determining arousal. To do. A region exceeding the third threshold is estimated as an arousal stage.

  The combining unit 21 may combine the determination results of the non-rem determining unit 13, the rem determining unit 14, and the awakening determining unit 15 and output the result as one sleep stage determination data. The REM determination unit 14 may add the determination result of the REM sleep stage using the determination result of the non-REM determination unit 13 as background data (REM correction). The awakening determination unit 15 may add the determination result of the awakening stage to the determination result of the non-rem determination unit 13 or the determination results of the non-rem determination unit 13 and the rem determination unit 14 (WAKE correction).

  The determination results of the non-rem determination unit 13, the rem determination unit 14, and the arousal determination unit 15 may be individually output from the user interface 16, or may be output after being combined by the combining unit 21.

When the operations of the non-rem determination unit 13, the rem determination unit 14, the awakening determination unit 15, and the synthesis unit 21 are realized by a computer program, the processor 11 executes a sleep stage determination program stored in the memory 17. The sleep stage determination program may be installed via the communication interface 18 or may be installed from a removable storage medium via the drive 19. In this case, the program
A procedure for obtaining heart rate data of the subject;
Converting the heartbeat data into frequency domain components;
A procedure for extracting periodic components in a range over a period of 2.5 seconds to 150 minutes from the frequency domain data and converting them into time domain data;
In the time domain data, a procedure for determining a region exceeding a predetermined threshold as a REM sleep stage is executed.

Or on your computer,
A procedure for acquiring body movement data of the subject;
Calculating the overall average and standard deviation of the body movement data;
A procedure for setting a value obtained by adding a value obtained by multiplying the standard deviation by a predetermined coefficient to the overall average as a threshold;
A procedure for determining a region exceeding the threshold as an awakening stage;
May be executed.

Also on the computer,
A procedure for obtaining the breathing data of the subject;
A procedure for calculating the overall average and standard deviation of the respiration rate per unit time based on the respiration data;
A procedure for setting a value obtained by adding a value obtained by multiplying the standard deviation by a predetermined coefficient to the overall average as a threshold;
A procedure for determining a region below the threshold as a non-REM sleep stage;
May be executed.

  With the configuration and method described above, the sleep stage can be estimated more accurately from the measured body data by a simple method.

DESCRIPTION OF SYMBOLS 10 Sleep stage determination apparatus 11 Processor 12 Data acquisition part 13 Non rem determination part 14 Rem determination part 15 Arousal determination part 16 User interface 17 Memory 18 Communication interface 19 Drive 20 Determination part 21 Synthesis | combination part

Claims (14)

A data acquisition unit for acquiring heart rate data of the subject;
A determination unit for estimating a REM sleep stage from the heartbeat data, wherein the heartbeat data is converted into a frequency domain component, and a periodic component in a range extending from 2.5 seconds to 150 minutes from the frequency domain data; A sleep stage determination apparatus comprising: a determination unit that extracts and converts to time domain data, and determines a region exceeding a predetermined threshold among the time domain data as a REM sleep stage.   The determination unit calculates an overall average and a standard deviation of the data in the time domain, and sets a value obtained by adding a value obtained by multiplying the standard deviation by a predetermined coefficient to the overall average as the threshold value. The sleep stage determination apparatus according to claim 1.   The sleep stage determination apparatus according to claim 1, wherein the determination unit calculates the overall average and the standard deviation after averaging the data in the time domain every predetermined time. A data acquisition unit for acquiring the body movement data of the subject;
A total average and a standard deviation of the body movement data are calculated, a value obtained by multiplying the standard deviation by a predetermined coefficient is added to the total average as a threshold value, and an area exceeding the threshold value is determined as a wakefulness stage. A determination unit to perform,
A sleep stage determination device characterized by comprising: A data acquisition unit for acquiring respiration data of the subject;
Based on the respiration data, calculate the overall average and standard deviation of the respiration rate per unit time, set a value obtained by adding a value obtained by multiplying the standard deviation by a predetermined coefficient to the overall average as a threshold, A determination unit that determines a region below a threshold as a non-REM sleep stage;
A sleep stage determination device characterized by comprising: A data acquisition unit for acquiring body data of the subject measured without restriction;
Separating heart rate data, respiratory data, and body movement data from the body data,
Determination of the sleep stage by performing the determination of the non-REM sleep stage using the respiratory data, the determination of the REM sleep stage using the heartbeat data, and the determination of the arousal stage using the body motion data individually or sequentially. And
A sleep stage determination device characterized by comprising:   The determination unit determines a non-REM sleep stage using the respiratory data, determines a REM sleep stage using the heartbeat data based on a determination result of the non-REM sleep stage, and based on a determination result of the REM sleep stage The sleep stage determination device according to claim 6, wherein the awakening stage is determined using the body movement data.   8. The sleep according to claim 6, wherein the determination unit prioritizes determination in the order of determination of the awakening stage, determination of the REM sleep stage, and determination of the non-REM sleep stage, and reflects the determination result in the determination result. Stage determination device. Obtain heart rate data of the subject,
Converting the heartbeat data into frequency domain components;
From the frequency domain data, a periodic component in a range ranging from 2.5 seconds to 150 minutes is extracted and converted to time domain data,
Of the data in the time domain, determine a region exceeding a predetermined threshold as a REM sleep stage,
A sleep stage determination method characterized by that. Obtain the subject's body movement data,
Calculate the overall average and standard deviation of the body movement data,
A value obtained by multiplying the standard deviation by a predetermined coefficient is added to the overall average as a threshold value.
A region exceeding the threshold is determined as an arousal stage,
A sleep stage determination method characterized by that. Obtain the breathing data of the subject,
Based on the respiratory data, calculate the overall average and standard deviation of the respiratory rate per unit time,
A value obtained by multiplying the standard deviation by a predetermined coefficient is added to the overall average as a threshold value.
The area below the threshold is determined as a non-REM sleep stage,
A sleep stage determination method characterized by that. On the computer,
A procedure for obtaining heart rate data of the subject;
Converting the heartbeat data into frequency domain components;
A procedure for extracting periodic components in a range over a period of 2.5 seconds to 150 minutes from the frequency domain data and converting them into time domain data;
Among the time domain data, a procedure for determining a region exceeding a predetermined threshold as a REM sleep stage;
A sleep stage determination program for executing On the computer,
A procedure for acquiring body movement data of the subject;
Calculating the overall average and standard deviation of the body movement data;
A procedure for setting a value obtained by adding a value obtained by multiplying the standard deviation by a predetermined coefficient to the overall average as a threshold;
A procedure for determining a region exceeding the threshold as an awakening stage;
A sleep stage determination program for executing On the computer,
A procedure for obtaining the breathing data of the subject;
A procedure for calculating the overall average and standard deviation of the respiration rate per unit time based on the respiration data;
A procedure for setting a value obtained by adding a value obtained by multiplying the standard deviation by a predetermined coefficient to the overall average as a threshold;
A procedure for determining a region below the threshold as a non-REM sleep stage;
A sleep stage determination program for executing

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