JP2015097636A - Sleep stage determination apparatus, sleep stage determination program, and sleep stage determination method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a sleep stage determination apparatus that determines a sleep stage of a subject with a high degree of precision.SOLUTION: A sleep stage determination apparatus includes: wakefulness determination means C9 for determining whether or not a subject is awake based on the frequency of body motions of a subject; breathing interval extraction means C12 for extracting a breathing interval of the subject based on the vibration detected by a detection member SN1; fractal dimension calculation means C17 for calculation a fractal dimension based on deviation accumulation (y(k)) of the breathing interval and a trend of changes in breathing (y(k)); and sleep stage determination means C20 for determining a sleep stage of the subject based on the fractal dimension and a threshold for the sleep stage determination when it is determined by the wakefulness determination means C9 that the subject is asleep.

Description

  The present invention relates to a sleep stage determination apparatus, a sleep stage determination program, and a sleep stage determination method for determining a sleep stage of a subject.

Traditionally, in order to investigate human sleep stages, sleep polysomnography (PSG) is used to measure and determine brain waves, electrocardiograms, and electromyograms during sleep. The measurement results are classified into 6 stages of non-REM sleep consisting of 4 stages, REM sleep and awakening. In general, non-REM sleep for brain rest and REM sleep, which is a preparation stage for awakening from rest, are repeated in a cycle of about 1 and a half hours, and 3 to 4 sleep cycles per night Appears. For humans, it is said that both deep non-REM sleep is sufficient to fully rest the brain, and that REM sleep appears periodically to wake up refreshed from sleep.
Many modern people have trouble sleeping. In addition, the quality of sleep depends on various factors such as mental and physical stress levels, the presence of daytime exercise, and bathing just before going to bed. It is difficult to judge the quality. Therefore, in order to ensure good quality sleep, it is desired to measure the depth of sleep quantitatively and manage it on a daily basis.

However, this detection of sleep depth and sleep cycle generally requires a large number of electrodes to be attached to the human body in order to collect brain waves, etc., and preparations are very troublesome, and there are also problems that prevent sleep. I couldn't measure it at home.
In order to deal with such problems, the techniques described in Patent Documents 1 to 8 are known as methods for detecting a sleep stage and a sleep cycle without using an electroencephalogram, an electromyogram or an electrooculogram.

In Patent Document 1 (Japanese Patent Application Laid-Open No. 2002-219116), the vibration sensor (2) is arranged under the mattress (1), and from the detection signal of the vibration sensor (2), rough body movement such as turning over, A technique is described in which the heart rate is determined, and the stage of REM sleep or non-REM sleep is determined based on the number of occurrences of rough body movements during a certain period of time and the magnitude of fluctuations in the heart rate.
Patent Document 2 (Japanese Patent Laid-Open No. 2006-280686), Patent Document 3 (Japanese Patent Laid-Open No. 2008-301951), Patent Document 4 (Japanese Patent Laid-Open No. 2009-160001), Patent Document 5 (Japanese Patent Laid-Open No. 2010-94379) ) Uses a condenser microphone that detects pressure fluctuations in a mattress containing an incompressible fluid as a sensor for acquiring a biological signal of the human body, and the standard deviation of the amplitude of the respiratory waveform in three adjacent sections , Techniques for determining coarse body movements such as rolling over, sluggish body movements such as snoring, and stable body movements, and no body movements, and variations in respiratory rate, respiratory rate, and respiratory cycle are below the threshold. In this case, a technique for determining a deep sleep state is described.

  In Patent Document 6 (Japanese Patent Laid-Open No. 2012-170624), Patent Document 7 (Japanese Patent Laid-Open No. 2012-187299), and Patent Document 8 (Japanese Patent Laid-Open No. 2012-187349), they are arranged away from a person's bedside. The human body movement is measured by the Doppler sensor (31), the respiratory waveform and the body movement waveform are separated from the measured waveform, and the variation of the period in a predetermined period (30 seconds or 1 minute) is set in advance. If it is smaller than the threshold value, it is determined that there is periodicity (stable), and the stage of sleep and wakefulness are determined based on the stability of breathing and absence / single / continuous body movement. The technology to do is described.

JP 2002-219116 A (“0068” to “0069”) JP 2006-280686 A (“0048”, “0065”) JP 2008-301951 A ("0011", "0028" to "0029") JP2009-160001 ("0022", "0037" "0044" to "0045") JP 2010-94379 A (“0029”, “0054” to “0055”) JP 2012-170624 A (“0029”, “0050” to “0058”) JP 2012-187299 A (“0032”, “0050” to “0069”) JP 2012-187349 A (“0028”, “0044” to “0063”)

“Leave sensor”, “online”, Technos Japan Co., Ltd., “November 18, 2013 search”, Internet <URL: http://technosjapan.jp/product/sensor/index.html>

(Problems of conventional technology)
In the configuration in which body movement and heartbeat are measured using a piezoelectric sensor as in the technique described in Patent Document 1, bedding sheets and futons are indirect vibration detection between a human body and the sensor, and the piezoelectric sensor Since the signal is weak and susceptible to noise, the accuracy of detecting the sleep stage and sleep cycle is not sufficiently improved. In particular, the heartbeat senses a pulse wave of very minute vibrations on the surface of the human body, and there is a problem that the detection accuracy is likely to fluctuate due to individual differences such as the posture of the person and the sleeping phase, and the sleeping environment.
In the techniques described in Patent Documents 2 to 5, a special mattress is used, which is difficult to be applied to an existing bed or mattress as it is, and there is a problem that the cost increases if the mattress is replaced. In addition, since the standard deviation of three adjacent sections is used, only the fluctuation of the waveform amplitude in a short period can be determined. In other words, human breathing has long-cycle fluctuations (long-cycle fluctuations) and short-cycle fluctuations (short-cycle fluctuations) even if the breathing is stable depending on the state of brain activity. However, the techniques described in Patent Documents 2 to 5 have a problem that only the short-period fluctuation in the three adjacent sections can be handled, the long-period fluctuation cannot be dealt with, and the detection accuracy is not sufficient.

  Furthermore, in the techniques described in Patent Documents 6 to 8, since the stability of breathing and body movement are determined based on the variation in the cycle within a predetermined period, it is only possible to deal with fluctuations in a short cycle. There is a problem that the detection accuracy is not sufficient.

  This invention makes it a technical subject to determine a test subject's sleep stage accurately.

In order to solve the technical problem, the sleep stage determination device of the invention according to claim 1,
A detection member that is arranged in a non-contact state to the subject and detects vibration of the surface of the subject,
Body motion determining means for determining presence or absence of body motion of the subject based on vibration detected by the detection member and a preset first threshold for body motion determination;
Awakening determination means for determining whether the subject is awake or sleeping based on the frequency of body movement of the subject;
A breathing interval extracting means for extracting a breathing interval of the subject based on the vibration detected by the detecting member;
Fractal dimension computing means for computing a fractal dimension based on the accumulated deviation of breathing intervals in a preset period and the tendency of respiratory fluctuation in each section obtained by dividing the period;
When the awakening determination means determines that the subject is sleeping, the sleep stage of the subject is determined based on the fractal dimension and a preset threshold for determining the sleep stage. A means for determining the sleep stage;
It is provided with.

The invention according to claim 2 is the sleep stage determination device according to claim 1,
The detection member composed of one microwave radar installed under the bedding on which the subject lies;
It is provided with.

The invention according to claim 3 is the sleep stage determination device according to claim 1 or 2,
A fractal value obtained by removing the tendency of fluctuation of respiration from accumulation of deviations of the respiration interval is calculated every time the division width of the divided section is changed, and the logarithmic value of the fractal value with respect to the logarithmic value of the division width is calculated. The fractal dimension calculating means for calculating the fractal dimension based on the inclination of the approximate straight line in a preset range;
It is provided with.

The invention according to claim 4 is the sleep stage determination device according to any one of claims 1 to 3,
A threshold for REM sleep determination is set based on the average value of the fractal dimension in the period determined to be awake and the average value of the respiratory rate in the entire period for all periods in which the sleep stage of the subject is determined. REM threshold value setting means to perform,
A non-rem threshold setting means for setting a threshold for determining a stage of non-REM sleep based on the maximum value and the minimum value of the fractal dimension in the period determined to be sleep for all periods in which the sleep stage of the subject is determined ,
The sleep stage determination means for determining the sleep stage of the subject using the sleep stage determination threshold configured by the threshold for determining the REM sleep and the threshold for determining the stage of non-REM sleep;
It is provided with.

In order to solve the technical problem, the sleep stage determination program of the invention according to claim 5 is:
Computer
Based on the vibration detected by the detection member that is arranged in a non-contact state with the subject and detects the vibration of the surface of the subject, and a preset first threshold for body movement determination, the subject's Body movement determination means for determining presence or absence of body movement,
Arousal determination means for determining whether the subject is awake or sleeping based on the frequency of body movement of the subject;
A breathing interval extracting means for extracting a breathing interval of the subject based on the vibration detected by the detecting member;
Fractal dimension calculating means for calculating a fractal dimension based on cumulative deviation of breathing intervals in a preset period and the tendency of respiratory fluctuation in each section obtained by dividing the period,
When the awakening determination means determines that the subject is sleeping, the sleep stage of the subject is determined based on the fractal dimension and a preset threshold for determining the sleep stage. Means for determining the sleep stage,
It is made to function as.

In order to solve the technical problem, the sleep stage determination method of the invention according to claim 6 comprises:
Based on the vibration detected by the detection member that is arranged in a non-contact state with the subject and detects the vibration of the surface of the subject, and a preset first threshold for body movement determination, the subject's A body movement determination step for determining the presence or absence of body movement;
An awakening determination step for determining whether the subject is awake or sleeping based on the frequency of body movement of the subject;
Based on the vibration detected by the detection member, a breathing interval extracting step of extracting the breathing interval of the subject;
A fractal dimension calculation step for calculating a fractal dimension based on accumulation of deviation of breathing intervals in a preset period and a tendency of fluctuation in breathing in each section obtained by dividing the period;
When the awakening determination means determines that the subject is sleeping, the sleep stage of the subject is determined based on the fractal dimension and a preset threshold for determining the sleep stage. A sleep stage determination process;
It is characterized by performing.

According to the first, fifth, and sixth aspects of the present invention, the stage of sleep can be accurately determined regardless of the difference depending on the age of the subject or the change in physical condition, as compared with the case where the fractal dimension is not used.
According to the second aspect of the invention, compared with the case where the microwave radar is not used, adverse effects such as futons and sleepwear are reduced, and body motion such as turning over, respiratory motion, and pulse wave vibration are clearly separated and detected. This makes it possible to accurately determine the sleep stage of the subject.
According to the third aspect of the invention, not only the short period “fluctuation” but also the long period “fluctuation”, and the fractal dimension representing the degree of change from the short period “fluctuation” to the long period “fluctuation”. As a result, the sleep stage can be accurately determined.
According to invention of Claim 4, compared with the case where a threshold value is not set based on the average value of a fractal dimension and a respiration rate, according to a test subject's individual difference and daily physical condition, a sleep stage is determined accurately. be able to.

FIG. 1 is an overall explanatory diagram of a sleep stage determination apparatus according to Embodiment 1 of the present invention. FIG. 2 is an explanatory diagram showing the function of the sleep stage determination apparatus according to the first embodiment of the present invention in a block diagram (functional block diagram). FIG. 3 is an explanatory diagram of body movement determination according to the first embodiment, and is a graph illustrating a time taken on the horizontal axis and a value obtained by squaring the output value on the vertical axis. FIG. 4 is an explanatory diagram of an example of extraction of the breathing interval according to the first embodiment, in which time is plotted on the horizontal axis and signal values are plotted on the vertical axis. FIG. 5 is an explanatory diagram of the fluctuation of the breathing interval according to the first embodiment. FIG. 5A is an explanatory diagram of the fluctuation of the breathing interval in which the horizontal axis represents the breathing interval number and the vertical axis represents the breathing interval, and FIG. It is explanatory drawing of the deviation accumulation of the respiration interval which took the addition range of deviation and took the accumulation of deviation on the vertical axis | shaft. 6A and 6B are explanatory diagrams of divided sections according to the first embodiment. FIG. 6A is an explanatory diagram when the step size is small, and FIG. 6B is an explanatory diagram when the step size is large. FIG. 7 is an explanatory diagram of the fractal dimension of Example 1, and is a graph in which the horizontal axis represents the step size and the vertical axis represents the fractal value. FIG. 8 is a flowchart of the arousal determination process of the sleep stage determination program AP1 according to the first embodiment. FIG. 9 is a flowchart of the fractal dimension calculation process of the sleep stage determination program AP1 according to the first embodiment. FIG. 10 is a flowchart of the sleep stage determination process of the sleep stage determination program according to the first embodiment. FIG. 11 is an explanatory diagram of an example of a determination result obtained by the sleep stage determination apparatus according to the first embodiment. FIG. 12 is a log-log graph in which the horizontal axis represents the step size and the vertical axis represents the fractal value. FIG. 12A is a graph of the change in the fractal value during REM sleep, and FIG. FIG. 12C is a graph of change, FIG. 12C is a graph of change in fractal value during sleep of non-rem 2, and FIG. 12D is a graph of change of fractal value during sleep of non-rem 3.

Next, specific examples of embodiments of the present invention (hereinafter referred to as examples) will be described with reference to the drawings, but the present invention is not limited to the following examples.
In the following description using the drawings, illustrations other than members necessary for the description are omitted as appropriate for easy understanding.

FIG. 1 is an overall explanatory diagram of a sleep stage determination apparatus according to Embodiment 1 of the present invention.
In FIG. 1, the sleep stage determination apparatus S according to the first embodiment of the present invention includes a bed (bed) 1 on which a human (subject) as an example of a subject lies. The bed 1 includes a mattress support 1a as a bed body and a mattress 1b supported by the mattress support 1a. The mattress 1b of Example 1 has a cushioning property for improving the sleeping comfort by being placed on the mattress support 1a. The mattress 1b is made of urethane or bedding such as a so-called air mattress (air mat or air bed) in which air is sealed.

A microwave radar SN1 as an example of a detection member is supported on the lower surface of the mattress 1b. The microwave radar SN1 according to the first embodiment can transmit and receive microwaves (electromagnetic waves) and detect vibrations on the body surface of the subject.
In the first embodiment, one microwave radar SN1 is arranged on the bed 1 at a position corresponding to the upper body of the subject, particularly the chest. Note that the number of microwave radars SN1 is preferably one from the viewpoint of reducing the number of parts and cost. For example, in the case of a large bed such as a king size bed or a double bed, In view of the above, it is possible to adopt a configuration in which a plurality are arranged.

In addition, the frequency of each microwave in Example 1 is open to the public and does not require a specific license, and passes through the outer surface of the mattress 1b, the clothes of the subject, and the like. It is desirable to employ a frequency that can be transmitted to the body surface of the specimen and can receive a reflected wave from the subject. Therefore, in Example 1, the frequency of the microwave is set in advance to about 24 [GHz].
The mattress 1b is also free of coil springs or the like that may adversely affect microwave transmission / reception, and is preferably, for example, the urethane low-resilience mattress or air mattress.

  A subject detection member SN3 that detects a subject lying on the mattress 1b is supported on the upper surface of the mattress 1b. The subject detection member SN3 according to the first embodiment is configured by a sheet-like sensor that detects whether or not the subject is away from the bed 1 based on the weight (body weight), that is, a so-called bed leaving sensor (a heel sensor). In addition, about the said bed leaving sensor, it is possible to use the well-known product described in the nonpatent literature 1, etc., for example.

Moreover, in FIG. 1, the sleep stage determination apparatus S of Example 1 has the client personal computer (sleep stage determination apparatus main body, personal computer) PCa as an information terminal to which the microwave radar SN1 and the subject detection member SN3 are connected. The client personal computer PCa according to the first embodiment includes a so-called computer device, and includes a computer main body H1, a display H2, input devices such as a keyboard H3 and a mouse H4, an HD drive (hard disk drive) (not shown), and the like. ing.
The sleep stage determination apparatus S according to the first embodiment is configured by the bed 1, the sensors SN1 and SN3, the client personal computer PCa, and the like.

(Description of the control part of Example 1)
FIG. 2 is an explanatory diagram showing the function of the sleep stage determination apparatus according to the first embodiment of the present invention in a block diagram (functional block diagram).
In FIG. 2, the computer main body H1 of the client personal computer PCa has an I / O (input / output interface) that performs input / output of signals with the outside and adjustment of input / output signal levels, programs and data for performing necessary processing, ROM (read-only memory, recording medium) in which is stored, RAM (random access memory, recording medium) for temporarily storing necessary data and programs, processing according to the program stored in ROM, etc. A CPU (central processing unit), a clock oscillator, and the like are included, and various functions can be realized by executing programs stored in a ROM, a RAM, and the like.
The client personal computer PCa having the above configuration can realize various functions by executing programs stored in a hard disk, a ROM, or the like.

(Signal input element connected to the computer main body H1 of the client personal computer PCa)
Output signals from the following signal output elements SN1, SN3, etc. are input to the computer main body H1 of the client personal computer PCa.
SN1: Microwave radar The microwave radar SN1 irradiates microwaves toward the upper surface of the bed 1, receives a reflected wave from the subject, and inputs an output signal corresponding to the received reflected wave to the control unit.
SN3: Subject detection member The subject detection member SN3 detects whether or not the subject is lying on the mattress 1b based on the weight (weight), and inputs the detection signal to the control unit.

(Description of function of computer main body H1 of client personal computer PCa)
The hard disk drive of the client personal computer PCa stores a basic software (operating system) OS that controls basic operations of the client personal computer PCa, a sleep stage determination program AP1 as an application program, and other software (not shown).

(Sleep stage determination program AP1)
The sleep stage determination program AP1 has the following functional means (program module).
C1: Landing determination unit The landing determination unit C1 determines whether the subject is landing or has left the floor based on a signal from the subject detection member SN3.

C2: Signal History Storage Unit The signal history storage unit C2 stores a history of output signals output from the microwave radar SN1. The radar output signal is usually a minute continuous analog signal of several millivolts, and the output signal is amplified about 100 times and converted into analog-digital data sampled discrete data per unit time suitable for computer processing. In the signal history storage unit C2 of the first embodiment, when the landing determination unit C1 determines that the subject has landed on the bed 1, storage of the output signal V1 of the microwave radar SN1 is started. The output signal V1 of the microwave radar SN1 is stored every 10 ms (1/100 second) as an example of unit time. Further, in the signal history storage unit C2 of the first embodiment, when it is determined that the subject has left the bed 1, the storage of the signal of the microwave radar SN1 is terminated. Therefore, in the signal history storage unit C2 of the first embodiment, the time during which a signal is detected differs depending on the sleep time of the subject, more precisely, the time from when the user gets to the floor. That is, the detection according to the individual difference and physical condition of the subject, and the length of sleep time for each day is performed.

C3: Signal Value Amplifying Unit The signal value amplifying unit C3 amplifies the value V1 of each signal stored in the signal history storage unit C2. In the microwave radar SN1 of the first embodiment, the output signal V1 is output as a signal of about ± 0.2 [V] to ± 0.4 [V] for the respiratory signal, while the body motion signal is ± It is 0.7 [V] or more, and the body motion signal is usually a signal of ± 1 [V] or more. Therefore, the signal value amplifying unit C3 according to the first embodiment amplifies the signal while eliminating the positive / negative distinction by squaring the value of each signal V1. That is, a respiration signal sufficiently smaller than ± 1 [V] becomes a small value by squaring, and a normal body motion signal of ± 1 [V] or more becomes a large value, and the respiration signal and the body motion signal The difference is significant.
C4: Threshold storage means for body movement determination Threshold storage means C4 for body movement determination as an example of the first threshold storage means stores a threshold Va for body movement determination as an example of the first threshold. In the first embodiment, the threshold value Va for body motion determination is 0.5 [V 2] obtained by squaring the lower limit value 0.7 [V] of the body motion signal to 0.5 [V ² ]. V ² ] is set. That is, Va = 0.5 is set.

FIG. 3 is an explanatory diagram of body movement determination according to the first embodiment, and is a graph illustrating a time taken on the horizontal axis and a value obtained by squaring the output value on the vertical axis. The threshold for body movement determination is 0.5 [V ² ], and the graph is shown.
C5: Body Movement Determination Unit The body movement determination unit C5 determines body movement based on the detection signal of the microwave radar SN1 and the threshold value Va for body movement determination. In FIG. 3, the body movement determination means C5 of the first embodiment generates body movement when the signal value ((V1) ² ) amplified by the signal value amplification means C3 is equal to or greater than the body movement determination threshold value Va. It is determined that
C6: Determination section allocating means The determination section allocating means C6 allocates a body movement determination section for determining arousal and sleep and a stage determination section for determining a sleep stage (hereinafter referred to as a sleep stage determination section). Is simply referred to as a determination interval). In Example 1, 30 seconds is preset as one body motion determination section, and the entire period stored in the signal history storage means C2 is divided into body motion determination sections every 30 seconds from the start of detection. Moreover, in Example 1, the period for 3 minutes is set as a determination stage of a sleep stage. In addition, in Example 1, although the structure for which the period for 3 minutes was set as the determination area for one sleep stage determination was illustrated, it is not limited to this, Short period fluctuation and long period fluctuation are detectable. For example, 3 to 5 minutes is preferable. Also, the body motion determination section is not limited to 30 seconds, and can be set to any time during which body motion can be determined.

C7: Body Movement Index Calculation Unit The body movement index calculation unit C7 calculates a body movement index that is the frequency of body movement of the subject. The body motion index calculation means C7 of the first embodiment calculates the number M1 of body motions in each body motion determination section as a body motion index.
C8: Threshold Determination Means for Awakening Determination The threshold storage means C8 for determination of awakening stores a threshold value Ma for determination of awakening. As an example, Ma = 4 is set as the threshold value Ma for determination of arousal in the first embodiment. That is, the threshold Ma for determination of arousal can set an average lower limit value of the number of body movements M1 that occur at the time of awakening in each determination section, but depending on the individual difference of the subject, the sensitivity of the sensor SN1, etc. It can be appropriately changed to any value.

C9: Awakening determination means The awakening determination means C9 determines whether or not the subject is awake based on the frequency of body movement. The awakening determination means C9 of Example 1 determines that the body motion index M1 is awake when the body motion index M1 is greater than or equal to the body motion determination threshold value Ma, and the body motion index M1 is less than the body motion determination threshold value Ma. When it is, it determines with sleeping. In this awakening determination, it is desirable to make a provisional determination of awakening and sleep for every body movement determination section (30 seconds) and to perform smooth processing of a specific section. In other words, it is possible to make one section having a determination result different from the previous two sections and the rear two sections as a specific section, and to reverse the temporary determination of awakening or sleep determination so that the determination of awakening and sleep is continued.
C10: Awakening / Sleep Period Storage Unit The awakening / sleeping period storage unit C10 stores the determination result by the awakening determination unit C9. The awakening / sleep period storage unit C10 according to the first embodiment stores a body movement determination section determined as awakening as an “awakening period” and stores a determination section determined as sleeping as a “sleeping period”.

C11: Filtering unit Filtering unit C11 as an example of a frequency component extracting unit extracts a respiratory signal based on history data stored in signal history storage unit C2. The filter unit C11 according to the first embodiment performs filtering on the waveform of the history data stored in the signal history storage unit C2. In the first embodiment, only a smooth respiratory waveform without distortion is applied by applying a band pass filter (band pass filter) that passes a band of 0.1 [Hz] to 0.5 [Hz] corresponding to the frequency of respiration. To extract.

FIG. 4 is an explanatory diagram of an example of extraction of the breathing interval according to the first embodiment, in which time is plotted on the horizontal axis and signal values are plotted on the vertical axis.
C12: Respiration Interval Extraction Unit The respiration interval extraction unit C12 extracts a respiration interval. In FIG. 4, the breathing interval extraction means C12 of the first embodiment calculates the interval from one peak of the breathing waveform to the next peak based on the data of only the breathing waveform that has passed through the filter means C11. Get as. That is, the interval from the peak (peak) to the next peak (peak) at which the subject switches from inhalation to exhalation is acquired as the breathing interval B (i).

FIG. 5 is an explanatory diagram of the fluctuation of the breathing interval according to the first embodiment. FIG. 5A is an explanatory diagram of the fluctuation of the breathing interval in which the horizontal axis represents the breathing interval number and the vertical axis represents the breathing interval, and FIG. It is explanatory drawing of the deviation accumulation of the respiration interval which took the addition range of deviation and took the accumulation of deviation on the vertical axis | shaft.
C13: Mean breathing interval calculating means The mean breathing interval calculating means C13 calculates an average breathing interval Bavg which is an average value of the breathing intervals B (i) in the determination section. The average breathing interval calculation means C13 of the first embodiment targets up to the kth (k ≦ N) breathing intervals when N breathing intervals are extracted in the determination section, and sets i to 1 to k. Bavg is derived by the following formula (1), where i is the value and the i-th breath interval is B (i).
_{^{Bavg = {Σ N i = 1}} B (i)} / N ... formula (1)

C14: Deviation Accumulation Function Calculation Unit The deviation accumulation function calculation unit C14 accumulates the difference (that is, deviation) between each breathing interval and the average value of the breathing intervals in the determination section from the array 1 to the array k. (K) is calculated. The deviation accumulating function calculating means C14 of the first embodiment calculates a deviation accumulating function y (k) that is a function obtained by summing deviations of the first to kth breathing intervals in each determination section. The deviation accumulation function calculation unit C14 according to the first embodiment calculates the deviation accumulation function y (k) by the following equation (2).
y (k) = Σ ^k _{i = 1} [B (i) −Bavg] (2)
As shown in FIG. 5B, the deviation accumulation function y (k) has an initial value of zero, and the deviation accumulates and changes up and down as the number of samples k increases (takes a negative value in the example in the figure). However, when k is the maximum value, that is, the total number N of all measurement data, y (N) is naturally zero.

6A and 6B are explanatory diagrams of divided sections according to the first embodiment. FIG. 6A is an explanatory diagram when the step size is small, and FIG. 6B is an explanatory diagram when the step size is large. The deviation addition range in FIGS. 6A and 6B is graphically displayed as a maximum of 300 as an example, but this maximum addition range varies from 40 to 300 (from 3 minutes to 10 minutes) corresponding to the determination section.
C15: Trend Straight Line Calculation Unit The trend straight line calculation unit C15 calculates a trend straight line as an example of the tendency of fluctuations in breathing in each divided section of the deviation accumulation function y (k). The trend straight line calculation means C15 according to the first embodiment calculates a trend straight line in each divided section obtained by dividing each determination section by a step size (divided width) n. The trend straight line calculation means C15 of the first embodiment calculates the approximate straight line y _n (k) of the waveform of the deviation accumulation function y (k) in the divided section of the step size n by the least square method. That is, in FIG. 6, an approximate straight line y _n (k) indicated by a broken line is calculated for each divided section with respect to a waveform of accumulated deviation of breathing intervals indicated by a solid line. That is, the trend is a straight line in each divided section, but is a connected curve throughout the determination section. In the first embodiment, the step size n varies from the minimum value n0 to the maximum value n1, and the trend line y _n (k) is calculated for each step size n. In the first embodiment, the minimum value n0 is set to n0 = 10, and the maximum value n1 is set to n1 = 30. Further, the fluctuation width na of the step width n is set to na = 1.

C16: Trend removal means A trend removal means C16 as an example of a fractal value calculation means calculates a fractal value F (n) obtained by removing a tendency (trend) of the respiratory fluctuation from the deviation accumulation function y (k). . The trend removal means C16 of Example 1 derives the fractal value F (n) by the following formula (3).
F (n) = {(Σ N k = 1 [y (k) -y n (k)] 2) / N} 1/2 ... Equation (3)
In addition, the trend removal means C16 of Example 1 calculates the fractal value F (n) with respect to each of the step width n from n0 to n1. The fractal value F (n) is also calculated every time the step size n is changed. As can be seen from the comparison between FIG. 6A and FIG. 6B, F (n) always increases as n increases.

FIG. 7 is an explanatory diagram of the fractal dimension of Example 1, and is a graph in which the horizontal axis represents the step size and the vertical axis represents the fractal value.
C17: Fractal dimension calculation means The fractal dimension calculation means C17 calculates the fractal dimension based on the fractal value calculated by the trend removal means C16. The fractal dimension calculation means C17 of the first embodiment uses the division width n of the common logarithm log ₁₀ n of the common logarithm log ₁₀ F (n) of the fractal value F (n) and the common logarithm log ₁₀ n of the fractal value F (n). The approximate straight line of the range is calculated by the least square method. In the first embodiment, the division width n is changed between the minimum value n0 and the maximum value n1. Then, the calculated inclination of the approximate straight line is calculated as the fractal dimension A. Here, the range from n0 to n1 of the division width n is referred to as “fitting range”. That is, the fractal dimension A is derived from an approximate expression represented by the following expression (4).
log ₁₀ F (n) = A × log ₁₀ n + B (4)

C18: Non-REM threshold value setting means Non-REM threshold value setting means C18 is based on the maximum value A1 and the minimum value A2 of the fractal dimension A in the period determined to be sleep for all periods in which the sleep stage of the subject is determined. Threshold values F, G, and H for determining the stage are set. The non-rem threshold value setting unit C18 of the first embodiment is based on the maximum value A1 and the minimum value A2 of the fractal dimension A in the sleep stage determination section in which five or more body movement determination sections determined as “sleep” continue. , Threshold values F, G, and H are set. Then, the non-REM threshold value setting unit C18 according to the first embodiment divides the value that divides the maximum value A1 and the minimum value A2 into four equal parts in order from the smallest one, the threshold value F for determining the first non-REM sleep stage F, the first value. It is calculated and set as a threshold G for determining the stage of the second non-REM sleep and a threshold H for determining the stage of the third non-REM sleep. That is, the thresholds F, G, and H for determining the stage of non-REM sleep are calculated by the following equations (5) to (7).
F = A2 + (A1-A2) / 4 Formula (5)
G = A2 + (A1-A2) / 2 Formula (6)
H = A2 + (A1-A2) × (3/4) (7)

C19: REM threshold value setting means The REM threshold value setting means C19 is the average value A3 of the fractal dimension A in the period determined to be awake and the average value of the respiratory rate in the entire period for all periods in which the sleep stage of the subject is determined. Based on ia, thresholds I and J for determining REM sleep are set. Note that the REM threshold value setting unit C19 according to the first embodiment calculates the average value A3 of the fractal dimension A in the sleep stage determination section in which two or more body movement determination sections determined to be “awake” continue. The REM threshold value setting means C19 of the first embodiment sets the average value A3 of the fractal dimension A as the first threshold value I for REM sleep determination, and sets the average value ia of the respiration rate as the second value for REM sleep determination. Set to threshold J.
C20: Sleep Stage Determination Means The sleep stage determination means C20, when the awakening determination means C9 determines that the subject is sleeping, the fractal dimension A and a preset threshold F for determining the sleep stage. , G, H, I, and J are used to determine the sleep stage of the subject. The sleep stage determination means C20 of the first embodiment uses the thresholds F to J for determining the sleep stage, which are configured by the thresholds I and J for determining the REM sleep and the thresholds F, G, and H for determining the stage of the non-REM sleep. Use to determine the stage of sleep of the subject.

Specifically, the sleep stage determination unit C20 according to the first embodiment, for example, in the case where two or more body motion determination sections among six consecutive body motion determination sections are determined to be “wakeful”. The determination section (3 minutes) of the sleep stage is determined as “awake state”. Further, the sleep stage determination means C20 of the first embodiment determines that five or more body movement determination sections (30 seconds) in the determination section (3 minutes) of the sleep stage are “sleep”, and the sleep stage When the fractal dimension A of the determination section is less than the first non-REM sleep stage determination threshold value F, the sleep stage determination section is determined to be the deepest sleep “Non REM 3”. Here, two or more body motion determination sections are based on the premise that there are six body motion determination sections (30 seconds) in the sleep stage determination section (3 minutes).
Furthermore, the sleep stage determination means C20 of the first embodiment determines that, for example, five or more body motion determination sections in the sleep stage determination section are “sleep”, and the fractal dimension A of the sleep stage determination section is When the threshold value F for determining the stage of the first non-REM sleep is greater than or equal to the threshold value G for determining the stage of the second non-REM sleep, the determination stage of the sleep stage is determined to be “non-REM 2” that is the second deepest sleep. .

The sleep stage determination means C20 according to the first embodiment determines that, for example, five or more body motion determination sections in the sleep stage determination section are “sleep”, and the fractal dimension A of the sleep stage determination section is When the second non-REM sleep stage determination threshold value G is greater than or equal to the third non-REM sleep stage determination threshold value H, the sleep stage determination section is determined to be “non-REM 1”, which is the third deepest sleep. . Further, in the first embodiment, for example, five or more body movement determination sections in the determination section of the sleep stage are determined to be “sleep”, and the fractal dimension A of the determination section of the sleep stage is the first for determining the REM sleep. It is also determined as “non-REM 1” also when it is less than the threshold value I and the respiratory rate i1 in the determination section of the sleep stage is less than the second threshold value J for REM sleep determination.
The sleep stage determination means C20 according to the first embodiment determines that, for example, five or more body motion determination sections in the sleep stage determination section are “sleep”, and the fractal dimension A of the sleep stage determination section is When the REM sleep determination is equal to or higher than the first threshold value I or the respiratory rate i1 in the determination section of the sleep stage is equal to or higher than the second threshold value J for REM sleep determination, the sleep state is determined to be “REM sleep state”.

(Description of Flowchart of Example 1)
Next, the flow of processing of each program AP1 of the client personal computer PCa of the first embodiment will be described using a flowchart. When the subject's landing is detected by the subject detection member SN3, the signal of the microwave radar SN1 is started. When the subject's leaving is detected by the subject detection member SN3, the signal of the microwave radar SN1 is stored. Since the process to be completed is simple, illustration and detailed description are omitted.

(Explanation of Flowchart of Awakening Determination Process of Sleep Stage Determination Program AP1 of Example 1)
FIG. 8 is a flowchart of the arousal determination process of the sleep stage determination program AP1 according to the first embodiment.
The process of each ST (step) in the flowchart of FIG. 8 is performed according to a program stored in the ROM or the like of the control unit. This process is executed in a multitasking manner in parallel with other various processes of the control unit.

The flowchart shown in FIG. 8 is started when the sleep stage determination program AP1 is activated.
In ST1 of FIG. 8, the signal data stored in the signal history storage means C2 is acquired. Then, the process proceeds to ST2.
In ST2, the value V1 ² obtained by squaring the value V1 of the signals was calculated, the process proceeds to ST3.
In ST3, all signal data is divided into body movement determination sections, and section numbers N0 are assigned from 1 to Na. Then, the process proceeds to ST4.
In ST4, for all squared values V1 ^2, it determines whether the threshold value Va and above for the body movement determination. Then, a signal that is equal to or higher than the threshold value Va for body movement determination is determined to be body movement occurrence. Then, the process proceeds to ST5.

In ST5, the section number N0 is set to 1. Then, the process proceeds to ST6.
In ST6, the body motion occurrence count M1 included in the body motion determination section N0 is counted. Then, the process proceeds to ST7.
In ST7, it is determined whether or not the number of body movement occurrences M1 is greater than or equal to a threshold Ma for determination of awakening. If yes (Y), the process proceeds to ST8. If no (N), the process proceeds to ST9.
In ST8, the body movement determination section N0 is determined as the “wakefulness” section. Then, the process proceeds to ST10.
In ST9, the body motion determination section N0 is determined as a “sleep” section. Then, the process proceeds to ST10.
In ST10, it is determined whether or not the body movement determination section number N0 is the maximum value Na. If no (N), the process proceeds to ST11. If yes (Y), the ST awakening determination process ends.
In ST11, 1 is added to the determination section number N0. That is, N0 = N0 + 1. Then, the process returns to ST6.

(Description of Flowchart of Fractal Dimension Calculation Processing of Sleep Stage Determination Program AP1 of Example 1)
FIG. 9 is a flowchart of the fractal dimension calculation process of the sleep stage determination program AP1 according to the first embodiment.
The processing of each ST (step) in the flowchart of FIG. 9 is performed according to a program stored in the ROM or the like of the control unit. This process is executed in a multitasking manner in parallel with other various processes of the control unit.

The flowchart shown in FIG. 9 is started when the arousal determination process is ended.
In ST21 of FIG. 9, the signal data stored in the signal history storage means C2 is acquired. Then, the process proceeds to ST22.
In ST22, the waveform of the signal data is filtered to obtain the waveform of the respiratory signal. Then, the process proceeds to ST23.
In ST23, the respiratory interval is acquired from the peak of the waveform of the respiratory signal in all sections. Then, the process proceeds to ST24.
In ST24, all sections are divided into sleep stage determination sections, and numbers N1 are assigned from 1 to Nb. Then, the process proceeds to ST25.
In ST25, the sleep stage determination section N1 is set to 1. Then, the process proceeds to ST26.

In ST26, a number i is assigned from 0 to i1 for each breathing interval included in the sleep stage determination section N1. Then, the process proceeds to ST27.
In ST27, the following processes (1) and (2) are executed, and the process proceeds to ST28.
(1) Based on the formula (1), the average respiration interval Bavg of all data in the determination section N1 is calculated.
(2) Based on the equation (2), the deviation accumulation function y (k) is calculated.
In ST28, the step size n is set to the initial value n0. Then, the process proceeds to ST29.
In ST29, an approximate straight line y _n (k) is calculated by the least square method for each divided section obtained by dividing the entire respiration interval N by the step size n. Then, the process proceeds to ST30.
In ST30, the fractal value F (n) at the step size n is calculated based on the equation (3). Then, the process proceeds to ST31.

In ST31, it is determined whether or not the step size n is equal to or larger than the final value n1. If no (N), the process proceeds to ST32. If yes (Y), the process proceeds to ST33.
In ST32, the width change value na is added to the step width n. That is, n = n + na. Then, the process returns to ST29.
In ST33, a fractal dimension A that is the slope of the approximate straight line in the log-log graph of the step size n and the fractal value F (n) is calculated. Then, the process proceeds to ST34.
In ST34, it is determined whether or not the determination section N1 is the maximum value Nb. If no (N), the process proceeds to ST35, and if yes (Y), the fractal dimension calculation process is terminated.
In ST35, 1 is added to the determination section number N1. That is, N1 = N1 + 1. Then, the process returns to ST26.

(Description of Flowchart of Sleep Stage Determination Process of Sleep Stage Determination Program AP1 of Embodiment 1)
FIG. 10 is a flowchart of the sleep stage determination process of the sleep stage determination program according to the first embodiment.
The processing of each ST (step) in the flowchart of FIG. 10 is performed according to a program stored in the ROM or the like of the control unit. This process is executed in a multitasking manner in parallel with other various processes of the control unit.

The flowchart shown in FIG. 10 is started when the fractal dimension calculation process is finished.
In ST51 of FIG. 10, the following processes (1) to (3) are executed, and the process proceeds to ST52.
(1) The maximum value A1 and the minimum value A2 of the fractal dimension A of the determination section N1 in which five or more sections determined as “sleep” continue are extracted.
(2) The average value A3 of the fractal dimension A in the determination section N1 in which two or more sections determined to be “awake” continue is calculated.
(3) The average value ia of the respiration rate i1 of all the sections is calculated.
In ST52, the following processes (1) to (3) are executed, and the process proceeds to ST53.
(1) From the maximum value A1 and the minimum value A2, thresholds F, G, and H for determining the stage of non-REM sleep are calculated and set based on the equations (5) to (7).
(2) The average value A3 is set to the first threshold value I for REM sleep determination.
(3) The average value ia is set to the second threshold value J for REM sleep determination.

In ST53, the determination section N1 is set to N1 = 1. Then, the process proceeds to ST54.
In ST54, the determination section N1 determines whether or not “wakefulness” is determined to be two or more. If yes (Y), the process proceeds to ST55, and if no (N), the process proceeds to ST56.
In ST55, the determination section N1 is determined to be “awake state”. Then, the process proceeds to ST65.
In ST56, it is determined whether or not the fractal dimension A of the determination section N1 is less than the first non-REM sleep stage determination threshold F. If yes (Y), the process proceeds to ST57, and, if no (N), the process proceeds to ST58.
In ST57, the determination section N1 is determined as “non-rem 3”. Then, the process proceeds to ST65.

In ST58, it is determined whether or not the fractal dimension A of the determination section N1 is less than a second non-REM sleep stage determination threshold value G. If yes (Y), the process proceeds to ST59, and, if no (N), the process proceeds to ST60.
In ST59, the determination section N1 is determined as “non-rem 2”. Then, the process proceeds to ST65.
In ST60, it is determined whether or not the fractal dimension A of the determination section N1 is less than a third non-REM sleep stage determination threshold value H. If yes (Y), the process proceeds to ST61, and, if no (N), the process proceeds to ST62.
In ST61, the determination section N1 is determined as “non-rem 1”. Then, the process proceeds to ST65.

In ST62, it is determined whether or not the fractal dimension A of the determination section N1 is less than the first threshold value I for REM sleep determination. If yes (Y), the process proceeds to ST63, and, if no (N), the process proceeds to ST64.
In ST63, it is determined whether or not the respiration rate i1 in the determination section N1 is less than the second threshold value J for REM sleep determination. If yes (Y), the process returns to ST61, and, if no (N), the process proceeds to ST64.
In ST64, the determination section N1 is determined as the “REM sleep state”. Then, the process proceeds to ST65.

In ST65, it is determined whether or not the number N1 of the determination section is greater than or equal to the maximum value Nb. If no (N), the process proceeds to ST66, and if yes (Y), the process proceeds to ST67.
In ST66, 1 is added to the determination section number N1. That is, N1 = N1 + 1. Then, the process returns to ST54.
In ST67, the determination result is output to display H2. Then, the sleep stage determination process ends.

(Operation of Example 1)
FIG. 11 is an explanatory diagram of an example of a determination result obtained by the sleep stage determination apparatus according to the first embodiment.
In the sleep stage determination apparatus S according to the first embodiment having the above-described configuration, the microwave radar SN1 detects vibration associated with body movement or breathing of the subject. The body motion index M1 is calculated based on the output signal of the microwave radar SN1, and the determination of arousal or sleep is performed for each body motion determination section based on the frequency of body motion. Further, a respiratory waveform is extracted from the output signal of the microwave radar SN1, and the fractal dimension A is calculated for each determination section N1 of the sleep stage. And based on the fractal dimension A, as shown in FIG. 11, the stage of sleep of REM sleep or non-rem 1 to non-rem 3 is determined with respect to the determination section N1 of the sleep stage determined to be sleep.

Here, when the fractal dimension A is calculated, the step size n varies from the initial value n0 to the final value n1. Then, a trend line y _n (k) is calculated for each step size n, and in equation (3), the trend line y _n (k) is subtracted from the deviation accumulation function y (k). That is, the tendency (trend) in the divided section with the step size n is removed from the deviation accumulation function y (k). This trend of breathing interval corresponds to the baseline of the periodic breathing interval at step size n. Therefore, when the step width n is small, the short cycle (high frequency) baseline is removed, and when the step width n is large, the long cycle (low frequency) baseline is removed. That is, by changing the step size n from the initial value n0 to the final value n1, the fluctuating dimension A reflects the state of the fluctuation of the breathing interval considering the fluctuation of the short cycle to the fluctuation of the long cycle. . In the embodiment, the “fitting range” is set to n0 (10) and n1 (30), but focusing on 10 breathing intervals focuses on fluctuations in the middle period, and targeting 30 breathing intervals. Doing is equivalent to focusing on long-period fluctuations.

FIG. 12 is a log-log graph in which the horizontal axis represents the step size and the vertical axis represents the fractal value. FIG. 12A is a graph of the change in the fractal value during REM sleep, and FIG. 12B is the fractal value during non-REM 1 sleep. FIG. 12C is a graph of change, FIG. 12C is a graph of change in fractal value during sleep of non-rem 2, and FIG. 12D is a graph of change of fractal value during sleep of non-rem 3.
As shown in FIGS. 12A to 12D, it can be seen that as the sleep becomes deeper, the slope of the change in the fractal value becomes smaller, that is, the fractal dimension A becomes smaller. This is thought to be because the deeper the sleep even in the sleep state, the more stable the breath is, and the fluctuation of the breathing interval is released from the control mechanism of the brain, so that the fluctuation becomes small and irregular. As a result, the fluctuation of the breathing interval is reduced even in the short cycle and the long cycle (both the short cycle correlation and the long cycle correlation of the fluctuation of the breathing interval are both weakened), and the gradient of the fractal value is reduced.
Therefore, the sleep stage determination device S according to the first embodiment determines the sleep stage based on the fractal dimension A, and improves the accuracy of the determination of the sleep stage as compared to the conventional technique using the standard deviation. Can do.

  In the first embodiment, the thresholds F to J for determining the sleep stage are set based on the maximum value A1 and the minimum value A2 of the fractal dimension A. In the prior art, a threshold value is set in advance, which cannot cope with individual differences, daily physical condition changes, and the like, and there is a problem that the accuracy of determination of the sleep stage is not improved. On the other hand, in the sleep stage determination apparatus S of Example 1, it is set from the maximum value A1, the minimum value A2, etc. in one sleep from landing to getting out of bed, and individual differences and daily physical condition are reflected. Thresholds F to J. Therefore, the accuracy of determination of the sleep stage can be improved as compared with the conventional technique.

  Further, in the first embodiment, the body motion of the subject is detected in a non-contact manner using the microwave radar SN1. Therefore, the burden on the subject is reduced as compared with PSG to which electrodes are attached. In addition, it uses the microwave radar SN1 installed on the lower surface of the mattress 1b, and detects vibrations on the body surface of the subject without being affected by sleepwear or mattresses compared to using a piezoelectric sensor or the like. Is possible. Therefore, it is possible to improve the accuracy of determination of the sleep stage.

(Example of change)
As mentioned above, although the Example of this invention was explained in full detail, this invention is not limited to the said Example, A various change is performed within the range of the summary of this invention described in the claim. It is possible. Modification examples (H01) to (H08) of the present invention are exemplified below.
(H01) In the above embodiment, the subject is a human, but is not limited to this. For example, the present invention can be applied to other living bodies such as animals.
(H02) In the above-described embodiments, the exemplified specific numerical values are not limited to the exemplified numerical values, and can be appropriately changed according to the design, specifications, and the like.
(H03) In the above embodiment, the signal value is squared at the time of arousal determination. However, it is possible to amplify to the third power or more if necessary, and it is not necessary to square. If it is possible to determine arousal and sleep, it is possible to adopt a configuration that does not amplify.

(H04) In the above-described embodiment, the microwave radar SN1 is supported on, or attached to, the lower surface of the mattress 1b. However, the present invention is not limited to this, and the microwave radar SN1 may be built in the mattress 1b. The microwave radar SN1 is fixedly supported on the lower surface of the mattress 1b. However, the microwave radar SN1 is not limited thereto. For example, the microwave radar SN1 is interposed between the mattress support 1a and the mattress 1b and the front and rear directions and By providing a slider or the like that can move in the direction, it can be configured to be variably set at an arbitrary position. In addition, the microwave radar SN1 is not supported by the bed 1 and can be changed, for example, stored in the sleepwear pocket of the subject, sewn on the sleepwear, or installed on the ceiling above the bed 1 It is.

(H05) In the above-described embodiment, the microwave radar SN1 that irradiates the microwave is illustrated as an example of the detection member. However, the present invention is not limited to this, and the configuration can be changed to any configuration that can detect the vibration of the human body surface. . For example, although it is possible to change the wavelength range of the electromagnetic wave to be used, when a wavelength range that cannot be transmitted through the mattress 1b or the like is selected, an opening for passing electromagnetic waves is formed in the mattress 1b, or legal restrictions are imposed. When a certain wavelength range is selected, it is necessary to obtain authorization and permission, so it is desirable to use microwaves.
(H06) In the above-described embodiment, the connection between the sensors SN1 and SN3 and the client personal computer PCa is configured by a wired cable. However, the present invention is not limited to this, and may be configured by, for example, wireless communication.

(H07) In the above-described embodiment, the subject detection member SN3 is configured by a so-called bed leaving sensor (an heel sensor), but is not limited thereto, and is configured by, for example, a strain gauge or a piezoelectric sensor, and the mattress 1b. It is also possible to detect the subject by determining whether or not pressure is applied to the subject. Also, the subject detection member SN3 is omitted, and when the signal is not detected for a predetermined time, it is determined that the person is getting out of bed, and when the signal is detected from the state where the signal is not detected, it is also determined that the person is landing. Is possible.
(H08) In the above embodiment, the sleep stage determination program AP1 has exemplified the configuration in which the single sleep stage determination apparatus S performs the concentration process, but the present invention is not limited to this. For example, all or part of the sleep stage determination program AP1 may be stored in a plurality of computer devices connected to the network, and distributed processing, that is, a so-called cloud configuration may be configured via the network.

A ... Fractal dimension,
A1: Maximum value of fractal dimension,
A2: Fractal dimension minimum value,
A3 ... Average value of fractal dimension,
AP1 ... sleep stage determination program,
C5 ... body movement determination means,
C9 ... Awakening determination means,
C12 ... breathing interval extraction means,
C17: Fractal dimension calculation means,
C18: non-rem threshold value setting means,
C19: Rem threshold value setting means,
C20: means for determining the sleep stage,
F, G, H: Threshold values for determining the stage of non-REM sleep,
F, G, H, I, J ... Threshold for determining sleep stage,
F (n) ... fractal value,
I, J: Threshold value for determining REM sleep,
ia ... average breathing rate,
log ₁₀ F (n)… Logarithm of fractal value,
log ₁₀ n… logarithm of the division width,
M1 ... frequency of body movement,
n: Dividing width,
PCa ... computer,
S ... Sleep stage determination device,
SN1 ... detection member, microwave radar,
Va: a first threshold value for body movement determination,
y (k) ... accumulation of deviation,
y _n (k): Trend of respiratory fluctuations.

Claims (6)

A detection member that is arranged in a non-contact state to the subject and detects vibration of the surface of the subject,
Body motion determining means for determining presence or absence of body motion of the subject based on vibration detected by the detection member and a preset first threshold for body motion determination;
Awakening determination means for determining whether the subject is awake or sleeping based on the frequency of body movement of the subject;
A breathing interval extracting means for extracting a breathing interval of the subject based on the vibration detected by the detecting member;
Fractal dimension computing means for computing a fractal dimension based on the accumulated deviation of breathing intervals in a preset period and the tendency of respiratory fluctuation in each section obtained by dividing the period;
When the awakening determination means determines that the subject is sleeping, the sleep stage of the subject is determined based on the fractal dimension and a preset threshold for determining the sleep stage. A means for determining the sleep stage;
A sleep stage determination device comprising: The detection member composed of one microwave radar installed under the bedding on which the subject lies;
The sleep stage determination device according to claim 1, comprising: A fractal value obtained by removing the tendency of fluctuation of respiration from accumulation of deviations of the respiration interval is calculated every time the division width of the divided section is changed, and the logarithmic value of the fractal value with respect to the logarithmic value of the division width is calculated. The fractal dimension calculating means for calculating the fractal dimension based on the inclination of the approximate straight line in a preset range;
The sleep stage determination device according to claim 1, further comprising: A threshold for REM sleep determination is set based on the average value of the fractal dimension in the period determined to be awake and the average value of the respiratory rate in the entire period for all periods in which the sleep stage of the subject is determined. REM threshold value setting means to perform,
A non-rem threshold setting means for setting a threshold for determining a stage of non-REM sleep based on the maximum value and the minimum value of the fractal dimension in the period determined to be sleep for all periods in which the sleep stage of the subject is determined ,
The sleep stage determination means for determining the sleep stage of the subject using the sleep stage determination threshold configured by the threshold for determining the REM sleep and the threshold for determining the stage of non-REM sleep;
The sleep stage determination device according to any one of claims 1 to 3, further comprising: Computer
Based on the vibration detected by the detection member that is arranged in a non-contact state with the subject and detects the vibration of the surface of the subject, and a preset first threshold for body movement determination, the subject's Body movement determination means for determining presence or absence of body movement,
Arousal determination means for determining whether the subject is awake or sleeping based on the frequency of body movement of the subject;
A breathing interval extracting means for extracting a breathing interval of the subject based on the vibration detected by the detecting member;
Fractal dimension calculating means for calculating a fractal dimension based on cumulative deviation of breathing intervals in a preset period and the tendency of respiratory fluctuation in each section obtained by dividing the period,
When the awakening determination means determines that the subject is sleeping, the sleep stage of the subject is determined based on the fractal dimension and a preset threshold for determining the sleep stage. Means for determining the sleep stage,
It is made to function as a sleep stage determination program characterized by the above-mentioned. Based on the vibration detected by the detection member that is arranged in a non-contact state with the subject and detects the vibration of the surface of the subject, and a preset first threshold for body movement determination, the subject's A body movement determination step for determining the presence or absence of body movement;
An awakening determination step for determining whether the subject is awake or sleeping based on the frequency of body movement of the subject;
Based on the vibration detected by the detection member, a breathing interval extracting step of extracting the breathing interval of the subject;
A fractal dimension calculation step for calculating a fractal dimension based on accumulation of deviation of breathing intervals in a preset period and a tendency of fluctuation in breathing in each section obtained by dividing the period;
When the awakening determination means determines that the subject is sleeping, the sleep stage of the subject is determined based on the fractal dimension and a preset threshold for determining the sleep stage. A sleep stage determination process;
The sleep stage determination method characterized by performing.

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