JP2021159571A - Sleep analysis method and device - Google Patents

Abstract

To provide sleep analysis method and device that process sleep detection information detected by three sensors easily attached to the body of a subject and incorporated to accurately analyze sleep in terms of various phenomenon that occurs during sleep, in a pain-free manner even for children.SOLUTION: A sleep information detection device including a temperature sensor, an acceleration sensor and an electro-cardiograph is attached to the body of a subject to accurately analyze sleep state such as falling asleep and awakening from sleep based on temperature data, acceleration data and electrocardiograph data transmitted from the sleep information detection device.SELECTED DRAWING: Figure 7

Description

The present invention can be easily attached to a subject, does not require special hospitalization and examination, and can be easily used by the subject in daily life, and the subject's sleep state (sleep onset (latency), sleep deprivation, REM). Sleep, non-REM sleep, light sleep, deep sleep, etc.) can be analyzed easily, accurately and reliably, and sleep that can be easily used not only by adults but also by children, infants, etc. who cannot be examined by conventional sleep polygraphy. Regarding analysis methods and devices.

The conventional sleep analysis method is a painful test in which a subject is hospitalized for one day, wears a large polysomnography device, and is restrained in a bed, and it is difficult for an adult to undergo the test. There was a problem that was not easily applicable to children and small children.

JP-A-2002-291710 Japanese Unexamined Patent Publication No. 2010-99173

As a device that solves the problem of sleep polygraphy that requires hospitalization for one day, for example, the device shown in Patent Document 1 has been proposed, but since it is analyzed only from pulse data, there is a problem in accuracy. Remains. Further, although Patent Document 2 discloses a sleep management system for a large number of people, there is a problem that the detection device itself is large-scale. Furthermore, it does not accurately determine the sleep onset time when the brain sleeps after bed-in.

The present invention has been made based on the above circumstances, and an object of the present invention is to easily attach the subject to a chest or the like and process sleep detection information detected by three types of built-in sensors to sleep. It is an object of the present invention to provide a sleep analysis method and an apparatus capable of performing accurate sleep analysis for various phenomena that occur at times (for example, sleep onset latency) and analyzing the sleep painlessly even for children and the like.

The present invention relates to a sleep analysis method having a small number of sensors. It is achieved by analyzing the sleep state of the subject based on the temperature data, the acceleration data, and the electrocardiographic data.

Further, the present invention relates to a sleep analysis device having a small number of sensors, and the above object of the present invention is a sleep information detection device that can be worn on the body of a subject and includes a temperature sensor, an acceleration sensor and an electrocardiograph, and the sleep. Sleep analysis that acquires and processes temperature data, acceleration data, and electrocardiographic data obtained by the information detector to analyze sleep states of sleep onset (latency), sleep onset, REM sleep, non-REM sleep, light sleep, and deep sleep. It is achieved by providing a part.

According to the sleep analysis method and device according to the present invention, the sleep state of the subject is analyzed by many analysis elements (skin temperature data, heart rate) based on the sleep detection information obtained by the sleep information detection device attached to the chest of the subject. , Respiratory rate, acceleration data, electrocardiographic data, etc.) It is possible to analyze the sleep state accurately and reliably.

In addition, since many analysis elements can be obtained with three few sensors, a temperature sensor, an acceleration sensor, and an electrocardiograph, the configuration is simple in terms of hardware, and data transmission (wireless, terminal connection, etc.) is easy. be.

It is a perspective view which shows the appearance example of the sleep information detection apparatus used in this invention. It is a bottom view of the sleep information detection apparatus main body used in this invention. It is a side view and a plan view which show the structural example of an electrode piece. It is a bottom view of the sleep information detection device used in this invention. It is a schematic diagram which shows the appearance that the sleep information detection device was attached to the chest of a subject. It is a schematic diagram which shows an example of the usage form of the sleep information detection apparatus which concerns on this invention. It is a block diagram which shows the internal structure example of the sleep information detection apparatus. It is a schematic diagram for demonstrating the coordinate (xyz) example of the sleep information detection apparatus in the standing position and the supine position of a subject. It is a block diagram which shows the structural example of an electrocardiograph. It is a block diagram which shows the structural example of the sleep analysis part. It is a block diagram which shows the structural example of the breathing detection part. It is a waveform diagram which shows an example of the electrocardiographic data. It is a waveform diagram which shows the normal example and the abnormal example of the electrocardiographic data waveform. It is a characteristic diagram showing the relationship between the index of sympathetic nerve activity and sleep. It is a characteristic diagram which shows the relationship between the respiratory frequency fluctuation width and sleep. It is a characteristic diagram which shows the relationship between a sympathetic nerve and a parasympathetic nerve, and sleep. It is a characteristic diagram which shows the relation example of the exercise intensity and the exercise acceleration. It is a block diagram which shows the structural example of the analysis processing part. It is a flowchart which shows the operation example of this invention. It is a flowchart which shows the operation example of the analysis processing part. It is a timing chart which shows the operation example of this invention. It is a time chart which shows an example of change during sleep.

One of the best methods of sleep analysis is to use an electroencephalograph. There are various types of brain waves. Alpha waves are waves that appear when you close your eyes and are in a resting state. They are irregular waveforms with a frequency of 8 to 13 Hz and an amplitude of about 30 to 60 μV, and β waves are concentrated. It is a wave that often appears, and is known to have an irregular waveform with a frequency of 14 to 30 Hz and an amplitude of 30 μV or less. Theta wave is a wave that appears in a drowsy state or a light sleep state, and shows a waveform with a frequency of 4 to 7 Hz and an amplitude of about 10 to 50 μV, and a delta wave is struck by deep sleep or anesthesia. It is known that it is a wave that appears in a situation where the frequency is 0.5 to 4 Hz and the amplitude is about 20 to 200 μV. From the viewpoint of brain waves during sleep, there are two types, REM sleep and non-REM sleep. When a person begins to fall asleep, he / she goes into non-REM sleep, gradually goes into deep sleep, and gradually goes into heart rate, blood pressure, and body temperature (inside the body). ) Etc., but this situation is called non-REM sleep, the brain is in a resting state, and the brain waves are more delta waves. Next, the brain waves rapidly become the same situation as when they start to sleep, and theta waves that are seen during light sleep increase, and this state is called REM sleep.

In addition, non-rem sleep is classified into categories 1 to 4 from the shallowest according to the depth of sleep (sleep degree), and categories 1 and 2 are classified as light sleep, and categories 3 and 4 are classified. Is defined as deep sleep.

In the present invention, sleep onset latency (latency) when the brain enters sleep based on sleep detection information by as few sensors (temperature sensor, acceleration sensor, electrocardiograph) as possible, instead of a polygraph device using a large-scale electroencephalograph. It is possible to reliably and accurately detect the state of sleep deprivation and non-rem sleep awakened from sleep, and also detect apnea syndrome and the number of times of turning over during sleep, and the quality of sleep. It is composed of a sleep information detection device worn on the subject and a sleep analysis unit that performs sleep analysis based on the sleep detection information from the sleep information detection device.

FIG. 1 shows an example of the appearance configuration of the sleep information detection device 100 used in the present invention. The main body of the sleep information detection device 100 is a flat plate-shaped rectangular body, and both sides are flexible made of synthetic resin or the like. The long-shaped electrode pieces 101A and 101B are mounted on the front surface, and a USB (Universal Serial Bus) terminal 102 for charging the built-in battery 130 is provided on the front surface thereof. In this example, when there are two electrodes for acquiring electrocardiographic data, and when there are three electrodes, three electrode pieces are attached. Further, although the USB terminal 102 protrudes to the front surface, it may have a movable structure that is buried and protrudes at the time of use, and the installation location can be changed as appropriate.

FIG. 2 shows the bottom surface of the sleep information detection device 100 main body, two circular conductive materials 103A and 103B are embedded on both sides of the bottom surface of the main body, and an electrode piece 101A is located at the center of the conductive materials 103A and 103B. And 101B are provided with recesses 104A and 104B which are recesses for attaching and detaching the 101B.

Further, the electrode pieces 101A and 101B have the same configuration, and a configuration example of the electrode piece 101A is shown in FIG. FIG. 3A is a side view, and FIG. 3B is a plan view. The electrode piece 101A is made of a flexible long-shaped synthetic resin or the like, and a disk-shaped conductive engaging member 105A that contacts and engages with the conductive material 103A is provided at one end of one surface. On the upper surface of the engaging member 105A, a columnar convex portion 106A that fits into the concave portion 104A of the conductive material 103A is provided. The concave portion 104A and the convex portion 106A can be easily attached to and detached from each other. A disk-shaped electrode material 107A that contacts or presses against the subject's skin is provided at the other end of the other surface of the electrode piece 101A, and the engaging member 105A and the electrode material 107A are conductive leads. It is electrically connected by the wire 108A, and the potential measured by the electrode material 107A passes through the lead wire 108A, the engaging member 105A (convex portion 106A), and the conductive material 103A (concave portion 104A) in the sleep information detection device 100. Is input to the control unit 140 of the above, and then is input to the sleep analysis unit 300 described later. The periphery of the electrode material 107A has a known suction cup structure, and can be easily attached to the skin (chest) of the subject.

Sleep information is obtained by fitting the two electrode pieces 101A and 101B having such a configuration with the concave portions 104A and 104B of the conductive materials 103A and 103B and the convex portions 106A and 106B of the engaging members 105A and 105B. It can be mounted on the detection device 100, and when the electrode pieces 101A and 101B are mounted, the structure is as shown in the perspective view of FIG. 1 and the bottom view of FIG. In this state, the suction cups at both ends of the electrode pieces 101A and 101B are applied to the chest of the subject, and the sleep information detection device 100 is attached to the subject as shown in FIG. Since the suction cup can be attached by simply touching the chest of the subject or by pressing lightly (pressing), it is easy for children, infants, and the like.

The electrode materials 107A and 107B are electrodes of an electrocardiograph, and in this example, the potentials of the subject are measured at two locations, so that the interval d is 80 [mm] or more. Is desirable. Further, the mounting member is not limited to the suction cup structure, and other known means can be used.

FIG. 6 shows various usage patterns of the sleep analysis unit 300 including the sleep information detection device 100 attached to the subject and a PC (personal computer), and the system of FIG. 6A shows the detection by the sleep information detection device 100. The sleep detection information is transmitted wirelessly, the sleep analysis unit 300 directly receives the sleep detection information, and the sleep analysis result analyzed by data processing by the sleep analysis unit 300 is output by printing, displaying, information, or the like. It has become like. Further, in the system of FIG. 6B, the sleep detection information detected by the sleep information detection device 100 is input to the sleep analysis unit 300 via the network 1. The network 1 can include a mobile communication network, a public network such as the Internet, a fixed telephone network, etc., and can be realized by a WAN (Wide Area Network), a LAN (Local Area Network), etc., and can be wired or wireless. It doesn't matter. Further, the system of FIG. 6C shows an example in which the system is connected to the network 1 via the terminal device 2 and the sleep analysis unit 300 is connected to the network 1.

Although FIG. 6 shows an example in which sleep detection information is wirelessly output from the sleep information detection device 100, wired or optical communication may be used, and the USB terminal 102 may be directly connected to a predetermined terminal portion of the sleep analysis unit 300. The sleep detection information stored in the memory in the sleep analysis unit 300 may be read and processed (direct transmission / reception method).

FIG. 7 shows an example of the internal configuration of the sleep information detection device 100, in which the sleep information detection device 100 has a sensor unit 120 for detecting sleep information, and the sensor unit 120 is the skin (skin) temperature of the subject. To the temperature sensor 121 that detects and outputs the temperature data Th, the acceleration sensor 122 that measures the acceleration of the movement (movement) of the subject and outputs the acceleration data α, and the two electrodes 107A and 107B that come into contact with the subject. It is composed of an electrocardiograph 123 that outputs electrocardiographic data EC based on the electrocardiograph 123. The temperature sensor 121 measures the skin temperature of the subject, and the measured temperature data Th is input to the control unit 140. The acceleration sensor 122 measures the acceleration according to the movement (movement) of the subject for each of the three orthogonal axes (x-axis, y-axis, and z-axis), and the measured acceleration data α of the xyz-axis is input to the control unit 140. NS. The electrocardiograph 122 measures the potential with the two electrodes 107A and 107B, and inputs the subject's electrocardiographic data EC calculated from the potential difference to the control unit 140. The measured potential is weak and is amplified by an amplifier or the like inside the electrocardiograph 123, so that it is easily affected by noise. Therefore, in order to reduce the influence of noise and improve the S / N ratio, the electrodes 107A and 107B, the amplifier, and the like are arranged close to each other.

As described above, the temperature data Th, acceleration data α and electrocardiographic data EC output from the sensor unit 120 are input to the control unit 140, and the control unit 140 inputs the input temperature data Th, acceleration data α and electrocardiogram. The data EC is stored in each area in the memory 141 preset for each data, and is transmitted to the external sleep analysis unit 300 as sleep detection information RS via the transmission unit 142. Further, the sleep information detection device 100 has a built-in rechargeable battery 130 that supplies electric power to each element, and the battery 130 is charged by inserting the USB terminal 102 into a USB terminal of a PC or the like. Further, in the form of the direct transmission / reception method described above, the data stored in the memory 141 can be taken in by inserting the USB terminal 102 into the predetermined terminal portion of the sleep analysis unit 300.

When the simultaneous transmission mode is set by the mode changeover switch (not shown), the control unit 140 transmits the detected sleep detection information to the transmission unit 142 without storing it in the memory 141, and the transmission unit 142 sends the detected sleep detection information to the transmission unit 142. The sleep detection information RS is transmitted to the outside. When the storage transmission mode is set by the mode selector switch, the sleep detection information is temporarily stored in the memory 141, then the information is read out at any time and transmitted to the transmission unit 142, and the transmission unit 142 externally transmits the sleep detection information RS. Send to. In this case, the detected sleep detection information can be stored in the memory 141 and simultaneously wirelessly transmitted to the outside, or can be simply stored in the memory 141. When only storing in the memory 141, it is taken into the sleep analysis unit 300 by the above-mentioned direct transmission / reception method. The transmission unit 142 converts the input sleep detection information RSA into a format that can be received by the external sleep analysis unit 300, and wirelessly transmits the sleep detection information RSA as the sleep detection information RSA. As a wireless transmission method, a Wi-Fi method, a Bluetooth (registered trademark) method, or the like is used. The sleep analysis unit 300 is constructed by software such as a personal computer, and is based on the received sleep detection information RS. Scientifically analyze the sleep state of the subject.

The sleep information detection device 100 used for the sleep analysis of the present invention is attached to the chest of the subject as shown in FIGS. 5 and 8, and the vertical direction is the y-axis (acceleration α _y ) in the standing state of FIG. 8 (A). The left-right direction is the x-axis (acceleration α _x ), the front-back direction is the z-axis (acceleration α _z ), and the left-right direction is the x-axis (acceleration α _x ) in the recumbent position shown in FIG. Is the y-axis (acceleration α _y ), and the direction perpendicular to the paper surface is the z-axis (acceleration α _z ), but the axis relationship can be changed as appropriate. The acceleration sensor 122 measures the acceleration related to the movement (movement) of the entire chest of the subject, the movement of the heart, the airway, the diaphragm, the rib cage, and the like, and all the measured acceleration data α is input to the control unit 140. Further, as shown in FIG. 9, the electrocardiograph 123 is composed of a potential difference calculation unit 123A that obtains the difference between the potential e1 of the electrode 107A and the potential e2 of the electrode 107B by the equation 1 and outputs the electrocardiographic data EC. The electrocardiographic data EC is also input to the control unit 140.
(Number 1)
EC = e1-e2
When the number of electrodes is three (the third electrode potential is e3), the potential difference is calculated according to the following equation 2 or 3, and the electrocardiographic data EC calculated based on the potential difference is output. ..
(Number 2)
EC = {(e1-e2) + (e1-e3)} / 2
(Number 3)
EC = e1- (e2 + e3) / 2

FIG. 10 shows a configuration example of the sleep analysis unit 300, and the sleep detection information RS of the temperature data Th, the acceleration data α, and the electrocardiographic data EC transmitted from the sleep information detection device 100 is an interface (format conversion and the like) for performing format conversion and the like. It is input to the input unit 301 of the I / F), and the electrocardiographic data EC is input to the peak detection unit 302 via the input unit 301. The temperature data Th is input to the analysis processing unit 320 as it is, and the acceleration data α is input to the breathing detection unit 310, the posture detection unit 360, and the motion detection unit 370. The posture data ST indicating the standing position, supine position, etc. detected by the posture detection unit 360 is input to the analysis processing unit 320, _{obtained from the motion acceleration α m,} and the energy consumption EN detected by the motion detection unit 370 is analyzed. It is input to the processing unit 320.

The peak R of the electrocardiographic data EC is detected by the peak detection unit 302, and the peak signal PS indicating the detected peak R is input to the peak interval detection unit 303. The peak interval detection unit 303 detects the peak interval RRI of each data based on the peak signal PS, and the peak interval signal IPS indicating the peak interval RRI includes the time / frequency analysis unit 350, the heart rate detection unit 340, and the CVRR (Coefficient of). Variation of RR Interval) Input to the detection unit 341. The time / frequency analysis unit 350 analyzes the frequency with respect to the time of the peak interval RRI, and the wavelength analysis unit 351 that inputs the analyzed frequency signal analyzes the frequency of 0.15 Hz or more as a high wavelength HF, and the frequency is 0. Frequencies smaller than 15 Hz and 0.04 Hz or higher are analyzed as low wavelength LF. The analyzed high wavelength HF and low wavelength LF are input to the autonomic nerve calculation unit 352, and the sympathetic nerve (Sympathetic Nervous System) index SNS and the parasympathetic nerve (Parasympathetic Nervous System) index PSNS indicating the activity of the autonomic nerve are the following numbers. Calculated according to 4.
(Number 4)
HF = parasympathetic index PSNS
LF / HF = sympathetic nerve index SNS

That is, the parasympathetic nerve index PSNS which is a parasympathetic nerve activity index is a high wavelength HF, the sympathetic nerve index SNS which is a sympathetic nerve activity index is LF / HF, and these parasympathetic nerve index PSNS and the sympathetic nerve index SNS are analyzed. While being input to the processing unit 320, the parasympathetic nerve index PSNS is input to the respiratory detection unit 310 and the aspiration syndrome detection unit 342 and subjected to Fourier analysis. Further, the peak interval signal IPS is input to the heart rate detection unit 340 and the CVRR detection unit 341, and the detected heart rate HN is input to the analysis processing unit 320. Since the peak interval signal IPS is based on the electrocardiographic data EC and the pulsation of the heart is associated with the heart rate, the heart rate HN can be easily detected.

Further, the acceleration data α from the input unit 301 is input to the respiration detection unit 310, the posture detection unit 360, and the movement detection unit 370, and the respiratory rate BR detected by the respiration detection unit 310 is input to the apnea syndrome detection unit 342. At the same time, it is input to the analysis processing unit 320. The energy consumption EN calculated based on the posture data ST such as standing position and supine position from the posture detection unit 360 and the motion acceleration (α _xm , α _ym , α _{zm) from the motion detection unit 370 is the analysis processing unit.} It is input to 320. The sleep analysis information SY analyzed by the analysis processing unit 320 is output as the sleep analysis information SY via the output unit 304 of the interface (I / F) for matching the format.

The sleep information detection device 100 according to the present invention has an acceleration sensor 122 built-in, and as described with reference to FIGS. 8A and 8B, the x-axis acceleration α _x , the y-axis acceleration α _y , and the z-axis. Acceleration α _{z of} is detected and output. The accelerations α _x , α _y , and α _z of each axis include the gravity acceleration α _g , the motion acceleration α _m , and the minute body motion acceleration α _s , respectively. For example, the x-axis acceleration α _x is the following number. It is represented by 5, and generally has the following relationship of 6 during exercise. The same applies to the y-axis acceleration α _y and the z-axis acceleration α _z.
(Number 5)
α _x = α _xg + α _xm + α _xs
(Number 6)
α _m ＞ α _g ＞ α _s

The output of the minute body motion acceleration α _s is very small, 0.01 [G] or less. Further, the motion acceleration α _m is a number [G] up to a light motion position. For the detection of respiration, the minute body movement acceleration α _{s is used for each axis of xyz, but the body movement acceleration α s} in a small range of about 0.01 [G] or less must be detected. Therefore, in the processing of the acceleration data α of the present invention, two types of bandpass filters (BPS) are used so as to increase the sampling period and increase the output gain in order to increase the small output. That is, since the respiratory rate is about 10 to 40 times per minute and the frequency is about 0.17 [Hz] to 0.67 [Hz], a BPF that passes this frequency is used. A low-pass filter (LPF) having a gravity of 0.01 [G] or less is used. If the noise is still large, the fast Fourier transform (FFT) is used to calculate the respiratory rate (respiratory frequency) BR.

What needs to be further examined is the examination of the output of the acceleration sensor 122 when the sleep information detection device 100 is attached to the chest of the subject. Depending on the person, it can be classified into a person with large airway movement (z-axis direction), a person with large diaphragm movement (y-axis direction), and a person with large rib movement (X-axis direction and z-axis direction). For the first predetermined time (for example, 5 seconds) when detecting information, the acceleration data α _zs in the z-axis direction, the acceleration data α _ys in the y-axis direction, and the acceleration data α _{xs in the} x-axis direction are automatically compared. , Adopt the two larger acceleration data. This has a great effect on respiratory rate detection. According to the experiment, the movement of the diaphragm (y-axis direction) is generally larger in many people, but since it differs in each case, the movement of the airway (z-axis direction), the movement of the diaphragm (y-axis direction) and The rib movements (X-axis direction and z-axis direction) are compared, and a large value is automatically selected.

A configuration example of the respiration detection unit 310 configured based on such a premise will be described with reference to FIG. _{Acceleration α (α x} , α _y , α _z ) is input to the selection unit 311 from the acceleration sensor 122 of the sleep information detection device 100, and _{the two larger accelerations α y,} α _z , and α _x are selected for high-speed sampling. It is input to the unit 312. In the high-speed sampling unit 312, _{in order to acquire a minute body motion acceleration α s} of 0.01 [G] or less, high-speed sampling higher than a general respiratory frequency is performed, and the high-speed sampled accelerations α _y1, α _{z 1} , Input α _x1 to BPF313. The BPF 313 has a frequency bandwidth of 0.17 to 0.67 [Hz], which corresponds to a normal human respiratory rate of 10 to 40 breaths per minute, and is bandpass-filtered by the BPF 313. Acceleration α _y2, α _z2 and α _x2 are input to LPF314. The LPF314 passes the magnitude of gravity of 0.01 [G] or less, _{and inputs the low-pass filtered accelerations α y3} , α _z3 , and α _x3 to the fast Fourier transform unit (FTT) 315. FTT315 frequency signal alpha _ys by removing noise components, alpha _zs, outputs alpha _xs, frequency signals α _ys, α _zs, α _zs is input to the comparison unit 316. The larger frequency signal is automatically selected by the comparison unit 316 and input to the respiration calculation unit 317. The respiratory rate BR calculated from the frequency signal by the respiration calculation unit 317 is input to the analysis processing unit 320 and also input to the apnea syndrome detection unit 342.

The apnea syndrome detection unit 342 inputs the respiratory rate BR and the parasympathetic nerve index PSNS, and when there is no breathing within a predetermined time (for example, 10 seconds) and the respiratory rate BR becomes "0", an apnea signal is signaled. Output NB. Further, since the apnea syndrome can be determined even when the parasympathetic nerve index PSNS has no output for a predetermined time (for example, 10 seconds), both determinations may be performed in parallel. Either one may be implemented. The apnea signal NB is input to the analysis processing unit 320.

FIG. 12 shows an example of the waveform of the electrocardiographic data EC, and the peak detection unit 302 detects the peaks R1, R2, ... Rn of the electrocardiographic data EC, and the peaks showing the peaks R1, R2, ... Rn. The signal PS is input to the peak interval detection unit 303. The peak interval detection unit 303 sequentially detects the interval of the peak R, that is, the peak interval RRI1 between the peak R1 and the peak R2, the peak interval RRI2 between the peak R2 and the peak R3, the peak interval RRI3 between the peak R3 and the peak R4, and so on. The peak interval signal IPS indicating these peak interval RRIs is input to the time / frequency analysis unit 350, the heart rate detection unit 340, and the CVRR detection unit 341. The coefficient of variation RRIV of the peak interval was obtained based on the peak signal PS and the peak interval signal IPS of the electrocardiographic data EC, and the activity of the autonomic nerve called CVRR (Coefficient of Variation of RR Interval) was normalized from the coefficient of variation RRIV. Calculate the coefficient CVRR. The calculation method of the coefficient CVRR is known, but when the variance value of the peak interval is σ and the average value of the peak interval is M, it is represented by the following equation 7. The coefficient CVRR is input to the analysis processing unit 320 and used for determining the activity of the autonomic nerve.

図１３の波形図において、正常な被験者は、図１３（Ａ）に示すようにピーク間隔ＲＲＩ１〜ＲＲＩ３に多少のバラツキがあり、異常な被験者は、図１３（Ｂ）に示すようにピーク間隔ＲＲＩ１〜ＲＲＩ３がほぼ均一でバラツキがない。

In the waveform diagram of FIG. 13, normal subjects have some variations in peak intervals RRI1 to RRI3 as shown in FIG. 13 (A), and abnormal subjects have peak intervals RRI1 as shown in FIG. 13 (B). ~ RRI3 is almost uniform and there is no variation.

It is known that the relationship between the exchange nerve index SNS, which is an activity index of the exchange nerve, and sleep (light sleep and deep sleep of non-rem sleep) is shown in FIG. 14, and from this relationship, based on the distribution of the exchange nerve index SNS. It is possible to distinguish between deep sleep and light sleep. Further, FIG. 15 shows the relationship between deep sleep and light sleep with respect to the respiratory frequency fluctuation range VRFRE (Variation of Respiratory Frequency), and from this relationship, deep sleep and light sleep are discriminated based on the respiratory frequency fluctuation range VRFRE. be able to.

The effects of the sympathetic nerve index SNS and the parasympathetic nerve index PSNS on sleep have the characteristics shown in FIG. That is, FIG. 16 (B) in which the sympathetic nerve index SNS and the parasympathetic nerve index PSNS antagonize each other shows the characteristics of a normal sleep state, and FIG. 16 (A) shows a wide range of the parasympathetic nerve index PSNS. Indicates good sleep. Conversely, FIG. 16 (C), in which the sympathetic nerve index SNS is widespread and the parasympathetic nerve index PSNS is narrow, indicates poor sleep.

Next, the operation in which the motion detection unit 370 detects the energy consumption EN based on the acceleration data α will be described.

Exercise intensity (exercise intensity) is an index of the amount of oxygen taken into the body per 1 kg of body weight, but since the amount of oxygen is difficult to understand, the unit METs (Metabolic Equivalent) is used. The unit METs is a unit indicating exercise intensity depending on how many times more energy can be consumed by the exercise when the resting oxygen intake of 3.5 ml / kg / min is set to "1". The relationship between the motion acceleration α _m and the unit METs has the characteristics shown in FIG. Therefore, the corresponding METs can be obtained by detecting the motion acceleration α _m. In the example of FIG. 17, it is 9.8 METs (running at 161 m / min) when the _{exercise acceleration α ma} , and 3.0 METs (normal walking) when the _{exercise acceleration α mc.} In this way, the value of the unit METs is obtained from the detected motion acceleration α _m, and the energy consumption EN is calculated according to the following equation 8. The calculated energy consumption EN is input to the analysis processing unit 320.
(Number 8)
EN = METs x time x weight x 1.05

FIG. 18 shows a configuration example of the analysis processing unit 320, the temperature change detection unit 321 that detects the change (rise, fall) of the temperature data Th and outputs the detection result DT1, and the change (rise, rise, heart rate HN) of the heart rate HN. Heart rate change detection unit 322 that detects (decrease) and outputs the detection result DT2, respiratory rate change detection unit 323 that detects changes (increase, decrease) in respiratory rate BR and outputs the detection result DT3, and sympathetic nerve Changes in the crossover between the sleep degree determination unit 324, which inputs the index SNS and the parasympathetic nerve index PSNS, determines the sleep degree based on the correlation between the two, and outputs the determination result DT4, and the sympathetic nerve index SNS and the parasympathetic nerve index PSNS. The crossing determination unit 325 that determines (rise, decrease) and outputs the determination result DT5, determines the subject's posture (standing position, recumbent position, lateral decubitus position, etc.) based on the posture signal ST, and outputs the determination result DT6. Posture determination unit 326 to perform, detection result DT1 to DT3 and DT7, determination result DT4 to DT6, and none It is composed of a sleep determination unit 328 that inputs a respiratory signal NB and outputs sleep analysis information SYA.

In the detection of each of the above changes, a threshold value for determining an increase and a threshold value for determining a decrease are set, and the same value or different values may be used.

In such a configuration, an operation example thereof will be described with reference to the flowchart of FIG.

First, the sleep information detection device 100 detects the sleep information of the temperature data Th, the acceleration data α, and the electrocardiographic data EC (step S1), and the sleep information detection device 100 transmits the sleep detection information as it is or temporarily enters the memory 141. It is stored (step S2). After that, the sleep detection information (temperature data Th, acceleration data α, and electrocardiographic data EC) RS is input to the sleep analysis unit 300 via the input unit 301 (step S10), and the peak detection unit 302 detects the peak of the electrocardiographic data EC. The detection (step S11) is performed, and the peak interval detection unit 303 detects the peak interval RRI based on the peak signal PS from the peak detection unit 302 (step S12). The peak interval signal IPS from the peak interval detection unit 303 is input to the heart rate detection unit 340 and the CVRR detection unit 341, the heart rate HN is detected by the heart rate detection unit 340 (step S13), and the CVRR detection unit 341 The coefficient CVRR is detected (step S14).

Further, the peak interval signal IPS is input to the time / frequency analysis unit 350 and the time / frequency analysis is performed (step S20), and the wavelength analysis based on the frequency analysis is performed by the wavelength analysis unit 351 (step S21). The analyzed high-wavelength HF and low-wavelength LF are input to the autonomic nerve calculation unit 352, and the parasympathetic nerve index PSNS and the sympathetic nerve index SNS related to the autonomic nerve are calculated (step S22). The parasympathetic nerve index PSNS and the sympathetic nerve index SNS are input to the analysis processing unit 320, and the parasympathetic nerve index PSNS is input to the respiratory detection unit 310 and the apnea syndrome detection unit 342.

The respiratory detection unit 310 detects the respiratory rate BR based on the acceleration data α and the parasympathetic nerve index PSNS (step S23), and the respiratory rate BR is input to the analysis processing unit 320 and input to the apnea syndrome detection unit 342. NS. The posture detection unit 360 detects the posture of the subject based on the acceleration data α (step S24), and the posture data ST is input to the analysis processing unit 320. Further, the motion detection unit 370 detects the energy consumption EN based on the acceleration data α (step S25), and the apnea syndrome detection unit 342 detects the apnea syndrome based on the respiratory rate BR and the parasympathetic nerve index PSNS (step S25). S26), the apnea signal NB indicating the apnea syndrome is input to the analysis processing unit 320. The analysis processing unit 320 detects the sleep state based on each input data (step S100), and the sleep analysis information SYA, which is the analysis result, is output via the output unit 304 (step S200).

The input of each of the above-mentioned data, the order of each process, and the like can be changed as appropriate.

Next, an operation example of the analysis processing unit 320 (step S100 in FIG. 19) will be described with reference to the flowchart of FIG.

The analysis processing unit 320 first determines whether or not there is a change in the temperature data Th (increased from the threshold Th1 and decreased from the threshold Th2) (step S101), and if there is a change, the heart rate HN changes (threshold HN1). It is determined whether or not there is a lower value (lower than the threshold value and higher than the threshold value HN2) (step S102), and if there is a change, it is determined whether or not there is a further change in the respiratory rate BR (lower than the threshold value BR1 and higher than the threshold value BR2). Determine (step S103). When there is a change in respiratory rate BR, the degree of sleep (light sleep, deep sleep) is determined based on the parasympathetic nerve index PSNS and the sympathetic nerve index SNS (step S104), and the parasympathetic nerve index PSNS and the sympathetic nerve index SNS are crossed. It is determined whether or not there is a change (lower than the threshold EC1 and higher than the threshold EC2) (step S105), and if there is a change, the posture detection unit 360 determines the posture of the subject (step S106). Next, it is determined whether or not there is a change (lower than the threshold value EN1 and higher than the threshold value EN2) in the energy consumption EN from the motion detection unit 370 (step S107), and if there is a change, sleep is determined (step). S110), the determination result SY is output from the output unit 304 (step S120).

If there is no change in step S101, step S102, step S103, step S105, and step S107, the determination process of step S110 is performed.

FIG. 21 is a timing chart showing changes in sleep information and operation examples analyzed by the present invention, and the operation of the present invention will be described with reference to FIG. 21.

The subject wears the sleep information detection device 100 on the chest as shown in FIG. 5 for sleep analysis. According to the present invention, the sleep information detection device 100 can be easily attached to the chest. In terms of the environment, the data is connected to the sleep analysis unit 300 in the form shown in FIG. 6, or the detected data is stored in the memory 141 of the sleep information detection device 100, and the data is captured via the USB terminal 102. May be done.

FIG. 21 (A) shows the time axis t1 when the subject entered the bed for sleep, the time point t2 when the subject entered sleep, the time point t3 when apnea awoke from sleep, and the time point t4 when waking up. T indicates that the apnea syndrome was detected by the apnea syndrome detection unit 342.

Before entering the bed before the time point t1, the subject is normally in a standing position as shown in FIG. 21 (B), and the xyz axis is as described in FIG. 8 (A). Then, when entering the bed from the standing position, the body is rotated by an angle π / 2 with respect to the vertical y-axis, and the patient is in the supine position and enters the bed (time point t1 (Bed-)). in)). It is considered that there is generally no movement in the vertical direction (z-axis) when the person enters the floor, and the movement is limited to the movement in the horizontal direction (x-axis and y-axis). However, the subject's heart, airway, diaphragm, rib cage, etc. are moving by autonomic nerves (sympathetic nerves, parasympathetic nerves). In addition, the patient falls asleep from the supine position (time point t3 (Bed-out)), and when waking up, the body is rotated by an angle of π / 2 to become a standing state (time point t4 (Bed-out)). ). Such a state of the subject is detected by the posture detection unit 360 based on the posture data ST, and the determination result DT6 determined by the posture determination unit 326 is input to the sleep determination unit 328.

The temperature data Th, which is the skin temperature of the subject, is detected by the temperature sensor 121, is input to the temperature change detection unit 321 in the analysis processing unit 320 via the control unit 140 and the transmission unit 142, and the rise or fall of the temperature data Th is the threshold value. Is detected based on. The temperature data Th maintains a constant temperature (normal heat) as shown in FIG. 21 (D) before going to bed (before time point t1) and before falling asleep (before time point t2). Then, when the person falls asleep (after the time point t2), the temperature data Th gradually rises, and when he wakes up from sleep (after the time point t3), the temperature data gradually decreases and returns to a constant temperature (normal heat). Therefore, the temperature change detection unit 321 monitors the change in the temperature data Th, and the detection result DT1 is input to the sleep determination unit 328, so that sleep onset and sleep onset can be detected as the first factor.

Further, the electrocardiographic data EC is input to the heart rate detection unit 340 as a peak interval signal IPS via the peak detection unit 302 and the peak interval detection unit 303, and the heart rate HN detected by the heart rate detection unit 340 is the analysis processing unit. It is input to the heart rate change detection unit 322 in 320, and the change (increase, decrease) of the heart rate HN is detected based on the threshold value. The heart rate HN maintains a constant heart rate as shown in FIG. 21 (C) before going to bed (before time point t1) and before falling asleep (before time point t2), but is in a sleep-onset state (time point). After t2), it gradually decreases, and when waking up from sleep (after time point t3), it gradually increases and returns to a constant heart rate HN. Therefore, the heart rate change detection unit 322 monitors the change in the heart rate HN, and the detection result DT2 is input to the sleep determination unit 328, so that sleep onset and sleep onset can be detected as the second factor.

The respiratory rate BR is input to the respiratory rate change detection unit 323 in the analysis processing unit 320, and the change (increase or decrease) of the respiratory rate BR is detected based on the threshold value. The respiratory rate BR maintains a constant respiratory rate before going to bed (before time point t1) and before falling asleep (before time point t2) as shown in FIG. 21 (E), but is in a sleep-onset state (time point). After t2), it gradually decreases, and when waking up from sleep (after time point t3), it gradually increases and returns to a constant respiratory rate. Therefore, the respiratory rate change detection unit 323 monitors the change in the respiratory rate BR, and the detection result DT3 is input to the sleep determination unit 328, so that sleep onset and sleep onset can be detected as the third factor.

The sympathetic nerve index SNS and the parasympathetic nerve index PSNS are input to the sleep degree determination unit 324 and the crossover judgment unit 325, and the sleep degree determination unit 324 is in deep sleep based on the correlation between the sympathetic nerve index SNS and the parasympathetic nerve index PSNS. It is determined whether or not there is light sleep, and the determination result DT4 is input to the sleep determination unit 328. Further, as shown in FIG. 21 (G), the sympathetic nerve index SNS is high and the parasympathetic nerve index PSNS is low before falling asleep. Since the nerve index PSNS gradually increases and crosses, sleep onset can be detected by detecting this crossing. Similarly, when sleep is deprived, the sympathetic nerve index SNS gradually increases and the parasympathetic nerve index PSNS gradually decreases and crosses. Therefore, sleep loss can be detected by detecting this crossover or change. Therefore, the crossover determination unit 325 monitors the intersection or change of the sympathetic nerve index SNS and the parasympathetic nerve index PSNS, and inputs the determination result DT5 to the sleep determination unit 328 to detect sleep onset and sleep onset as the fourth factor. can do.

As shown in FIG. 21 (B), the user is in a standing position before entering the floor (before the time point t1), and as shown in FIG. 21 (F), the acceleration data α has an acceleration α _y with respect to gravity of 1 [. G], but since the posture is in the supine position when entering the floor (after the time point t1), the acceleration α _y becomes “0”. Then, when the person wakes up (after the time point t4), the acceleration α _y with respect to gravity becomes 1 [G] again. Before entering the bed (before time point t1), the acceleration α _z parallel to gravity is “0” because it is in a standing position, but when entering the bed and in a lying position (after time point t2), the acceleration α _z becomes gravity. Since it acts in the direction, the acceleration α _z becomes 1 [G], and when waking up (after the time point t4), the acceleration α _{z with} respect to gravity becomes “0” again. The posture of such a subject is determined by the posture detection unit 360 and the posture determination unit 326, and is an important element of sleep analysis. Since the posture of the subject can be determined from the accelerations α _x , α _y , and α _{z of the} xyz axis, and the lateral decubitus position can also be determined, the number of times of turning over during sleep can be detected.

The energy consumption EN detected by the motion detection unit 370 is input to the energy consumption change detection unit 327, and the change in the energy consumption EN is detected based on the threshold value. The energy consumption EN maintains a constant size as shown in FIG. 21 (F) before going to bed (before time point t1) and before falling asleep (before time point t2), but is in a sleep-onset state (time point). It decreases when (after t2), rises when waking up from sleep (after time point t3), and returns to a certain size. Therefore, the energy consumption change detection unit 327 monitors the change in the energy consumption EN, and the detection result DT7 is input to the sleep determination unit 328, so that sleep onset and sleep onset can be detected as the fifth factor. The sleep determination unit 328 outputs the sleep analysis information SYA based on the detection result and the determination result. When the apnea signal NB is input to the sleep determination unit 328, the sleep analysis information SYA indicating the apnea syndrome is output.

As described above, according to the sleep analysis of the present invention, when all five factors of heart rate HN, skin temperature data Th, respiratory rate BR, energy consumption EN, sympathetic nerve index SNS and parasympathetic nerve index PSNS are satisfied, Since sleep onset and sleep onset are determined, the analysis result is accurate. In addition, it is possible to determine the apnea syndrome and the number of times of turning over.

FIG. 22 shows an example of actual detection data, showing an apneic attack occurring during sleep and a state of taking a shower.

1 Network 2 Terminal device 100 Sleep information detection device 101A, 101B Electrode piece 102 Charging terminal 103A, 103B Conductive material 104A, 104B Recess 107A, 107B Electrode material 120 Sensor unit 121 Temperature sensor 122 Acceleration sensor 123 Electrocardiograph 130 Battery 140 Control unit 141 Memory 300 Sleep analysis unit 301 Input unit 302 Peak detection unit 303 Peak interval detection unit 304 Output unit 310 Respiration detection unit 311 Selection unit 312 High-speed sampling unit 313 BPF
314 LPF
315 Fast Fourier Transform (FFT)
316 Comparison unit 317 Respiration calculation unit 320 Analysis processing unit 321 Temperature change detection unit 322 Heart rate change detection unit 323 Respiratory rate change detection unit 324 Sleep degree judgment unit 325 Crossing judgment unit 326 Posture judgment unit 327 Energy consumption change detection unit 328 Sleep judgment Part 330 Sympathetic nerve detection unit 331 Fourier conversion unit 340 Heart rate detection unit 341 CVRR detection unit 342 Apnea syndrome detection unit 350 hours / frequency analysis unit 351 Wavelength analysis unit 352 Autonomic nerve calculation unit 360 Posture detection unit 370 Motion detection unit

Claims (8)

A sleep information detection device equipped with a temperature sensor, an acceleration sensor and an electrocardiograph is attached to the subject's body, and the sleep state of the subject is determined based on the temperature data, acceleration data and electrocardiographic data obtained by the sleep information detection device. Sleep analysis method to analyze. The temperature data, which is the skin temperature of the subject obtained from the temperature sensor,
The heart rate, exchange nerve index, and parasympathetic nerve index obtained by processing the electrocardiographic data of the subject from the electrocardiograph, and
Respiratory rate and energy consumption obtained by processing the acceleration data of the subject from the acceleration sensor, and
The sleep analysis method according to claim 1, wherein the sleep state of the subject is analyzed based on the above. The sleep analysis method according to claim 1 or 2, wherein the sleep state is bed onset, sleep onset (latent), sleep onset, wake up, REM sleep, non-REM sleep, light sleep, and deep sleep. The sleep onset (latency) is based on the crossover change of the temperature data increase, the exchange nerve index increase and the parasympathetic nerve index decrease, the heart rate decrease, the respiratory rate decrease, and the energy consumption decrease. The sleep analysis method according to claim 3. A sleep information detector that can be worn on the subject's body and is equipped with a temperature sensor, accelerometer, and electrocardiograph.
The temperature data, acceleration data, and electrocardiographic data obtained by the sleep information detection device are acquired and processed, and sleep onset, sleep onset (latency), sleep onset, wake up, REM sleep, non-REM sleep, light sleep, and deep sleep. A sleep analysis unit that analyzes sleep states,
A sleep analysis device characterized by being composed of. The temperature data, the acceleration data, and the electrocardiographic data from the sleep information detection device are transmitted to the sleep analysis unit by terminal connection via a memory, wirelessly, by wire, or by optical communication. The sleep analysis device according to claim 5. The sleep analysis unit
A heart rate detection unit that detects the heart rate based on the peak interval of the electrocardiographic data, and
An autonomic nerve calculation unit that calculates a sympathetic nerve index and a parasympathetic nerve index based on the time / frequency analysis of the peak interval, and
A respiratory rate detector that examines the respiratory rate based on the electrocardiographic data and the parasympathetic nerve,
A motion detection unit that detects energy consumption based on the acceleration data,
Consists of
The sleep analysis device according to claim 6, wherein the sleep analysis unit analyzes the sleep state based on the temperature data, the heart rate, the sympathetic nerve index, the parasympathetic nerve index, the respiratory rate, and the energy consumption. The sleep onset (latency) based on the crossover change of the temperature data increase, the accessory sympathetic index increase and the sympathetic index decrease, the heart rate decrease, the respiratory rate decrease, and the energy consumption decrease. The sleep analysis device according to claim 7.

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