WO2023048158A1

WO2023048158A1 - Sleep apnea syndrome determination device, sleep apnea syndrome determination method, and program

Info

Publication number: WO2023048158A1
Application number: PCT/JP2022/035080
Authority: WO
Inventors: 圭樹 ▲高▼玉; 怡恒中理
Original assignee: 国立大学法人電気通信大学
Priority date: 2021-09-22
Filing date: 2022-09-21
Publication date: 2023-03-30

Abstract

The present invention obtains biological vibration data from the heart rate, respiration, and body motion of a subject during sleep, performs frequency analysis on the acquired biological vibration data, and acquires any one among an average of the frequency spectrum at a location determined to be awakening, an average of the frequency spectrum at a location determined to be other than awakening, and an average of the frequency spectrum both at the location determined to be awakening and the location determined to be other than awakening. The present invention acquires an approximation curve for the logarithmic value of the obtained average of the frequency spectrum, and determines whether the subject suffers from sleep apnea syndrome on the basis of the unimodal characteristics of the logarithmic value of the average of the frequency spectrum.

Description

Device for determining sleep apnea syndrome, method and program for determining sleep apnea syndrome

The present invention relates to a sleep apnea syndrome determination device, a sleep apnea syndrome determination method, and a program for determining sleep apnea syndrome of a subject.

In the medical field, the sleep state of the person being measured is measured in order to diagnose sleep disorders and sleep apnea syndrome. It is known that the human sleep stage is classified into six stages from the viewpoint of the depth of sleep, and the six sleep stages are wakefulness, REM sleep, non-REM sleep (stage 1 to 4). Conventionally, these six sleep stages are determined by attaching a large number of electrodes to the subject's face and head, for example, and measuring electroencephalograms, eye movements, and jaw electromyograms from the large number of electrodes. Analysis of the results was done.

In addition, patients with sleep apnea syndrome often experience apnea during sleep, difficulty breathing, light sleep, and awake sleep stages. , sleep stages should be measured.
However, in order to determine sleep apnea syndrome, in addition to the measurement of sleep stages, various measurements such as air flow associated with breathing such as mouth and nose air flow and ventilation movements of the chest and abdomen are required. measurements must be made simultaneously. Then, based on the analysis result of the sleep stage and the measurement result of the respiratory state, the doctor diagnoses whether or not the patient has an apnea syndrome.

Sleep tests with a large number of electrodes attached to the face and head, which are necessary for making such a diagnosis, are usually performed by staying at a medical institution and attaching electrodes to the body continuously for a long time. This is an examination performed by the patient, and imposes a mental and physical burden on the person (patient) to be measured. In addition, the acquired data must be analyzed and judged by a doctor with specialized knowledge and experience. For this reason, sleep apnea syndrome cannot be determined easily.

In order to solve the problems related to sleep stage measurement, many sleep stage estimation methods that do not require diagnosis by a specialist have been proposed.
For example, Patent Document 1 describes a method called Database-based Compact Genetic Algorithm, which is an improved learning method using a genetic algorithm, and a technology for estimating sleep stages from detection data of a mattress type pressure sensor. there is The technique described in this Patent Document 1 estimates a sleep stage based on the subject's body motion and heart rate detected by a mattress-type pressure sensor. According to the technique described in Patent Literature 1, by estimating the sleep stage using the mattress-type pressure sensor, it is possible to estimate the sleep state of the subject without imposing a burden on the subject.

JP 2014-239789 A

As mentioned above, when patients with apnea syndrome develop apnea during sleep, they often find it difficult to breathe, their sleep becomes shallow, and their sleep stage becomes awake. For this reason, detecting sleep stages using the detection data of the mattress-type pressure sensor, that is, the body movement detection data, is an index for determining apnea syndrome.

The point that there is a possibility of apnea syndrome when the sleep stage during sleep often becomes arousal is, for example, the change example of the sleep stage of a healthy person in FIG. 5A described later and the apnea syndrome in FIG. 5B This can be seen by comparing the changes in sleep stages of patients with That is, as can be seen from FIG. 5, the apnea syndrome patients have more awakening sleep stages than healthy subjects.
However, even in healthy people, there are many cases in which deep sleep is not possible depending on the situation, and it is not possible to judge that apnea syndrome is present simply because of the large number of awakenings in the sleep stage during sleep. Therefore, there is an urgent need to more accurately determine apnea syndrome.

As described above, it has been desired to develop an apnea syndrome determination device, an apnea syndrome determination method, and a program that can determine sleep apnea syndrome without imposing a burden on the subject.

The sleep apnea syndrome determination device of the present invention includes a biological data acquisition unit that acquires biological vibration data due to heartbeat, respiration, and body movement during sleep of the subject, and the biological vibration data acquired by the biological data acquisition unit. By frequency analysis, the average of the frequency spectrum of the location determined to be arousal during sleep, the average of the frequency spectrum of the location determined to be other than arousal, and the location determined to be awake and the location determined to be other than that a biological data processing unit that acquires one of the averages of the frequency spectrum of the biological data acquisition unit, acquires an approximate curve for the logarithm of the average of the frequency spectra obtained by the biological data acquisition unit, and obtains the frequency spectrum obtained by the biological data acquisition unit Detects the amount by which the logarithm of the average deviates from the fitted curve in the positive or negative direction a determination unit that determines whether the subject has sleep apnea syndrome.

Further, the sleep apnea syndrome determination method of the present invention includes a biological data acquisition process for acquiring biological vibration data due to heartbeat, respiration, and body movement during sleep of the subject, and biological vibration acquired by the biological data acquisition process. Frequency analysis of the data, the average of the frequency spectrum of the location determined to be arousal during sleep, the average of the frequency spectrum of the location determined to be other than awake, and the location determined to be awake and other locations Biological data processing for obtaining one of the averages of both frequency spectra, Approximate curve acquisition processing for obtaining an approximated curve for the logarithm of the average of the frequency spectra obtained by the biological data processing, and obtained by the biological data processing Detects the amount by which the average logarithm value of the frequency spectrum deviates from the approximated curve acquired in the approximated curve acquisition process in the positive or negative direction, and the amount by which the detected approximated curve deviates in the positive or negative direction and a determining unit determining process for determining whether the subject has sleep apnea syndrome based on the magnitude of the measurement.

Further, the program of the present invention is a program that causes a computer to execute each process performed by the sleep apnea syndrome determination method as a procedure.

BRIEF DESCRIPTION OF THE DRAWINGS It is a block diagram which shows the structural example of the sleep apnea syndrome determination apparatus by one embodiment of this invention. FIG. 4 is a diagram showing an example of sleep apnea syndrome determination state according to an embodiment of the present invention; 1 is a block diagram showing a hardware configuration example of a device for determining sleep apnea syndrome according to an embodiment of the present invention; FIG. FIG. 5 is a diagram showing an example of processing for obtaining a power spectrum during sleep at regular intervals according to an embodiment of the present invention; FIG. 5A shows an example of changes in sleep stages during sleep of a healthy subject. FIG. 5B shows an example of sleep stage changes during sleep of a patient with sleep apnea syndrome. 4 is a flow chart showing the flow of processing for determining sleep apnea syndrome according to one embodiment of the present invention. FIG. 7A shows an example of contribution to vibration frequency during sleep of a healthy subject. FIG. 7B shows an example of the average vibration frequency in the wakefulness (W) section during sleep of a healthy subject. FIG. 7C shows an example of the average frequency of vibrations in the non-awakening period during sleep of a healthy subject. FIG. 7D shows an example of the average logarithmic value of the vibration frequency in the wakefulness (W) period during sleep of a healthy subject. FIG. 7E is a diagram showing an example of the average logarithmic value of the vibration frequency in the non-awakening period during sleep of a healthy subject. FIG. 8A shows an example of the contribution to the frequency of oscillations during sleep of a patient with sleep apnea. FIG. 8B shows an example of the average frequency of oscillations in the arousal (W) interval during sleep of a patient with sleep apnea syndrome. FIG. 8C shows an example of the average frequency of vibrations in the non-awakening period during sleep of a patient with sleep apnea syndrome. FIG. 8D shows an example of the average logarithmic value of the oscillation frequency in the wakefulness (W) interval during sleep of a patient with sleep apnea syndrome.　Fig. 8E is a diagram showing an example of the average logarithmic value of the frequency of the vibration in the interval other than the awakening period during sleep of a patient with sleep apnea syndrome. FIG. 10 is a diagram showing a representative example in which the average logarithmically calculated value of the frequency of vibrations in a section other than awakening of a patient with sleep apnea syndrome is superimposed on an approximate curve. FIG. 10A is a diagram showing a first example in which the average logarithmically calculated value of the frequency of vibration in a section other than awakening of a healthy person is superimposed on an approximate curve. FIG. 10B is a diagram showing an example of the second person in which the average logarithmically calculated value of the vibration frequency in the non-awakening section of the healthy person is superimposed on the approximate curve. FIG. 10C is a diagram showing an example of a third person in which the average logarithmically calculated value of the vibration frequency in the non-awakening section of the healthy person is superimposed on the approximation curve. FIG. 10D is a diagram showing an example of a fourth person in which the average logarithmically calculated value of the vibration frequency in the section other than the awakening of the healthy person is superimposed on the approximate curve. FIG. 10E is a diagram showing an example of the fifth person in which the average logarithmically calculated value of the vibration frequency in the section other than the awakening of the healthy person is superimposed on the approximation curve. FIG. 11A is a diagram showing a first example in which an average logarithmically calculated value of the frequency of vibrations in a section other than awakening of a patient with sleep apnea syndrome is superimposed on an approximate curve. FIG. 11B is a diagram showing a second example in which the average logarithmically calculated value of the vibration frequency in the interval other than the awakening period of the patient with sleep apnea syndrome is superimposed on the approximation curve. FIG. 11C is a diagram showing a third example in which the average logarithmically calculated value of the vibration frequency in the interval other than the awakening period of the patient with sleep apnea syndrome is superimposed on the approximation curve. FIG. 11D is a diagram showing a fourth example in which the average logarithmically calculated value of the vibration frequency in the interval other than the awakening period of the patient with sleep apnea syndrome is superimposed on the approximate curve. FIG. 11E is a diagram showing a fifth example in which the average logarithmically calculated value of the vibration frequency in the interval other than the awakening period of the patient with sleep apnea syndrome is superimposed on the approximate curve. It is a figure which shows the example of the determination result by one embodiment of this invention.

An embodiment of the present invention (hereinafter referred to as "this example") will now be described with reference to the drawings. In the following description and drawings, the sleep apnea syndrome is referred to as SAS, and the sleep apnea syndrome patient is referred to as the SAS patient. In addition, when referring to healthy subjects in the following description, it means those who are not SAS patients.
[1. Configuration of sleep apnea syndrome determination device]
FIG. 1 is a block diagram showing the configuration of a sleep apnea syndrome determination device 10 of this example.
FIG. 2 is a diagram showing an example of a state in which sleep apnea syndrome determination is performed using the sleep apnea syndrome determination device 10 of this example.

The sleep apnea syndrome determination device 10 of the present embodiment acquires the body vibration of the person to be measured as pressure data with the mattress sensor 2 . The bio-vibration includes vibration components due to heartbeat and respiration in addition to vibration components due to the body motion of the person being measured. The mattress sensor 2 detects the bio-vibration of the upper body of the subject A during sleep as a change in pressure. The mattress sensor 2 is used by laying it on or under the mattress of the bed 1 on which the subject A sleeps, as shown in FIG. 2, for example.
It should be noted that the arrangement of the mattress sensor 2 on the mattress under the subject A is an example, and the mattress sensor 2 may be incorporated in the mattress, for example.

FIG. 2 shows an example in which the sleep apnea syndrome determination device 10 is installed beside the bed 1 and the mattress sensor 2 and the sleep apnea syndrome determination device 10 are connected with a cable. The pressure data (biological vibration data) may be wirelessly transmitted to the sleep apnea syndrome determination device 10 in another room.
In the following description, the pressure data output by the mattress sensor 2 will be referred to as biological vibration data.
It should be noted that obtaining biological vibration data from a pressure sensor is an example, and other sensors may be used. For example, an infrared sensor, a laser, or the like may be used to measure the vibration of the subject during sleep without contact.

As shown in FIG. 1, the sleep apnea syndrome determination device 10 includes a biological data acquisition unit 11, a biological data processing unit 12, a sleep stage determination unit 13, a sleep apnea syndrome determination unit (hereinafter, "SAS determination unit"). ) 14 and an output unit 15 .
The biometric data acquisition unit 11 performs biodata acquisition processing for acquiring biovibration data output from the mattress sensor 2 . The biological vibration data acquired by the biological data acquisition section 11 is supplied to the biological data processing section 12 .

The biological data processing unit 12 samples the supplied biological vibration data, converts it into digital data, and calculates the frequency power spectrum of the digitalized biological vibration data. The process of calculating the power spectrum of the frequency of this biovibration data is performed in a cycle of 30 seconds. However, in this example, one calculation is actually performed for 32 seconds, and the calculation for the 32 seconds is performed in a cycle of 30 seconds, that is, overlapped by 2 seconds.

It should be noted that the calculation of the power spectrum by the biological data processing unit 12 at a cycle of 30 seconds is an example, and the power spectrum may be calculated at a cycle shorter than 30 seconds or longer than 30 seconds. For example, the biological data processing unit 12 may calculate the power spectrum in 60-second cycles. The overlapping of two seconds in one calculation is also an example, and there may be no overlapping period.
Then, the biological data processing unit 12 supplies the calculation result of the power spectrum for each fixed period during sleep to the sleep stage determination unit 13 and the SAS determination unit 14 .

The sleep stage determination unit 13 determines the subject's sleep stage for each period based on the calculation result of the power spectrum for each certain period. When determining this sleep stage, not only the power spectrum but also various feature amounts obtained by calculating the biological vibration data may be used for determination. Alternatively, a random forest, which is one of machine learning, may be used to determine a sleep stage from feature amount data.

As sleep stages, there are awakening (WAKE), REM sleep (R), and non-REM sleep (NR) in order from the lightest sleep stage. Non-REM sleep has four stages from stage 1 to stage 4 (NR1-NR4). divided into Therefore, sleep stages are divided into six stages in total. Among the six sleep stages, stage 4 non-REM sleep (NR4) is the deepest sleep stage. However, in actual sleep, it is rare to enter a deep sleep stage such as stage 4 non-REM sleep.
It should be noted that the sleep stage determination unit 13 of this example does not need to determine all of these six sleep stages, and may at least determine whether or not it is wakefulness (WAKE).
In addition, although the sleep stage determination unit 13 of this example determines sleep stages from biological vibration data, the sleep stage determination unit 13 may determine sleep stages from other biological data such as electroencephalograms.

The SAS determination unit 14 determines whether or not the subject has sleep apnea syndrome (SAS) based on the calculation result of the power spectrum for each fixed period and the sleep stage determination result of the sleep stage determination unit 13 . It should be noted that the use of the sleep stage when the SAS determination unit 14 determines whether or not it is SAS is merely an example, and it is not necessary to use the determination result of the sleep stage for determination of SAS.
The details of the processing procedure for the SAS determination unit 14 to determine the SAS based on the calculation results of the power spectrum for each fixed period will be described later.

The output unit 15 outputs the result of sleep apnea syndrome determined by the SAS determination unit 14 . The output unit 15 is configured by, for example, a display device, and displays the determination result of sleep apnea syndrome. Alternatively, the output unit 15 may be configured as a recording device to record the determination result of the sleep apnea syndrome together with the sleeping state of the night. Further, when displaying or recording, the output unit 15 may display or record not only the sleep apnea syndrome determination result but also the sleep stage determination result at the same time.

Further, the output unit 15 may be another external terminal to transmit the determination result via the network. For example, the output unit 15 may be a pre-registered smartphone, and the determination result may be transmitted.
In addition, the sleep apnea syndrome determination apparatus 10 of this example may perform data acquisition and determination during sleep in real time. On the other hand, in the sleep apnea syndrome determination device 10, the biological data acquisition unit 11 only acquires biological vibration data during sleep, records the acquired data, and uses the recorded data to perform processing up to determination at a later date. You can do it.

[2. Hardware configuration example of sleep apnea syndrome determination device]
FIG. 3 shows a hardware configuration example when the sleep apnea syndrome determination device 10 is configured by a computer device.
The computer device C includes a CPU (Central Processing Unit) C1, a ROM (Read Only Memory) C2, and a RAM (Random Access Memory) C3 connected to a bus C8. Further, computer device C comprises non-volatile storage C4, network interface C5, input device C6, and display device C7.

The CPU C1 reads from the ROM C2 the program code of the software that realizes each function of the biological data processing unit 12, the sleep stage determination unit 13, and the SAS determination unit 14 of the sleep apnea syndrome determination device 10 and executes it. For the process of frequency analysis of pressure data, the process of judging sleep stages, and the process of judging SAS, the programs for executing these processes are also read out from ROM C2 and executed by CPU C1. Variables, parameters, etc. generated during arithmetic processing are temporarily written to RAM C3.

As the non-volatile storage C4, for example, HDD (Hard disk drive), SSD (Solid State Drive), flexible disk, optical disk, magneto-optical disk, CD-ROM, non-volatile memory, etc. are used. In addition to the OS (Operating System) and various parameters, the nonvolatile storage C4 stores a program for causing the computer device C to function as the sleep apnea syndrome determination device 10, and as a recording medium for recording the program there is In addition, data about the sleep stages determined by the sleep stage determination unit 13 and the SAS determination results determined by the SAS determination unit 14 are also recorded in the nonvolatile storage C4.

For the network interface C5, for example, a NIC (Network Interface Card) or the like is used, and various data can be transmitted and received via a LAN (Local Area Network) to which terminals are connected, a dedicated line, or the like. For example, the computer device C acquires pressure data output by the mattress sensor 2 via the network interface C5.
The input device C6 is configured by, for example, a device such as a keyboard, and the input device C6 is used to set the period for determining sleep apnea syndrome by the sleep apnea syndrome determination device 10, to instruct the display format of the determination result, etc. is done. The result of sleep apnea syndrome determination by the sleep apnea syndrome determination device 10 is displayed on the display device C7.

Note that the sleep apnea syndrome determination device 10 is an example of a computer device that functions as a determination device by executing a program (software) recorded on a recording medium, and the sleep apnea syndrome determination device 10 Dedicated hardware that executes some or all of the processing may be prepared.

[3. Calculation state of frequency power spectrum and determination example of sleep stage]
Next, processing performed in each part of the sleep apnea syndrome determination device 10 of this example will be described.
First, with reference to FIG. 4, an example in which the biological data processing unit 12 calculates the power spectrum of the frequencies of biological vibrations during sleep for a certain period of time will be described. As already explained, in the case of this example, the biological data processing unit 12 processes data for 32 seconds at intervals of 30 seconds.
That is, as shown in FIG. 4, the biometric data acquisition unit 11 first acquires the sensor values of the mattress sensor 2 from falling asleep (0 seconds) to 32 seconds, and thereafter acquires sensor values for 32 seconds at intervals of 30 seconds. I will get. The biological data acquisition unit 11 continuously acquires the biological vibration data from going to bed to waking up, and supplies the acquired biological vibration data to the biological data processing unit 12 .

Then, the biometric data processing unit 12 calculates the frequency power spectrum for 32 seconds as shown in the lower right of FIG. Frequency analysis for calculating this power spectrum is performed by, for example, fast Fourier transform (FFT).
When the power spectrum of the bio-vibration during sleep is calculated as in this example, the frequencies exhibiting a high density are the bio-vibration component due to heartbeat and the bio-vibration component due to respiration. Normally, the heartbeat frequency is around 1 Hz, and the component of bio-vibration due to respiration is around 2 Hz. However, in the case of an SAS patient, there is an apnea section, and therefore there is a period during which no biological vibration due to respiration occurs. In addition, in sections with large body movements such as rolling over, a bio-vibration component is generated due to body movements that are much larger than heartbeats and respirations.

The sleep stage determination unit 13 determines the main sleep stage in 30 seconds from the power spectrum of biological vibrations as shown in FIG. The method already proposed by the inventors of the present application can be applied as a method for determining sleep stages from the power spectrum of biological vibrations. Although description is omitted here, according to this method, for example, the sleep stage can be determined from the occurrence of different feature amounts for each sleep stage. In the example of FIG. 4, the first 30 seconds after falling asleep are determined to be wakefulness (W), and the next 30 seconds are determined to be stage 2 non-REM (N2).

Figure 5 compares an example of measuring the sleep stages of a healthy subject (Figure 5A) and an example of measuring the sleep stages of a patient with sleep apnea syndrome (SAS patient) during a certain sleep period (Figure 5B). It is a diagram.

In FIG. 5, the horizontal axis indicates sleep time, and the vertical axis indicates sleep stages. The sleep stage on the vertical axis shows the lightest wakefulness (WAKE) at the top, and REM sleep (REM), stage 1 non-REM sleep (NREM1), stage 2 non-REM sleep (NREM1) toward the bottom. NREM 2), stage 3 non-REM sleep (NREM 3), and stage 4 non-REM sleep (NREM 4), indicating a deep sleep stage.
However, in the example of FIG. 5, in both the healthy subject shown in FIG. 5A and the SAS patient shown in FIG. (NREM4) has not been reached.

As can be seen by comparing the healthy subject in FIG. 5A and the SAS patient in FIG. 5B, the SAS patient frequently wakes up due to apnea (WAKE).
In the case of an SAS patient, when the patient wakes up (WAKE), the tongue blocks the airway, causing apnea, and its influence appears in changes in pulsation (heart rate).

Here, in the sleep apnea syndrome determination device 10 of this example, SAS is determined according to the processing procedure described below, mainly from the features that appear in the frequency spectrum in intervals other than wake (WAKE). However, it is an example to determine the SAS from the frequency spectrum of the interval other than wakefulness (WAKE). It is also possible to determine SAS using the frequency spectrum of all sections.

[4. Flow of sleep apnea syndrome (SAS) determination processing]
FIG. 6 is a flow chart showing the flow of processing for determining SAS by the sleep apnea syndrome determination device 10 of this example.
First, the biometric data acquisition unit 11 acquires biovibration data during sleep (step S11). The biological vibration data may be real-time data acquired during sleep, or may be recorded biological vibration data.

Next, the biometric data processing unit 12 calculates feature amounts of biovibration data at regular intervals (step S12). Then, the sleep stage determination unit 13 sets the sleep stage at regular intervals (30 seconds) based on the feature amount calculated by the biological data processing unit 12, and converts the set sleep stage into the biological vibration data of the corresponding section. Label (step S13).

Next, the SAS determination unit 14 acquires, from the biological data processing unit 12, the frequency spectrum, which is the frequency analysis result of the biological vibration data in the interval determined to be other than wakefulness (WAKE) by the sleep stage determination unit 13 (step S14 ). Here, an example will be described in which biological vibration data in a section other than wakefulness (WAKE) is acquired and the subsequent processing is performed. ), when acquiring biovibration data in the interval determined as other than arousal, and in both the interval determined as arousal and the interval determined as other than arousal. There are three cases for acquisition.
The SAS determination unit 14 calculates the average of all frequency spectra in the interval other than the acquired wakefulness (WAKE), and further performs logarithmic calculation (for example, calculation of log2) on the average of the frequency spectrum to obtain a logarithmic value (log calculated value) is obtained (step S15).

Then, the SAS determination unit 14 calculates an approximated curve of changes in the average logarithmic value of the frequency spectrum, and performs a process of acquiring the approximated curve (approximate curve acquisition process) (step S16). The SAS determination unit 14 employs, for example, the method of least squares as a method of calculating the approximate curve. When calculating this approximated curve, it may be calculated by excluding some values such as the lowest frequency.

Next, the SAS determination unit 14 compares the calculated approximated curve with the average logarithm value of the frequency spectrum, and determines that the average logarithm value exceeds the approximated curve. Then, the size of the portion where the image is drawn is calculated (step S17). In the case of this example, as a method of calculating the size of the portion exceeding the approximate curve, the area of the portion where the average logarithm value is larger than the approximate curve on the graph described in FIG. 7 is calculated. A calculation method is adopted.

Then, the SAS determination unit 14 compares the size (area) calculated in step S17 with a threshold value for determination prepared in advance to determine whether or not it is SAS (step S18). That is, when the area calculated in step S17 is equal to or larger than the threshold value, it is determined to be SAS, and when the area calculated in step S17 is less than the threshold value, it is determined not to be SAS.
A determination result is output from the output unit 15 .

FIG. 7 shows a frequency spectrum acquired in one sleep of a healthy subject and data obtained by processing the frequency spectrum. Moreover, FIG. 8 shows a frequency spectrum acquired in one sleep of an SAS patient and data obtained by processing the frequency spectrum.
7 and 8, FIGS. 7A and 8A show the contribution to the overall frequency of the frequency spectrum acquired in one sleep. Figures 7B and 8B show the average of all frequency spectra for the wake interval (WAKE). FIGS. 7C and 8C show averages of all frequency spectra in the non-wake interval (NON-WAKE). Furthermore, FIGS. 7D and 8D show logarithmically calculated values from the average of all frequency spectra of the arousal interval of FIGS. 7B and 8B, and FIGS. Shows the value obtained by logarithmically calculating the average of all the frequency spectra in the interval other than .
7A and 8A show the frequency on the vertical axis and the degree of contribution on the horizontal axis, and FIGS. 7B to 7E and 8B to 8E show the frequency on the horizontal axis and the density on the vertical axis, respectively.

As can be seen by comparing FIG. 7A and FIG. 8A, there are some differences in the distribution of the frequency spectrum of the healthy subject and the contribution of the frequency spectrum of the SAS patient, but the average of each figure is shown in FIG. In 8B and in FIGS. 7C and 8C, no apparent clear difference appears between the healthy subject and the SAS patient.
On the other hand, in FIGS. 7D and 8D, and in FIGS. 7E and 8E, which show the values obtained by calculating the logarithmic value from the average of the frequency spectrum, there is a relatively large difference at frequencies around 3 Hz between healthy subjects and SAS patients. is appearing. That is, both the average logarithmic value of the arousal interval (WAKE) shown in FIGS. 7D and 8D and the average logarithmic value of the non-awakening interval (NON-WAKE) shown in FIGS. 8, the average logarithmic values of the SAS patients shown in FIG. 8 are higher than those of the healthy subjects shown in FIG. In particular, it is shown that the average logarithmic value of the non-awakening interval (NON-WAKE) is significantly higher in SAS patients.
The SAS determination unit 14 of this example determines that the patient is an SAS patient from the difference in the average logarithmic value between the healthy subject and the SAS patient.

Here, the fact that the SAS patient can be determined from the frequency around 3 Hz will be described in more detail.
A vibration phenomenon called microvibration is observed on the surface of the body, and the component of 3 to 4 Hz becomes stronger at a low wakefulness level when one becomes sleepy. From this result, SAS patients have frequent wake-sleep cycles and fall into a low wakefulness level when transitioning from wakefulness to sleep. It is assumed that the component will come out strongly.
On the other hand, in the case of a healthy subject, since wakefulness and sleep are not repeated frequently, it is assumed that the frequency component of 3 Hz appears less as a whole when the logarithmic spectrum for one night is averaged. Therefore, it is possible to appropriately determine that the patient is an SAS patient from the frequency around 3 Hz.

Next, an example of obtaining an approximate curve in step S16 of the flow chart of FIG. 6 will be described with reference to FIG.
FIG. 9 shows an example of an approximation curve C1 obtained by the least-squares method for the average logarithmic value NW1 of the non-awakening interval (NON-WAKE) of the SAS patient.
Note that when obtaining the approximated curve C1, the SAS determination unit 14 excludes several (here, two) values from the lowest frequency in the average logarithmic value NW1 (the values in the range x in FIG. 9). Then, calculation is performed by the method of least squares.
The approximation curve obtained in this manner has the highest value at a low frequency and gradually decreases to a lower value as the frequency increases, drawing a gentle curve.

The SAS determination unit 14 then compares the approximated curve C1 with the average logarithmic value NW1. In the case of the SAS patient shown in FIG. 9, in the frequency band near 3 Hz, there is a continuous interval in which the logarithmic value NW1 is relatively larger than the approximate curve C1.
Therefore, the area value Supper of the region in which the logarithmic value NW1 is larger than the approximate curve C1 on the graph becomes a relatively large value. Since the approximated curve C1 basically reflects (approximates) the state of the average logarithmic value NW1, the value Sunder of the area of the region where the logarithmic value NW1 is smaller than that of the approximated curve C1 and the approximation The difference from the area value Supper of the region where the logarithmic value NW1 is larger than the curve C1 is small.
Therefore, the area value Sunder of the region where the logarithmic value NW1 is smaller than the approximate curve C1 also becomes a large value corresponding to the area value Super of the region where the logarithmic value NW1 is larger than the approximate curve C1. .

Here, as can be seen by comparing the approximated curve C1 and the logarithmic value NW1 shown in FIG. is a unimodal shape, it can be determined as an SAS patient. On the other hand, even if there is a portion where the logarithmic value NW is larger than the approximation curve C1 at a frequency around 3 Hz, if it does not have a single peak, it is determined that the patient is not an SAS patient. Depending on the size of the unimodal portion where the logarithmic value NW is larger than the approximate curve C1, that is, based on the size of the amount deviating in the positive direction, whether the subject is an SAS patient may be determined. When the magnitude of the deviation of the unimodal portion in the positive direction is greater than or equal to a set threshold value, the patient is determined to be an SAS patient, and when it is smaller than the set threshold value, the patient is determined not to be an SAS patient. As a result, it is possible to improve the accuracy of determination as an SAS patient.

It should be noted that here, when the portion where the logarithmic value NW is larger than the approximated curve C1 at a frequency around 3 Hz has a unimodal shape, it is determined to be an SAS patient, but it is detected from the frequency around 3 Hz. is an example, and even if the frequency deviates from 3 Hz, if the location where the logarithmic value NW is larger has a unimodal shape, it may be determined as an SAS patient. However, detection accuracy is higher when detection is performed at a frequency near 3 Hz.

FIG. 9 shows an example of a single SAS patient, while FIGS. 10 and 11 show examples of multiple (five) healthy subjects and SAS patients.
FIGS. 10A, 10B, 10C, 10D, and 10E show the average logarithmic values NW11 to NW15 of the non-awakening interval (NON-WAKE) of five healthy subjects and their approximate curves C11 to C15.
FIGS. 11A, 11B, 11C, 11D, and 11E show average logarithmic values NW21 to NW25 of non-awakening intervals (NON-WAKE) of five SAS patients and their approximate curves C11 to C15.

As can be seen from the logarithmic values NW11 to NW15 of five healthy subjects and their approximate curves C11 to C15 in FIGS. There are almost no frequencies greatly deviating from C11 to C15.
On the other hand, as can be seen from the logarithmic values NW21 to NW25 of five SAS patients in FIGS. ˜NW25 has a larger area than the approximation curves C21 to C25. Also, in the frequency band of about 1 Hz to 2 Hz, there is a region where the logarithmic values NW21 to NW25 are smaller than the approximation curves C21 to C25. Moreover, the logarithmic values NW21 to NW25 of any of the SAS patients have a unimodal shape where they are larger than the approximation curves C21 to C25.

Here, an example of comparing only 5 healthy subjects and 5 SAS patients is shown, but the results of experiments conducted by the inventors of the present application show that for most subjects, healthy subjects and SAS patients Almost the same tendency is shown.

FIG. 12 shows the logarithmic value obtained from the vibration data during one sleep of 18 subjects a to r and the area value of the area where the logarithmic value obtained from the approximate curve is larger than the approximate curve. Supper and the value Sunder of the area of the region whose logarithmic value is smaller than the approximate curve. The vertical axis in FIG. 12 indicates the area value. In the bar graphs of each measurer a to r, the left side is the value Supper and the right side is the value Sunder.

Of the 18 subjects a to r, 9 subjects a to i are measurements of SAS patients, and the remaining 9 subjects j to r are measurements of healthy subjects. . Regarding both the value Supper and the value Sunder, the measurement values of the healthy subjects j to r and the measurement values of the SAS patients a to i are greatly different.

Therefore, as shown in FIG. 12, the SAS determination unit 14 sets a threshold value TH1 for determination between the measured values of the healthy subjects j to r and the measured values of the SAS patients a to i, and the value Supper or the value Sunder is compared with the threshold value TH1. As a result, the SAS determination unit 14 determines that the patient is an SAS patient when the threshold TH is equal to or greater than TH1, and determines that the patient is not an SAS patient when the threshold TH is less than TH1, thereby accurately determining whether the patient suffers from sleep apnea syndrome. can be diagnosed.

As described above, according to the sleep apnea syndrome determination device 10 of the present example, from the biological vibration data based on the body movement or pressure change during sleep of the subject, the patient with sleep apnea syndrome with high accuracy can be determined. Moreover, the data determined by the sleep apnea syndrome determination device 10 of this example is biological vibration data that can be measured by the mattress sensor 2 or the like. It is possible to determine sleep apnea syndrome with high accuracy.
Therefore, the apparatus 10 for determining sleep apnea syndrome according to the present embodiment can produce an effect that the burden on the person to be measured is extremely small compared to the conventional method of making determination by attaching electrodes or the like to the body of the person to be measured. can.

When the sleep apnea syndrome determination device of this example is used for determination, healthy subjects who are not SAS patients may be erroneously determined to be SAS patients, albeit in a small percentage. However, when the health conditions of the erroneously determined healthy subjects are examined in detail, all of the healthy subjects have characteristics similar to those of SAS patients, such as being heavier than the average. Therefore, it indicates the possibility of screening those who have a tendency similar to SAS patients, and it is also possible to detect signs of SAS at an early stage.

[5. Modification]
It should be noted that the processing described in the above-described embodiment is a preferred example, and the processing is not limited to that described in the embodiment.
For example, the SAS determination unit 14 compares the average logarithmic value with the approximated curve, calculates the area where the average logarithmic value is larger or smaller than the approximated curve, and compares the area with the threshold value. I made it On the other hand, as can be seen from FIGS. 9 and 11, the SAS determination unit 14 determines whether or not the average logarithmic value greatly deviates from the approximate curve in the vicinity of 3 Hz, which is a characteristic feature of SAS patients. may
In addition, in the above-described embodiment, the approximated curve is calculated by the method of least squares, but similar approximated curves may be calculated by other calculation methods instead of the method of least squares.

In addition, in the above-described embodiment, the biological vibration data is obtained from the mattress sensor as the biological data acquisition unit that acquires the biological vibration data due to the heartbeat, respiration, and body movement of the subject during sleep. On the other hand, other sensors may be used as long as they can similarly acquire biological vibration data due to heartbeat, respiration, and body movement of the subject during sleep.

1 ... bed, 2 ... mattress sensor, 10 ... sleep apnea syndrome determination device, 11 ... biological data acquisition unit, 12 ... biological data processing unit, 13 ... sleep stage determination unit, 14 ... sleep apnea syndrome determination unit ( SAS determination unit), 15... output unit, A... subject, C... computer device, C1... CPU, C2... ROM, C3... RAM, C4... non-volatile storage, C5... network interface display unit, C6... input device , C7... display device, C8... bus

Claims

a biometric data acquisition unit that acquires biovibration data due to heartbeat, respiration, and body movement of the subject during sleep;
The biological vibration data obtained by the biological data obtaining unit is frequency-analyzed, and the average of the frequency spectrum of the location determined to be awake during sleep, the average of the frequency spectrum of the location determined to be other than wakefulness, and the determination of wakefulness a biological data processing unit that acquires either the average of the frequency spectrum of both the location where the
a sleep apnea syndrome determination unit that determines whether the subject has sleep apnea syndrome based on the unimodality of the average logarithmic value of the frequency spectrum obtained by the biological data acquisition unit; A device for determining sleep apnea syndrome.
The sleep apnea syndrome determination unit calculates an approximated curve for the average logarithm of the frequency spectrum, and compares the average logarithm of the frequency spectrum with the approximated curve to determine the average of the frequency spectrum. The device for determining sleep apnea syndrome according to claim 1, which determines unimodality of logarithmic values.
Further comprising an input unit for inputting information on the approximate curve,
The sleep apnea syndrome determination device according to claim 2, wherein the biological data processing unit calculates the approximate curve based on the information of the approximate curve input from the input unit.
The apparatus for determining sleep apnea syndrome according to claim 2, wherein the lowest frequency component included in the biological vibration data is excluded when calculating the approximate curve.
Further, a sleep stage determination unit that determines the sleep stage of the subject at regular intervals based on the results of frequency analysis of the biological vibration data,
The sleep apnea syndrome determination unit determines that the sleep stage determined by the sleep stage determination unit is a sleep stage interval other than arousal, an arousal sleep stage interval, and both non-awakening and awake sleep stage intervals. The apparatus for determining sleep apnea syndrome according to claim 1 or 2, wherein determination is made by obtaining an average logarithmic value for each frequency.
The sleep apnea syndrome determination unit calculates an approximate curve for the average logarithm of the frequency spectrum obtained by the biological data acquisition unit, and calculates the average logarithm of the frequency spectrum obtained by the biological data acquisition unit. detects the amount of deviation from the calculated approximate curve in the positive direction or the negative direction, and based on the magnitude of the amount of deviation from the detected approximate curve in the positive direction or the negative direction, the subject sleeps The device for determining sleep apnea syndrome according to claim 1, which determines whether or not there is an occasional apnea syndrome.
The sleep apnea syndrome determination unit determines whether the subject has sleep apnea syndrome based on the magnitude of the deviation of the average logarithmic value around 3 Hz from the approximate curve in the positive direction. The apparatus for determining sleep apnea syndrome according to claim 6.
biometric data acquisition processing for acquiring biovibration data due to heartbeat, respiration, and body movement of the subject during sleep;
Frequency analysis of the biological vibration data acquired by the biological data acquisition process, the average of the frequency spectrum of the location determined to be awake during sleep, the average of the frequency spectrum of the location determined to be other than wakefulness, and determination of wakefulness biometric data processing to acquire either the average of the frequency spectrum of both the location where it was determined and the location determined as other;
an approximated curve acquisition process for acquiring an approximated curve for the average logarithm of the frequency spectrum obtained by the biological data processing;
a determination process for determining whether the subject has sleep apnea syndrome based on the unimodality of the average logarithmic value of the frequency spectrum obtained by the biological data processing. Syndrome determination method.
a biometric data acquisition procedure for acquiring biovibration data due to heartbeat, respiration, and body movement of the subject during sleep;
Frequency analysis of the biological vibration data acquired by the biological data acquisition procedure, the average of the frequency spectrum of the location determined to be awake during sleep, the average of the frequency spectrum of the location determined to be other than wakefulness, and determination of wakefulness a biological data processing procedure for obtaining either the average of the frequency spectrum of both the location where the
an approximated curve acquisition procedure for acquiring an approximated curve for the average logarithm of the frequency spectrum obtained by the biological data processing procedure;
a determination procedure for determining whether the subject has sleep apnea syndrome based on the unimodality of the average logarithmic value of the frequency spectrum obtained by the biological data processing procedure;
A program that makes a computer run