Sleep apnea syndrome detection device, sleep apnea syndrome detection method, and program

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

Technical Field

[0001] The present invention relates to a sleep apnea syndrome determination device, a sleep apnea syndrome determination method, and a program for determining a subject's sleep apnea syndrome.

Background Art

[0002] In the medical field, in order to diagnose sleep disorders and sleep apnea syndrome, the sleep state of the measured person is measured. The human sleep stage is known to be classified into six stages from the perspective of the depth of sleep. The six sleep stages are, in order from the stage of light sleep, wakefulness, REM sleep, and non-REM sleep (stages 1 to 4). The determination of these six sleep stages has conventionally been performed, for example, by attaching a large number of electrodes to the face and head of the measured person, measuring electroencephalogram, eye movement, and masseter muscle potential from the large number of electrodes, and analyzing the measurement results.

[0003] In addition, patients with sleep apnea syndrome often experience apnea during sleep, making it difficult to breathe and resulting in shallow sleep, and the sleep stage often reverts to a wakeful state. Therefore, it is also necessary to measure the sleep stage when diagnosing sleep apnea syndrome. However, in order to determine sleep apnea syndrome, in addition to measuring the sleep stage, it is necessary to simultaneously perform various measurements such as measuring the flow of air associated with breathing, such as the airflow through the mouth and nose, and the ventilation movements of the chest and abdomen. Based on the analysis results of the sleep stage and the measurement results of the respiratory state, a doctor diagnoses whether it is apnea syndrome.

[0004] The sleep examination in the state of wearing a large number of electrodes on the face and head, which is necessary for such a diagnosis, is usually an examination that requires staying in a medical institution and continuously wearing the electrodes on the body for a long time, imposing a mental burden and a physical burden on the measured person (patient). In addition, the acquired data needs to be analyzed and determined by a doctor with specialized knowledge and experience. Therefore, it has not been easy to determine sleep apnea syndrome.

[0005] To address the problems associated with measuring sleep stages, numerous sleep stage estimation methods that do not require a diagnosis by a specialist physician 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 describes a technique for estimating sleep stages from detection data of a mattress-type pressure sensor. The technique described in Patent Document 1 estimates sleep stages based on the body movements and heart rate of the subject detected by the mattress-type pressure sensor. According to the technique described in Patent Document 1, by estimating sleep stages using a mattress-type pressure sensor, the sleep state of the subject can be estimated without imposing any burden on the subject. [Prior art documents] [Patent Documents]

[0006] [Patent Document 1] Japanese Patent Publication No. 2014-239789 [Overview of the project] [Problems that the invention aims to solve]

[0007] As mentioned above, patients with sleep apnea often experience shortness of breath during sleep, leading to shallow sleep and frequently transitioning to an awakened state. Therefore, detecting sleep stages using data from mattress-type pressure sensors, i.e., body movement detection data, can serve as one indicator for diagnosing sleep apnea.

[0008] The fact that frequent awakenings during sleep can increase the risk of sleep apnea can be seen, for example, by comparing the changes in sleep stages of a healthy person (Figure 5A) with those of a patient with sleep apnea (Figure 5B). In other words, as can be seen from Figure 5, patients with sleep apnea experience more awakenings during sleep compared to healthy individuals. However, even healthy individuals often experience difficulty achieving deep sleep depending on the circumstances, and a high frequency of awakenings during sleep does not automatically diagnose sleep apnea. Therefore, there has been an urgent need to develop a more accurate diagnostic method for sleep apnea.

[0009] As explained above, there has been a need for the development of a sleep apnea syndrome detection device, sleep apnea syndrome detection method, and program that can determine sleep apnea syndrome without burdening the person being measured. [Means for solving the problem]

[0010] The sleep apnea syndrome detection device of the present invention comprises: a biometric data acquisition unit that acquires bio-vibration data due to the heart rate, respiration, and body movement of a subject during sleep; a biometric data processing unit that performs frequency analysis on the bio-vibration data acquired by the biometric data acquisition unit and acquires the average of the frequency spectrum of areas determined to be other than wakefulness; and a detection unit that acquires an approximation curve for the logarithm of the average of the frequency spectrum obtained by the biometric data processing unit, detects the amount by which the logarithm of the average of the frequency spectrum obtained by the biometric data acquisition unit deviates from the acquired approximation curve in the positive or negative direction, and determines whether the subject has sleep apnea syndrome based on the magnitude of the detected amount of deviation in the positive or negative direction from the approximation curve.

[0011] Furthermore, the sleep apnea syndrome determination method of the present invention is This is a sleep apnea syndrome diagnosis method that uses computer calculations to determine whether a person has sleep apnea syndrome, and the calculation process performed by the computer is as follows: A biometric data acquisition process is performed to obtain bio-vibration data from the subject's heart rate, respiration, and body movements during sleep. Another biometric data acquisition process is performed to perform frequency analysis on the bio-vibration data obtained by the biometric data acquisition process and obtain the average frequency spectrum of the areas determined to be other than wakefulness. A determination is then made as to whether the subject has sleep apnea syndrome based on the magnitude of the deviation of the logarithm of the average frequency spectrum obtained by the biometric data acquisition process from the approximation curve obtained by the approximation curve acquisition process, either in the positive or negative direction. The determination to be made Includes processing.

[0012] Furthermore, the program of the present invention is a program that causes a computer to execute each of the processes performed by the above-described method for determining sleep apnea syndrome as a set of steps. [Brief explanation of the drawing]

[0013] [Figure 1] This is a block diagram showing an example of the configuration of a sleep apnea syndrome detection device according to one embodiment of the present invention. [Figure 2] This figure shows an example of the determination state for sleep apnea syndrome according to one embodiment of the present invention. [Figure 3] This is a block diagram showing an example of the hardware configuration of a sleep apnea syndrome detection device according to one embodiment of the present invention. [Figure 4] This figure shows an example of a process for obtaining a power spectrum during sleep at regular intervals according to one embodiment of the present invention. [Figure 5] Figure 5A shows an example of changes in sleep stages during sleep in a healthy individual. Figure 5B shows an example of changes in sleep stages during sleep in a patient with sleep apnea syndrome. [Figure 6] This flowchart shows the processing flow for determining sleep apnea syndrome according to one embodiment of the present invention. [Figure 7] Figure 7A shows an example of the contribution of vibrations to frequency during sleep in healthy individuals. Figure 7B shows an example of the average vibration frequency during the wakefulness (W) interval in healthy individuals during sleep. Figure 7C shows an example of the average vibration frequency during non-wakefulness intervals in healthy individuals during sleep. Figure 7D shows an example of the logarithmic value of the average vibration frequency during the wakefulness (W) interval in healthy individuals during sleep. Figure 7E shows an example of the logarithmic value of the average vibration frequency during non-wakefulness intervals in healthy individuals during sleep. [Figure 8]Figure 8A shows an example of the contribution of vibrations to frequency during sleep in patients with sleep apnea syndrome. Figure 8B shows an example of the average vibration frequency during the wakefulness (W) interval of sleep in patients with sleep apnea syndrome. Figure 8C shows an example of the average vibration frequency during the non-wakefulness intervals of sleep in patients with sleep apnea syndrome. Figure 8D shows an example of the logarithmic value of the average vibration frequency during the wakefulness (W) interval of sleep in patients with sleep apnea syndrome. Figure 8E shows an example of the logarithmic value of the average vibration frequency during the non-wakefulness intervals of sleep in patients with sleep apnea syndrome. [Figure 9] This figure shows a representative example of a approximation curve overlaid with the logarithmic average of the oscillation frequencies during non-wakeful intervals in patients with sleep apnea syndrome. [Figure 10] Figure 10A shows the first example, where the logarithmic average of the vibration frequencies in the non-awake intervals of a healthy individual is superimposed on an approximation curve. Figure 10B shows the second example, where the logarithmic average of the vibration frequencies in the non-awake intervals of a healthy individual is superimposed on an approximation curve. Figure 10C shows the third example, where the logarithmic average of the vibration frequencies in the non-awake intervals of a healthy individual is superimposed on an approximation curve. Figure 10D shows the fourth example, where the logarithmic average of the vibration frequencies in the non-awake intervals of a healthy individual is superimposed on an approximation curve. Figure 10E shows the fifth example, where the logarithmic average of the vibration frequencies in the non-awake intervals of a healthy individual is superimposed on an approximation curve. [Figure 11] Figure 11A shows the first example of a patient with sleep apnea syndrome where the logarithmic average of the vibration frequencies during non-wake periods is superimposed on an approximation curve. Figure 11B shows the second example of a patient with sleep apnea syndrome where the logarithmic average of the vibration frequencies during non-wake periods is superimposed on an approximation curve. Figure 11C shows the third example of a patient with sleep apnea syndrome where the logarithmic average of the vibration frequencies during non-wake periods is superimposed on an approximation curve. Figure 11D shows the fourth example of a patient with sleep apnea syndrome where the logarithmic average of the vibration frequencies during non-wake periods is superimposed on an approximation curve. Figure 11E shows the fifth example of a patient with sleep apnea syndrome where the logarithmic average of the vibration frequencies during non-wake periods is superimposed on an approximation curve. [Figure 12]It is a diagram showing an example of a determination result according to an embodiment of the present invention. [Embodiment for Carrying out the Invention]

[0014] Hereinafter, an embodiment of the present invention (hereinafter referred to as "this example") will be described with reference to the drawings. In the following description and drawings, the sleep apnea syndrome is referred to as SAS, and a patient with the sleep apnea syndrome is referred to as a SAS patient. Also, when a healthy person is mentioned in the following description, it indicates a person who is not a SAS patient. [1. Configuration of Sleep Apnea Syndrome Determination Device] FIG. 1 is a block diagram showing the configuration of the sleep apnea syndrome determination device 10 of this example. FIG. 2 is a diagram showing an example of a state in which the sleep apnea syndrome is determined using the sleep apnea syndrome determination device 10 of this example.

[0015] The sleep apnea syndrome determination device 10 of this embodiment acquires the biometric vibration of the subject as pressure data by the mattress sensor 2. This biometric vibration includes, in addition to the vibration component due to the body movement of the subject, the vibration components due to heartbeat and respiration. The mattress sensor 2 detects the biometric vibration of the upper body of the subject A during sleep as a change in pressure. The mattress sensor 2 is used, for example, as shown in FIG. 2, by being laid on or under the mattress of the bed 1 on which the subject A sleeps. Placing the mattress sensor 2 on the mattress on the lower side of the subject A is just an example, and for example, the mattress sensor 2 may be built into the mattress.

[0016] In FIG. 2, an example is shown 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 by a cable. However, for example, the pressure data (biometric vibration data) acquired by the mattress sensor 2 may be transmitted wirelessly to the sleep apnea syndrome determination device 10 in another room. In the following description, the pressure data output by the mattress sensor 2 is referred to as biometric vibration data. Obtaining bio-vibration data from a pressure sensor is just one example; other sensors may also be used. For instance, infrared sensors or lasers may be used to measure the vibrations of a subject during sleep in a non-contact manner.

[0017] As shown in Figure 1, the sleep apnea syndrome detection device 10 comprises a biological data acquisition unit 11, a biological data processing unit 12, a sleep stage determination unit 13, a sleep apnea syndrome detection unit (hereinafter referred to as the "SAS detection unit") 14, and an output unit 15. The bio-data acquisition unit 11 performs bio-data acquisition processing to acquire bio-vibration data output by the mattress sensor 2. The bio-vibration data acquired by the bio-data acquisition unit 11 is supplied to the bio-data processing unit 12.

[0018] The bio-data processing unit 12 samples the supplied bio-vibration data, converts it into digitized data, and calculates the power spectrum of the frequency of the digitized bio-vibration data. This process of calculating the power spectrum of the frequency of the bio-vibration data is performed in 30-second cycles. However, in this example, one calculation is actually performed for 32 seconds, and these 32-second calculations are repeated in 30-second cycles, meaning they overlap by only 2 seconds.

[0019] Note that the calculation of the power spectrum by the biological data processing unit 12 at a 30-second cycle is just one example; the power spectrum may be calculated at a shorter or longer cycle. For example, the biological data processing unit 12 may calculate the power spectrum at a 60-second cycle. The 2-second overlap in each calculation is also just one example; there may be no overlapping period. The biological data processing unit 12 then supplies the calculated power spectrum results for each period of time during sleep to the sleep stage determination unit 13 and the SAS determination unit 14.

[0020] The sleep stage determination unit 13 determines the sleep stage of the subject during a given period based on the calculation results of the power spectrum for each period. When determining the sleep stage, various features obtained by calculating bio-vibration data may be used in addition to the power spectrum. Alternatively, the sleep stage may be determined from the feature data using a random forest, which is a type of machine learning.

[0021] Sleep stages, from lightest to heaviest, are WAKE, REM sleep, and non-REM sleep. Non-REM sleep is further divided into four stages (NR1-NR4), from Stage 1 to Stage 4. Therefore, there are a total of six sleep stages. Of these six stages, Stage 4, non-REM sleep (NR4), is the deepest. However, it is rare for people to actually experience such a deep sleep stage as Stage 4 non-REM sleep. It should be noted that the sleep stage determination unit 13 in this example does not need to determine all six sleep stages; it only needs to determine whether or not the person is awake. Furthermore, although the sleep stage determination unit 13 in this example determines the sleep stage from biological vibration data, the sleep stage determination unit 13 may also determine the sleep stage from other biological data such as electroencephalograms (EEGs).

[0022] The SAS determination unit 14 determines whether or not the subject has sleep apnea syndrome (SAS) based on the power spectrum calculation results for a certain period and the sleep stage determination results from the sleep stage determination unit 13. Note that using sleep stages when the SAS determination unit 14 determines whether or not the subject has SAS is merely one example, and it is not necessary to use the sleep stage determination results to determine SAS. The details of the process by which the SAS determination unit 14 determines SAS based on the calculation results of the power spectrum at regular intervals will be described later.

[0023] The output unit 15 outputs the result of whether or not the SAS determination unit 14 has determined sleep apnea syndrome. The output unit 15 is configured as, for example, a display device and displays the sleep apnea syndrome determination result. Alternatively, the output unit 15 may be configured as a recording device to record the sleep apnea syndrome determination result along with the sleep state for the night. Furthermore, when the output unit 15 displays or records, it may simultaneously display or record not only the sleep apnea syndrome determination result but also the sleep stage determination result.

[0024] Furthermore, the output unit 15 may be configured to transmit the judgment result via a network, acting as an external terminal. For example, the output unit 15 may be configured to transmit the judgment result as a pre-registered smartphone. In this example, the sleep apnea syndrome detection device 10 may perform data acquisition and detection during sleep in real time. Alternatively, the sleep apnea syndrome detection device 10 may have a biological data acquisition unit 11 that only acquires biological vibration data during sleep, records the acquired data, and then performs the detection process using the recorded data at a later date.

[0025] [2. Example Hardware Configuration of a Sleep Apnea Syndrome Detection Device] Figure 3 shows an example of the hardware configuration when the sleep apnea syndrome diagnosis device 10 is configured as a computer device. Computer device C comprises a CPU (Central Processing Unit) C1, ROM (Read Only Memory) C2, and RAM (Random Access Memory) C3 connected to bus C8. Furthermore, computer device C includes non-volatile storage C4, a network interface C5, an input device C6, and a display device C7.

[0026] CPU C1 reads and executes the program code for the software that implements the functions of the biological data processing unit 12, sleep stage determination unit 13, and SAS determination unit 14 of the sleep apnea syndrome detection device 10 from ROM C2. For the processes of frequency analysis of pressure data, sleep stage determination, and SAS determination, CPU C1 reads the programs that perform these processes from ROM C2 and executes them. Variables and parameters that occur during the calculation process are temporarily written to RAM C3.

[0027] Non-volatile storage C4 can include, for example, an HDD (Hard disk drive), SSD (Solid State Drive), flexible disk, optical disk, magneto-optical disk, CD-ROM, or non-volatile memory. This non-volatile storage C4 stores the OS (Operating System), various parameters, and a program that allows the computer device C to function as a sleep apnea syndrome detection device 10, and serves as a recording medium for storing the program. In addition, data on the sleep stage 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 non-volatile storage C4.

[0028] Network interface C5 can use, for example, a NIC (Network Interface Card), and can transmit and receive various types of data via a LAN (Local Area Network), dedicated line, etc., to which its terminals are connected. For example, computer device C acquires pressure data output by mattress sensor 2 via network interface C5. The input device C6 consists of, for example, a keyboard, and is used to set the period for determining sleep apnea syndrome using the sleep apnea syndrome determination device 10, and to instruct the display format of the determination results. The display device C7 displays the sleep apnea syndrome determination results from the sleep apnea syndrome determination device 10.

[0029] It should be noted that the sleep apnea syndrome detection device 10 is composed of a computer device that functions as a detection device by executing a program (software) recorded on a recording medium; however, dedicated hardware may be provided to perform some or all of the processing of the sleep apnea syndrome detection device 10.

[0030] [3. Example of calculation status of frequency power spectrum and determination of sleep stage] Next, we will explain the processes performed in each part of the sleep apnea syndrome detection device 10 in this example. First, referring to Figure 4, we will explain an example in which the biodata processing unit 12 calculates the power spectrum of the frequency of bio-vibrations during sleep over a certain period. As already explained, in this example, the biodata processing unit 12 processes 32 seconds of data at 30-second intervals. Specifically, as shown in Figure 4, the bio-data acquisition unit 11 first acquires sensor values ​​from the mattress sensor 2 from the time of falling asleep (0 seconds) to 32 seconds, and then acquires sensor values ​​for 32 seconds at 30-second intervals. The bio-data acquisition unit 11 continuously acquires this bio-vibration data from the time of falling asleep until waking up, and supplies the acquired bio-vibration data to the bio-data processing unit 12.

[0031] The biological data processing unit 12 then calculates the power spectrum of the frequency over a 32-second period, as shown in the lower right of Figure 4. This frequency analysis to calculate the power spectrum is performed, for example, by the Fast Fourier Transform (FFT). When calculating the power spectrum of bio-vibrations during sleep, as in this example, the frequencies showing high density are the components of bio-vibrations due to heartbeat and respiration. Normally, the frequency of heartbeat is around 1 Hz, and the component of bio-vibrations due to respiration is around 2 Hz. However, in the case of sleep apnea patients, there are periods when no bio-vibrations occur due to respiration. Also, in periods with large body movements such as turning over in bed, a component of bio-vibration due to body movement is generated that is much larger than that of heartbeat or respiration.

[0032] The sleep stage determination unit 13 determines the dominant sleep stage over a 30-second period from the power spectrum of bio-vibrations, as shown in Figure 4. The method for determining the sleep stage from the power spectrum of bio-vibrations is applicable to methods already proposed by the inventors of this application. While not explained here, this method allows for the determination of the sleep stage from the occurrence of different feature quantities for each sleep stage. In the example in Figure 4, the first 30 seconds after falling asleep are determined to be wakeful (W), and the next 30 seconds are determined to be stage 2 non-REM (N2).

[0033] Figure 5 compares an example of measuring the sleep stages of a healthy individual during a certain sleep period (Figure 5A) with an example of measuring the sleep stages of a patient with sleep apnea syndrome (SAS patient) (Figure 5B).

[0034] In Figure 5, the horizontal axis represents sleep duration, and the vertical axis represents sleep stages. The vertical axis of sleep stages shows that the uppermost part represents wakefulness, the lightest sleep stage, and as you move downwards, the sleep stages progress sequentially to deeper sleep stages: REM sleep, Stage 1 non-REM sleep (NREM1), Stage 2 non-REM sleep (NREM2), Stage 3 non-REM sleep (NREM3), and Stage 4 non-REM sleep (NREM4). However, in the example in Figure 5, in both the healthy individual shown in Figure 5A and the sleep apnea patient shown in Figure 5B, the deepest sleep stage is Stage 3 non-REM sleep (NREM3), and Stage 4 non-REM sleep (NREM4) is not reached.

[0035] As can be seen by comparing healthy individuals in Figure 5A with sleep apnea patients in Figure 5B, sleep apnea patients experience frequent awakenings (WAKE) due to apnea. In patients with sleep apnea (SAS), the tongue obstructs the airway during wakefulness, causing apnea, which manifests as changes in heart rate.

[0036] In this example, the sleep apnea syndrome detection device 10 primarily determines SAS based on characteristics appearing in the frequency spectrum of intervals other than wakefulness, using the processing procedure described below. However, determining SAS from the frequency spectrum of intervals other than wakefulness is just one example; as will be described later, it is also possible to determine SAS using the frequency spectrum of the wakefulness interval, or using the frequency spectrum of all intervals without distinguishing between wakefulness and wakefulness.

[0037] [4. Flowchart for determining sleep apnea syndrome (SAS)] Figure 6 is a flowchart showing the process flow for determining SAS using the sleep apnea syndrome detection device 10 in this example. First, the bio-data acquisition unit 11 acquires bio-vibration data during sleep (step S11). This bio-vibration data may be real-time data acquired during sleep, or it may be recorded bio-vibration data.

[0038] Next, the bio-data processing unit 12 calculates feature quantities of bio-vibration data at regular intervals (step S12). Then, the sleep stage determination unit 13 sets a sleep stage at regular intervals (30 seconds) based on the feature quantities calculated by the bio-data processing unit 12, and labels the set sleep stage with the bio-vibration data for the corresponding interval (step S13).

[0039] Next, the SAS determination unit 14 obtains the frequency spectrum, which is the frequency analysis result of the bio-vibration data for the interval determined by the sleep stage determination unit 13 to be other than wakefulness, from the bio-data processing unit 12 (step S14). Here, we will explain an example in which bio-vibration data for intervals other than wakefulness is obtained and the subsequent processing is carried out. In carrying out the processing in this example, there are three cases in which bio-vibration data to be obtained: obtaining bio-vibration data for intervals determined to be wakefulness, obtaining bio-vibration data for intervals determined to be other than wakefulness, and obtaining bio-vibration data for both intervals determined to be wakefulness and intervals determined to be other than wakefulness. The SAS determination unit 14 calculates the average of all frequency spectra in intervals other than the acquired wake interval, and then performs a logarithmic calculation (for example, log2 calculation) on the average of that frequency spectrum to obtain a logarithmic value (log operation value) (step S15).

[0040] The SAS determination unit 14 then calculates an approximate curve of the change in the logarithm of the mean of the frequency spectrum and performs a process to acquire the approximate curve (approximate curve acquisition process) (step S16). The SAS determination unit 14 employs, for example, the least squares method as a method for calculating the approximate curve. Note that when calculating this approximate curve, some values, such as the lowest frequency, may be excluded from the calculation.

[0041] Next, the SAS determination unit 14 compares the calculated approximation curve with the logarithm of the mean of the frequency spectrum and calculates the size of the areas where the logarithm of the mean is greater than the approximation curve (step S17). In this example, the method used to calculate the size of the areas where the logarithm of the mean is greater than the approximation curve is calculated on the graph, as explained in Figure 7 below.

[0042] Then, the SAS determination unit 14 determines whether or not it is SAS by comparing the size (area) calculated in step S17 with a pre-prepared threshold for determination (step S18). That is, if the area calculated in step S17 is greater than or equal to the threshold, it is determined to be SAS, and if the area calculated in step S17 is less than the threshold, it is determined not to be SAS. The judgment result is output from the output unit 15.

[0043] Figure 7 shows the frequency spectrum obtained from a single sleep period in a healthy individual, and the data obtained after processing that frequency spectrum. Figure 8 shows the frequency spectrum obtained from a single sleep period in a patient with sleep apnea syndrome (SAS), and the data obtained after processing that frequency spectrum. In Figures 7 and 8, Figures 7A and 8A show the contribution of the frequency spectrum acquired during a single sleep cycle to the overall frequency. Figures 7B and 8B show the average of all frequency spectra during the wakefulness interval. Figures 7C and 8C show the average of all frequency spectra during the non-wake interval. Furthermore, Figures 7D and 8D show the logarithmic values ​​obtained from the average of all frequency spectra during the wakefulness interval in Figures 7B and 8B, and Figures 7E and 8E show the logarithmic values ​​obtained from the average of all frequency spectra during the non-wake interval in Figures 7C and 8C. Figures 7A and 8A show frequency on the vertical axis and contribution on the horizontal axis, while Figures 7B to 7E and 8B to 8E show frequency on the horizontal axis and density on the vertical axis, respectively.

[0044] As can be seen by comparing Figure 7A and Figure 8A, there are some differences in the frequency spectrum distribution of healthy individuals and the contribution of the frequency spectrum of SAS patients. However, in Figures 7B and 8B, which show the average of each figure, and in Figures 7C and 8C, no clear difference appears between healthy individuals and SAS patients. On the other hand, Figures 7D and 8D, and Figures 7E and 8E, which show values ​​obtained by calculating the logarithm from the average of the frequency spectrum, show relatively large differences between healthy individuals and SAS patients at frequencies around 3 Hz. Specifically, for both the logarithm of the average of the wakefulness interval shown in Figures 7D and 8D, and the logarithm of the average of the non-wake interval shown in Figures 7E and 8E, the logarithm of the average is higher for SAS patients in Figure 8 than for healthy individuals in Figure 7 at frequencies around 3 Hz. In particular, the logarithm of the average of the non-wake interval shows an even more pronounced difference in the value for SAS patients. In this example, the SAS determination unit 14 determines whether a person has SAS based on the difference between the logarithm of the mean between healthy individuals and SAS patients.

[0045] Here, we will explain in more detail how a sleep apnea syndrome (SAS) can be identified from a frequency around 3 Hz. A vibrational phenomenon called micro-vibration is observed on the surface of the body, and the 3-4 Hz component becomes stronger when the arousal level is low and sleepy. From this result, it is assumed that SAS patients frequently repeat wake-sleep cycles and fall into a low arousal level when transitioning from wake to sleep, so when the logarithmic spectrum over one night is averaged, the 3 Hz frequency component is strongly present in SAS patients. On the other hand, in healthy individuals, since they do not frequently cycle between wakefulness and sleep, it is assumed that the 3Hz frequency component will appear less frequently overall when averaging the logarithmic spectrum over one night. Therefore, it is possible to appropriately determine whether someone has sleep apnea syndrome (SAS) based on frequencies around 3Hz.

[0046] Next, referring to Figure 9, we will explain an example of finding an approximate curve in step S16 of the flowchart in Figure 6. Figure 9 shows an example of finding an approximate curve C1 using the least squares method for the logarithm of the mean of the non-wake interval (NW1) in SAS patients. Furthermore, when the SAS determination unit 14 determines the approximation curve C1, it excludes a few points (in this case, two points) from the lowest frequency within the logarithmic value NW1 of the mean (values ​​in the range x of Figure 9) and performs the calculation using the least squares method. The resulting approximation curve, regardless of the data from any subject, shows that the highest value is at a low frequency, and as the frequency increases, the curve becomes gentler and gradually decreases to a lower value.

[0047] The SAS determination unit 14 then compares the approximation curve C1 with the logarithmic value of the mean NW1. In the case of the SAS patient shown in Figure 9, there is a continuous interval in the frequency band near 3 Hz where the logarithmic value NW1 is relatively larger than the approximation curve C1. Therefore, the area value Supper, which represents the region on the graph where the logarithmic value NW1 is larger than the approximation curve C1, will be a relatively large value. Note that since the approximation curve C1 basically reflects (approximates) the state of the average logarithmic value NW1, the difference between the area value Sunder, which represents the region where the logarithmic value NW1 is smaller than the approximation curve C1, and the area value Supper, which represents the region where the logarithmic value NW1 is larger than the approximation curve C1, is small. Therefore, Sunder, the area value of the region where the logarithmic value NW1 is smaller than the approximation curve C1, will be a large value, corresponding to Supper, the area value of the region where the logarithmic value NW1 is larger than the approximation curve C1.

[0048] In this example, the SAS determination unit 14 can determine that a person is a SAS patient if, as can be seen by comparing the approximation curve C1 and the logarithmic value NW1 shown in Figure 9, the logarithmic value NW is greater than the approximation curve C1 at frequencies around 3 Hz and the shape exhibits unimodality. On the other hand, even if there are regions where the logarithmic value NW is greater than the approximation curve C1 at frequencies around 3 Hz, if the shape does not exhibit unimodality, the person is determined not to be an SAS patient. It is also possible to determine whether a person is an SAS patient based on the magnitude of the region exhibiting unimodality where the logarithmic value NW is greater than the approximation curve C1, that is, based on the magnitude of the positive deviation. If the magnitude of the positive deviation of the region exhibiting unimodality is greater than or equal to a set threshold, the person is determined to be an SAS patient; if it is less than the set threshold, the person is determined not to be an SAS patient. This can improve the accuracy of determining whether a person is an SAS patient.

[0049] In this example, a patient was identified as having SAS if the logarithmic value NW was greater than the approximation curve C1 at frequencies around 3 Hz, indicating a unimodal shape. However, detecting at frequencies around 3 Hz is just one example; even at frequencies outside of 3 Hz, a patient may be identified as having SAS if the logarithmic value NW is greater and indicates a unimodal shape. However, detection at frequencies around 3 Hz results in higher accuracy.

[0050] Figure 9 shows an example of one SAS patient, while Figures 10 and 11 show examples of multiple (five) healthy individuals and SAS patients. Figures 10A, 10B, 10C, 10D, and 10E show the logarithmic values ​​NW11 to NW15 of the mean non-wake intervals for five healthy individuals, and their approximate curves C11 to C15. Figures 11A, 11B, 11C, 11D, and 11E show the logarithmic mean values ​​NW21 to NW25 of the non-wake intervals for five SAS patients, along with their approximation curves C11 to C15.

[0051] As can be seen from the logarithmic values ​​NW11-NW15 and their approximation curves C11-C15 for the five healthy individuals in Figures 10A-10E, in the case of healthy individuals, there are almost no frequencies in any of the examples where the logarithmic values ​​NW11-NW15 deviate significantly from the approximation curves C11-C15. On the other hand, as can be seen from the logarithmic values ​​NW21-NW25 and their approximation curves C21-C25 for the five SAS patients in Figures 11A-11E, in the case of SAS patients, a region occurs in the frequency band around 3 Hz where the logarithmic values ​​NW21-NW25 are larger than the approximation curves C21-C25. Also, in the frequency band of about 1 Hz to 2 Hz, a region occurs where the logarithmic values ​​NW21-NW25 are smaller than the approximation curves C21-C25. Moreover, for all of the logarithmic values ​​NW21-NW25 of the SAS patients, the region where they are larger than the approximation curves C21-C25 has a unimodal shape.

[0052] Here, we show an example comparing only five healthy individuals with five individuals with sleep apnea syndrome (SAS). However, in experiments conducted by the inventor of this invention, the results showed that for most subjects, the trends were almost the same for both healthy individuals and those with SAS.

[0053] Figure 12 shows the area values ​​(Supper) and (Sunder), respectively, of the region where the logarithmic value obtained from the vibration data of 18 subjects a-r during a single sleep cycle, and the area where the logarithmic value is larger than the approximation curve. The vertical axis of Figure 12 represents the area values. In the bar graphs for each subject a-r, the left side represents the value Supper and the right side represents the value Sunder.

[0054] Of the 18 subjects a-r, nine subjects a-i were sleep apnea patients, while the remaining nine subjects j-r were healthy individuals. For both the supper and sunder values, the measurements of healthy individuals j-r differed significantly from those of sleep apnea patients a-i.

[0055] Therefore, as shown in Figure 12, the SAS determination unit 14 sets a threshold TH1 for determination between the measured values ​​of healthy individuals j to r and the measured values ​​of SAS patients a to i, and compares the value Supper or Sunder with the threshold TH1. This allows the SAS determination unit 14 to accurately diagnose whether or not a person suffers from sleep apnea syndrome by determining that they are an SAS patient when the value is greater than or equal to the threshold TH1, and not an SAS patient when the value is less than the threshold TH1.

[0056] As explained above, the sleep apnea syndrome detection device 10 in this example makes it possible to accurately identify patients with sleep apnea syndrome based on bio-vibration data derived from the subject's body movements or pressure changes during sleep. Moreover, the data detected by the sleep apnea syndrome detection device 10 in this example is bio-vibration data that can be measured with a mattress sensor 2, etc., and it becomes possible to easily and accurately determine sleep apnea syndrome simply by sleeping with a pressure sensor such as a mattress placed on the body. Therefore, the sleep apnea syndrome detection device 10 in this example has the advantage of placing significantly less burden on the subject compared to conventional methods that involve attaching electrodes or other devices to the subject's body for detection.

[0057] It should be noted that, in this example, the sleep apnea syndrome (SAS) detection device may, in a small percentage of cases, incorrectly identify healthy individuals who do not have SAS as having the condition. However, when the health status of these incorrectly identified healthy individuals is examined in detail, they all exhibit characteristics similar to those of SAS patients, such as being above average weight. Therefore, this suggests that the device may have screened individuals with tendencies similar to those of SAS patients, and it may also be possible to detect early signs of SAS.

[0058] [5. Variant] The processes described in the above-mentioned embodiment examples are merely preferred examples and are not limited to those described in the embodiment examples. For example, the SAS determination unit 14 compares the logarithm of the mean with the approximation curve, calculates the area where the logarithm of the mean is larger or smaller than the approximation curve, and compares that area with a threshold. Alternatively, as can be seen in Figures 9 and 11, the SAS determination unit 14 may determine whether the logarithm of the mean deviates significantly from the approximation curve near 3 Hz, which is a characteristic specific to SAS patients. Furthermore, in the above-described embodiment, the approximation curve was calculated using the least squares method, but a similar approximation curve may be calculated using other calculation methods instead of the least squares method.

[0059] Furthermore, in the above-described embodiment, the bio-data acquisition unit, which acquires bio-vibration data due to the subject's heart rate, respiration, and body movements during sleep, is configured to acquire bio-vibration data from a mattress sensor. However, other sensors may be used if they can similarly acquire bio-vibration data due to the subject's heart rate, respiration, and body movements during sleep. [Explanation of Symbols]

[0060] 1...Bed, 2...Mattress sensor, 10...Sleep apnea syndrome detection device, 11...Biometric data acquisition unit, 12...Biometric data processing unit, 13...Sleep stage determination unit, 14...Sleep apnea syndrome detection 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

1. A biometric data acquisition unit that acquires bio-vibration data from the subject's heart rate, respiration, and body movements during sleep, A biological data processing unit performs frequency analysis on the biological vibration data acquired by the biological data acquisition unit and obtains the average of the frequency spectrum of the areas determined to be other than those of the person awake. The system includes a sleep apnea syndrome determination unit that determines whether the subject has sleep apnea syndrome based on the unimodality of the logarithm of the mean of the frequency spectrum obtained by the biological data processing unit. Sleep apnea syndrome determination device.

2. The sleep apnea syndrome determination unit calculates an approximation curve for the logarithm of the mean of the frequency spectrum, and determines the unimodality of the logarithm of the mean of the frequency spectrum by comparing the logarithm of the mean of the frequency spectrum with the approximation curve. A sleep apnea syndrome detection device according to claim 1.

3. When calculating the aforementioned approximation curve, the lowest frequency component included in the bio-vibration data is excluded from the calculation. The sleep apnea syndrome detection device according to claim 2.

4. Furthermore, it includes 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 bio-vibration data. The sleep apnea syndrome determination unit determines whether the sleep stage determined by the sleep stage determination unit is a sleep stage other than wakefulness, a sleep stage of wakefulness, or a sleep stage of both wakefulness and non-wakefulness, by obtaining the logarithm of the average value for each frequency. A sleep apnea syndrome detection device according to claim 1 or 2.

5. The sleep apnea syndrome determination unit calculates an approximation curve for the logarithm of the mean of the frequency spectrum obtained by the biological data acquisition unit, detects the amount by which the logarithm of the mean of the frequency spectrum obtained by the biological data acquisition unit deviates from the calculated approximation curve in the positive or negative direction, and determines whether the subject has sleep apnea syndrome based on the magnitude of the detected deviation from the approximation curve in the positive or negative direction. A sleep apnea syndrome detection device according to claim 1.

6. The sleep apnea syndrome determination unit determines whether the subject has sleep apnea syndrome based on the magnitude of the deviation of the logarithm of the average around 3 Hz from the approximation curve in the positive direction. The sleep apnea syndrome detection device according to claim 5.

7. A method for determining whether a person has sleep apnea syndrome, which is determined by computer calculations, The calculation process performed by the aforementioned computer is as follows: A biometric data acquisition process that obtains bio-vibration data from the subject's heart rate, respiration, and body movements during sleep, A bio-data processing method which involves frequency analysis of the bio-vibration data acquired by the bio-data acquisition process and obtaining the average of the frequency spectrum of the areas determined to be other than those of the person awake, An approximation curve acquisition process is performed to obtain an approximation curve for the logarithm of the mean of the frequency spectrum obtained by the above-mentioned biological data processing, This includes a determination process that determines whether the subject has sleep apnea syndrome based on the unimodality of the logarithm of the mean of the frequency spectrum obtained by the above-mentioned biological data processing. How to determine sleep apnea syndrome.

8. A procedure for acquiring biometric data to obtain bio-vibration data from the subject's heart rate, respiration, and body movement during sleep, A biodata processing procedure which involves frequency analysis of the bio-vibration data acquired by the bio-data acquisition procedure and obtaining the average of the frequency spectra of areas determined to be other than those of the person awake, A procedure for obtaining an approximate curve for obtaining an approximate curve for the logarithm of the mean of the frequency spectrum obtained by the above-mentioned biological data processing procedure, A determination procedure for determining whether the subject has sleep apnea syndrome based on the unimodality of the logarithm of the mean of the frequency spectrum obtained by the above-mentioned biological data processing procedure, A program that causes a computer to execute something.

9. The sleep apnea syndrome detection unit further determines whether the subject has sleep apnea syndrome by also considering the average frequency spectrum of the areas determined to be awake, or the average frequency spectrum of both the areas determined to be awake and the areas determined to be other. A sleep apnea syndrome detection device according to claim 1.

10. The aforementioned biological data acquisition unit detects biological vibration data from a pressure sensor installed on or inside the mattress. A sleep apnea syndrome detection device according to claim 1.

11. In the aforementioned determination process, the average frequency spectrum of the areas determined to be awake, or the average frequency spectrum of both the areas determined to be awake and the areas determined to be otherwise, is also considered to determine whether the subject has sleep apnea syndrome. The method for determining sleep apnea syndrome according to claim 7.

12. In the aforementioned determination procedure, the average frequency spectrum of the areas determined to be awake, or the average frequency spectrum of both the areas determined to be awake and the areas determined to be other, is also considered to determine whether the subject has sleep apnea syndrome. The program according to claim 8.

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