Snoring determination system, snoring determination method, and program

Abstract

To provide a snoring determination system capable of suitably determining the existence of snoring from a voice signal by a person to be measured.SOLUTION: A snoring determination system includes: measurement means (S201) for measuring transition of a noise level or sound pressure level of respiratory sound generated from a sleeping person to be measured; frequency spectrum acquisition means (S202) for acquiring a frequency spectrum of the transition of the noise level or sound pressure level; peak identification means (S203) for identifying a plurality of frequency peaks on the basis of the frequency spectrum; and determination means (S205) for determining the existence of snoring by the person to be measured on the basis of an intensity ratio of a first peak having the highest intensity to a second peak having the next highest intensity of the plurality of frequency peaks.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present invention relates to a snoring determination system, a snoring determination method, and a program.

Background Art

[0002] Patent Document 1 below discloses a sleep aid device that can suppress the snoring of a subject during sleep and provide a comfortable sleep. This sleep aid device includes a detection device that acquires an audio signal by a microphone installed near the subject and detects snoring from the audio signal. Specifically, in the detection device, an envelope of the audio signal is extracted, and when the periodicity of the envelope continues for a certain period of time, it is determined that the subject during sleep is snoring.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The audio signal acquired from the subject during sleep by the microphone contains various frequency components, and during snoring, the periodicity of the envelope does not necessarily continue for a certain period of time ideally.

[0005] The present invention has been made in view of the above problems, and an object thereof is to provide a snoring determination system, a snoring determination method, and a program that can preferably determine the presence or absence of snoring from an audio signal by a subject.

Means for Solving the Problems

[0006] (1) To solve the above problems, the snoring determination system according to the present invention includes a measuring means for measuring the transition of the noise level or sound pressure level of the breathing sound emitted from the subject during sleep, a frequency spectrum acquisition means for acquiring the frequency spectrum of the transition of the noise level or the sound pressure level, a peak identification means for identifying a plurality of frequency peaks based on the frequency spectrum, and a determination means for determining the presence or absence of snoring by the subject based on the intensity ratio between a first peak having the largest intensity and a second peak having the next largest intensity among the plurality of frequency peaks.

[0007] (2) In the snoring determination system according to (1) above, the determination means may determine the presence or absence of snoring by the subject based on the magnitude of the intensity of the first peak.

[0008] (3) In the snoring determination system according to (1) or (2) above, the measuring means may be a noise meter.

[0009] (4) The snoring determination system according to any one of (1) to (3) above may further include a Doppler sensor for acquiring Doppler data indicating the movement of the chest of the subject. The determination means may determine whether the waveform of the Doppler data and the waveform of the noise level or sound pressure level are synchronized based on the Doppler data and the transition of the noise level or sound pressure level. Further, the presence or absence of snoring by the subject may be determined based on whether they are synchronized.

[0010] (5) In the snoring determination system according to (4) above, the determination means may determine whether the waveforms are synchronized based on the deviation between the timing of the maximum point of the waveform of the Doppler data and the timing of the maximum point of the waveform of the noise level or sound pressure level.

[0011] (6) In the snoring determination system according to (4), when the difference between the respiration rate calculated based on the frequency spectrum of the Doppler data and the respiration rate calculated based on the frequency spectrum of the transition of the noise level or sound pressure level is less than a predetermined threshold value, it may be determined that the subject snores.

[0012] (7) The snoring determination system according to any one of (1) to (6) may further include a sleep depth acquisition unit that acquires the sleep depth of the subject, and the determination unit may determine the presence or absence of snoring by the subject based further on the sleep depth of the subject.

[0013] (8) The snoring determination method according to the present invention includes a measurement step of measuring the transition of the noise level or sound pressure level of the breathing sound emitted from the subject during sleep, a frequency spectrum acquisition step of acquiring the frequency spectrum of the transition of the noise level or the sound pressure level, a peak identification unit that identifies a plurality of frequency peaks based on the frequency spectrum, and a determination step of determining the presence or absence of snoring by the subject based on the intensity ratio between a first peak having the largest intensity and a second peak having the next largest intensity among the plurality of frequency peaks.

[0014] (9) The program according to the present invention is a program for causing a computer to function as means for acquiring the transition of the noise level or sound pressure level of the breathing sound emitted from the subject during sleep, means for acquiring the frequency spectrum of the transition of the noise level or the sound pressure level, means for identifying a plurality of frequency peaks based on the frequency spectrum, and means for determining the presence or absence of snoring by the subject based on the intensity ratio between a first peak having the largest intensity and a second peak having the next largest intensity among the plurality of frequency peaks. This program may be stored in a computer-readable information storage medium such as a semiconductor memory or a magneto-optical disk.

Advantages of the Invention

[0015] According to the present invention, it becomes possible to suitably determine the presence or absence of snoring from the voice signal of the person to be measured.

Brief Description of the Drawings

[0016]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Mode for Carrying Out the Invention

[0017] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

[0018] (Embodiment 1) FIG. 1 is a block diagram of a snoring determination system according to Embodiment 1 of the present invention. The snoring determination system 1 shown in the figure is arranged, for example, on the side of the bed in the bedroom, and determines whether or not the person to be measured who is sleeping in the bed is snoring from the breathing sound emitted by the person to be measured.

[0019] As shown in the figure, this snoring determination system 1 includes a sound collection unit 2 and a signal processing device 3. The sound collection unit 2 is for acquiring sound data of the breathing sound emitted from the subject during sleep, and is configured using, for example, a microphone or a noise meter. When using a microphone, time-series data indicating the transition of the sound pressure level (loudness of the sound) is generated from the raw data of the breathing sound acquired, and this is used as the sound data. Generating the sound data from the raw data of the breathing sound acquired by the microphone may be performed, for example, by the sound collection unit 2 itself or by the signal processing device 3.

[0020] On the other hand, when using a noise meter, the sound data acquired by the noise meter is data (time-series data) indicating the transition of the noise level of the breathing sound emitted from the subject. From the perspective of privacy protection, it is desirable to adopt a noise meter rather than a microphone as the sound collection unit 2. Hereinafter, the description will continue assuming that the sound collection unit 2 is a noise meter. Note that a noise meter is a legal measuring instrument defined in the Metrology Law, and there are two types: a general noise meter and a precision noise meter, and either type of noise meter may be adopted as the sound collection unit 2.

[0021] The signal processing device 3 acquires sound data indicating the transition of the noise level from the sound collection unit 2, and determines whether or not the subject is snoring based on the sound data. At this time, the frequency spectrum of the transition of the noise level indicated by the sound data for a certain period is calculated, and the presence or absence of snoring is determined based on this frequency spectrum. The signal processing device 3 may be configured using a known computer including, for example, a CPU and a memory.

[0022] Figure 2 is a flowchart showing the specific operation of the signal processing device 3. The operation shown in the figure is realized by executing the signal processing program according to this embodiment in the signal processing device 3 which is a computer. The signal processing program is executed periodically, for example, once a day, when the subject wakes up, etc.

[0023] In the signal processing device 3, first, sound data is acquired from the sound collection unit 2 (S201). For example, this sound data indicates the change in the noise level of the breathing sound of the subject from the time of falling asleep to the time of waking up. Next, sound data for a predetermined time window (for example, one minute) is cut out from the thus acquired sound data, and FFT (Fast Fourier Transform) is executed on the cut-out sound data. Thereby, a frequency spectrum is obtained for the sound data of the time window. This process is executed for all the time windows set from the beginning to the end of the sound data, that is, from the time of falling asleep to the time of waking up (S202). Then, the peaks included in the frequency spectrum of each time window are identified. Specifically, the frequency and intensity of each peak included in the frequency spectrum are identified. Then, the frequency and intensity of the peak with the largest intensity (the first peak), and the frequency and intensity of the peak with the next largest intensity (the second peak) are identified (S203).

[0024] Thereafter, the processes of S204 to S206 are repeated for each time window. That is, it is determined whether the intensity of the first peak is equal to or greater than the threshold value (S204). If the intensity of the first peak is less than the threshold value, it is considered that the subject did not snore during the time period related to the time window, and the process proceeds to the next time window. On the other hand, if the intensity of the first peak is equal to or greater than the threshold value, then it is determined whether the intensity of the first peak is α times or more the intensity of the second peak (S205). Here, α is a value greater than 1, and may be a value of about 1.8 to 2.0, for example.

[0025] FIG. 3 is a diagram showing the frequency spectrum of sound data when the subject is not snoring, and FIG. 4 is a diagram showing the frequency spectrum of sound data when the subject is snoring. In FIG. 3, the difference between the intensity of the first peak 20 and the intensity of the second peak 21 is small. In the present embodiment, in such a case, it is determined that the subject is not snoring. On the other hand, in FIG. 4, the difference between the intensity of the first peak 22 and the intensity of the second peak 23 is sufficiently large. In the present embodiment, in such a case, it is determined that the subject is snoring.

[0026] Return to FIG. 2. If the intensity of the first peak is not α times or more the intensity of the second peak, proceed to the processing of the next time window (S205). On the other hand, if the intensity of the first peak is α times or more the intensity of the second peak, record the time period of the time window in a file as the section during which the measured person snored (S206), and proceed to the processing of the next time window.

[0027] According to the snore determination system 1 described above, since the determination of snoring is performed using a noise meter, privacy protection can be achieved. In addition, by acquiring the frequency spectrum of the sound data and determining the presence or absence of snoring by the measured person based on the intensity ratio between the first peak and the second peak, the determination accuracy can be improved. Further, when the intensity of the first peak is less than the threshold value, by determining that no snoring has occurred, the determination accuracy of snoring can be further improved.

[0028] (Embodiment 2) FIG. 5 is a configuration diagram of a snore determination system according to Embodiment 2. The snore determination system 10 shown in the figure is also arranged, for example, beside the bed in a bedroom, and determines whether or not the measured person sleeping in the bed is snoring from the breathing sounds emitted by the measured person. The snore determination system 10 includes a sound collection unit 2, a Doppler sensor 40, and a signal processing device 30. The configuration of the sound collection unit 2 is the same as that in Embodiment 1. The Doppler sensor 40 is installed, for example, facing the chest of the measured person near the bed. Microwaves are emitted from the Doppler sensor 40, and the reflected wave at the chest of the measured person is received by the Doppler sensor 40. Due to the Doppler effect, the reflected wave is frequency-shifted, and by observing this, the breathing rate and heart rate of the measured person can be obtained. The reflected wave is detected as Doppler data including an I signal that is the in-phase component of the transmitted wave and a Q signal that is the quadrature component, and is output to the signal processing device 30 in digital form. The Doppler data indicates the movement of the chest of the measured person.

[0029] The signal processing device 30 according to Embodiment 2 may also be configured using a known computer including, for example, a CPU and a memory, similar to the signal processing device 3 according to Embodiment 1. In particular, the signal processing device 30 further accurately determines whether the subject is snoring by using, in addition to the sound data acquired from the sound collection unit 2, Doppler data acquired from the Doppler sensor 40.

[0030] Note that the Doppler data includes components derived from body movement and heartbeats in addition to components derived from breathing. Therefore, in the signal processing device 3, before executing the snoring determination process, a predetermined band-limiting filter (a band-pass filter that extracts the frequency band of breathing) is applied to the raw Doppler data acquired from the Doppler sensor 40.

[0031] FIG. 6 is a diagram showing sound data and Doppler data (after filter application) during the sleep of the subject. The sound data obtained from the subject is shown on the upper side of the figure. The vertical axis represents the amplitude of the sound data, and the horizontal axis represents time. The Doppler data obtained from the subject at the same time is shown on the lower side of the figure. The vertical axis represents the amplitude of the Doppler data, and the horizontal axis represents time. Also, a large number of vertical two-dot chain lines pass through the maximum points of the sound data. Also, the filled circles of the one-dot chain line drawn in the Doppler data indicate the maximum points of the Doppler data.

[0032] As shown in the figure, the sound data during the sleep of the subject and the Doppler data indicating the movement of the subject's chest are usually synchronized. That is, the maximum points of the Doppler data exist near the vertical two-dot chain lines. On the other hand, when the sound data and the Doppler data are not synchronized, for example, it is considered that the sound indicated by the sound data includes a sound not derived from the subject. In such a case, the snoring determination accuracy decreases. Therefore, in the present Embodiment 2, it is determined whether the sound data during the sleep of the subject and the Doppler data indicating the movement of the subject's chest are synchronized, and the presence or absence of snoring by the subject is determined in consideration of the result.

[0033] Specifically, based on the deviation between the timing of the maximum points in the sound data and the timing of the maximum points in the Doppler data, it is determined whether the two data are synchronized. For example, the magnitude of the deviation between the timing (time) of each maximum point in the sound data and the timing of the maximum point in the Doppler data closest to that timing is calculated. And if the average value of the deviation is equal to or less than a predetermined threshold (for example, 0.5 seconds), it may be determined that the sound data and the Doppler data are synchronized.

[0034] Note that here, based on the deviation between the timing of the minimum points in the sound data and the timing of the minimum points in the Doppler data, or based on the deviation between the timing of the maximum and minimum points (extreme value points) in the sound data and the timing of the maximum and minimum points in the Doppler data, it may also be determined whether the two data are synchronized.

[0035] FIG. 7 is a flowchart showing the operation of the signal processing device 30. The operations shown in the figure are realized by executing the signal processing program according to the present embodiment in the signal processing device 30 which is a computer. The signal processing program is also executed periodically, for example, once a day, when the subject wakes up, etc. In the figure, the processes of S701 to S705 and S710 are the same as the processes of S201 to S205 and S206 in FIG. 2 respectively, and the description is omitted here.

[0036] As shown in the figure, in S705, when it is determined that the intensity of the first peak is α times or more the intensity of the second peak, in the second embodiment, the maximum points in the sound data of the current processing time window are specified (S706). Also, the Doppler data of the same time window as the current processing time window is acquired (S707), and the maximum points in the acquired Doppler data are specified (S708).

[0037] Then, the deviation between the maximum point identified in S706 and the maximum point identified in S708 is evaluated, and it is determined whether the deviation is below the standard (S709). For example, as described above, it is determined whether the average value of the deviation magnitude is below the threshold. If the deviation is greater than the standard, the process moves to the next time window. On the other hand, if the deviation is below the standard, the time period of the time window is recorded as the target interval (S710), and the process moves to the next time window.

[0038] According to the second embodiment described above, since the presence or absence of snoring is determined by using Doppler data in combination, the determination accuracy can be improved. In particular, when the sound data contains sounds other than the breathing sounds of the subject to be measured, it is possible to prevent erroneously determining that snoring is occurring.

[0039] (Embodiment 3) In the second embodiment, it is determined whether the sound data and the Doppler data are synchronized by comparing the extreme points of both data. In addition, it may be determined whether the sound data and the Doppler data are synchronized by comparing the respiration rate calculated from the sound data and the respiration rate calculated from the Doppler data.

[0040] FIG. 8 is a flowchart showing a modified example of the operation of the signal processing device 30. The operation shown in the figure is realized by executing the signal processing program according to the present embodiment in the signal processing device 30 which is a computer. The signal processing program is also executed periodically, for example, once a day, when the subject wakes up, etc. In the figure, the processes of S801 to S805 and S810 are the same as the processes of S201 to S205 and S206 in FIG. 2, respectively, and the description is omitted here.

[0041] As shown in the figure, in S805, if it is determined that the intensity of the first peak is α times or more the intensity of the second peak, in the third embodiment, the respiration rate of the subject is calculated based on the frequency of the first peak included in the frequency spectrum of the sound data of the current processing time window (S806). Next, the Doppler data of the current processing time window is acquired (S807), and the respiration rate of the subject is calculated based on the acquired Doppler data (S808). Specifically, the Doppler data is subjected to FFT processing to obtain a frequency spectrum. Then, the respiration rate is obtained from the frequency of the peak with the highest intensity included in the frequency spectrum.

[0042] Thereafter, it is determined whether the respiration rate based on the sound data and the respiration rate based on the Doppler data are approximate (S809). For example, if the difference between the two respiration rates is less than the threshold value, it is determined that the two respiration rates are approximate. If the two respiration rates are not approximate, the process moves to the next time window. On the other hand, if the two respiration rates are approximate, the time zone of the current time window is recorded as an apnea-free interval (S810), and the process moves to the next time window.

[0043] Also in the third embodiment described above, since Doppler data is used in combination to determine the presence or absence of snoring, the determination accuracy can be improved. In particular, when the sound data contains something other than the breathing sound of the subject, it is possible to prevent erroneously determining that snoring is occurring.

[0044] The present invention is not limited to the above-described embodiments, and various modifications are possible, and such modifications are also included in the scope of the present invention. For example, since snoring occurs during so-called REM sleep, the sleep depth of the subject may be acquired, and based on the acquired sleep depth, the presence or absence of snoring by the subject may be determined. That is, when it is not REM sleep, the determination that the subject is snoring may be suppressed or stopped. For example, the presence or absence of body movement of the subject may be acquired from a moving image of the subject or a load sensor provided on the bed, and the sleep depth may be determined from that information. Also, the heart rate of the subject may be calculated from the Doppler data, and based on the heart rate, the sleep depth may be determined.

Description of Signs

[0045] 1,10 Snoring determination system, 2 Sound collection unit, 3,30 Signal processing device, 40 Doppler sensor.

Claims

1. A measuring means for measuring a transition of a noise level or a sound pressure level of a breathing sound emitted from a subject during sleep; a frequency spectrum acquisition means for acquiring a frequency spectrum of the transition of the noise level or the sound pressure level; a peak identification means for identifying a plurality of frequency peaks based on the frequency spectrum; a determination means for determining whether the subject is snoring based on an intensity ratio between a first peak having the highest intensity and a second peak having the second highest intensity among the plurality of frequency peaks; A snoring detection system comprising:

2. The snore detection system according to claim 1, The snoring determination system, wherein the determination means determines whether or not the subject is snoring based on the intensity of the first peak.

3. The snore detection system according to claim 1, The snoring determination system, wherein the measuring means is a sound level meter.

4. The snore detection system according to claim 1, a Doppler sensor for acquiring Doppler data indicative of chest movement of the subject; The determination means determines whether or not the waveform of the Doppler data and the waveform of the noise level or sound pressure level are synchronized based on the Doppler data and the changes in the noise level or sound pressure level, and determines whether or not the person being measured is snoring based on whether or not they are synchronized.

5. The snore determination system according to claim 4, The determination means determines whether or not the waveforms of the Doppler data and the noise level or sound pressure level are synchronized based on the difference between the timing of the extreme points of the waveform of the Doppler data and the timing of the extreme points of the waveform of the noise level or sound pressure level.

6. The snore determination system according to claim 4, The snoring determination system, wherein the determination means determines that the subject is snoring when the difference between the respiratory rate calculated based on the frequency spectrum of the Doppler data and the respiratory rate calculated based on the frequency spectrum of the change in the noise level or sound pressure level is less than a predetermined threshold value.

7. The snore detection system according to claim 1, The method further includes a sleep depth acquisition means for acquiring a sleep depth of the subject, The snoring determination system, wherein the determination means determines whether or not the subject is snoring based further on the depth of sleep of the subject.

8. A measurement step of measuring a transition of a noise level or a sound pressure level of a breathing sound emitted from a subject during sleep; a frequency spectrum acquisition step of acquiring a frequency spectrum of the transition of the noise level or the sound pressure level; a peak identification means for identifying a plurality of frequency peaks based on the frequency spectrum; a determining step of determining whether or not the subject is snoring based on an intensity ratio between a first peak having the highest intensity and a second peak having the second highest intensity among the plurality of frequency peaks; A method for determining snoring comprising:

9. A means for acquiring a transition of a noise level or a sound pressure level of a breathing sound emitted from a subject during sleep; A means for acquiring a frequency spectrum of the change in the noise level or the sound pressure level; means for identifying a plurality of frequency peaks based on the frequency spectrum; and a means for determining whether the subject is snoring based on an intensity ratio between a first peak having the highest intensity and a second peak having the second highest intensity among the plurality of frequency peaks; A program that makes a computer function as a

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