Apparatus and method for snore sound detection based on sound analysis

Abstract

The snoring sound detection device includes one or more microphones that receive sounds produced by the subject, and a controller including circuitry that converts the sounds produced by the subject into received signals, converts the received signals into sound intensity signals, measures periodicity of the sound intensity signals using the one or more sound intensity signals, evaluates the relevance of the periodicity of the sound intensity signals relative to the respiratory rate, and detects snoring sounds using the sound intensity and the relevance of the periodicity of the sound intensity signals relative to the respiratory rate.

Description

[Technical field]

[0001] This application is based on and claims the benefit of priority to U.S. Provisional Application No. 62 / 908,545, filed September 30, 2019, and U.S. Provisional Application No. 62 / 910,408, filed October 3, 2019, the entire contents of which are incorporated herein by reference.

[0002] The present invention relates to an apparatus and method for detecting sounds associated with snoring and for estimating sleep disordered breathing and sleep apnea based on sound analysis. [Background technology]

[0003] Obstructive sleep apnea is the most common form of sleep-disordered breathing (NPL1). Obstructive sleep apnea is a sleep disorder in which breathing is repeatedly interrupted during sleep (NPL2). Sleep apnea not only causes insomnia but also an increased incidence of various diseases and conditions, such as hypertension, heart attack, cardiac arrhythmia, stroke, and depression. Several inventors and researchers have focused on diagnosing obstructive sleep apnea using multi-parametric approaches based on sound analysis (PL1, NPL3). However, most of the parameters adopted by multi-parametric approaches are not suitable for detecting snoring or sleep-disordered breathing. Moreover, many of them are based on existing sound analysis techniques, and frequency bands below 3 Hz have been overlooked in previous studies (NPL4, NPL5).

[0004] Prior art literature Patent literature (Citation List Patent Literature) PL 1 Udantha Abeyratne, Asela Samantha Karunajeewa, Houman Ghaemmaghami, “Multi-parametric analysis of snore sounds for the community screening of sleep apnea with non-gaussianity index, US20120004749A1.

[0005] Citation List Non Patent Literature (Citation List Non Patent Literature) NPL 1 https: / / www.thoracic.org / patients / patient-resources / breathing-in-america / resources / chapter-23-sleep-disordered-breathing.pdf NPL 2 https: / / www.sleepfoundation.org / sleep-apnea NPL 3 Nir Ben-Israel, Ariel Tarasiuk, Yaniv Zigel, “Obstructive apnea hypopnea index estimation by analysis of nocturnal snoring signals in adults,” Sleep 2012 Sep; 35(9):1299-1305. NPL 4 Li-Ang Lee, Yu-Lun Lo, Jen-Fang Yu, Gui-She Lee, Yung-Lun Ni, Ning-Hung Chen, Tuan-Jen Fang, Chung-Guei Huang, Wen-Nuan Cheng, Hsueh-Yu Li, “Snoring sounds predict obstruction sites and surgical response in patients with obstructive sleep apnea syndrome,” Sci Rep.2016; 6:30629. NPL 5 Li-Ang Lee, Jen-Fang Yu, Yu-Lun Lo, Yen-Sheng Chen, Ding-Li Wang, Chih-Ming Cho, Yung-Lun Ni, Ning-Hung Chen, Tuan-Jen Fang, Chung-Guei Huang, Hsueh-Yu Li, “Energy types of snoring sounds in patients with obstructive sleep apnea syndrome:A preliminary observation,” PLoS One 2012; 7(12): e53481. Summary of the Invention

[0006] According to one aspect of the invention, a snoring sound detection device includes one or more microphones that receive sounds produced by a subject, and a controller including circuitry for converting sounds produced by the subject into a received signal, converting the received signal into a plurality of sound intensity signals, measuring periodicity of the sound intensity signals using the one or more sound intensity signals, evaluating the validity of the periodicity of the sound intensity signals with respect to the respiratory rate, and detecting snoring sounds using the sound intensity and the validity of the periodicity of the sound intensity signals with respect to the respiratory rate.

[0007] According to another aspect of the present invention, a method for detecting snoring sounds includes obtaining sounds produced by a subject, converting the sounds produced by the subject into received signals, converting the received signals into sound intensity signals, measuring periodicity of the sound intensity signals using one or more sound intensity signals, evaluating a validity of the periodicity of the sound intensity signals with respect to a respiratory rate, and detecting snoring sounds using the sound intensity and the validity of the periodicity of the sound intensity signals with respect to a respiratory rate.

[0008] A more complete appreciation of the present invention and many of the attendant advantages thereof will be readily obtained as the same becomes better understood by reference to the following detailed description when considered in connection with the accompanying drawings, in which: [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a schematic diagram of a sleep disordered breathing estimation device based on sound analysis, where snoring sounds are detected using an assessment of the periodicity of the sound intensity signal with respect to the breathing rate. [Diagram 2] FIG. 2 is a schematic diagram of a sound analysis based sleep disordered breathing estimation method, where snoring sounds are detected using an assessment of the periodicity of the sound intensity signal with respect to the breathing rate. [Diagram 3] FIG. 3 shows a schematic diagram of a sound analysis based sleep disordered breathing estimation method, in which a Fourier transform is applied to the sound intensity signal to evaluate its periodicity with respect to the respiratory rate. [Figure 4] FIG. 4 is a schematic diagram of an apparatus for snoring sound detection based on sound analysis. [Diagram 5] FIG. 5 is a schematic diagram of a method for snore sound detection based on sound analysis. [Figure 6] FIG. 6 is a schematic diagram of a method for snore sound detection using a process of storing indices to calculate the indices. [Figure 7] FIG. 7 is a schematic diagram of a method for detecting snoring sounds using the condition that the snoring sound has a second harmonic. [Figure 8] FIG. 8 is a schematic diagram of a method for detecting snoring sounds that examines whether the received signal is dominated by low frequency components. [Figure 9] FIG. 9 is a schematic diagram of a method for detecting snoring sounds using autocorrelation. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Embodiments will now be described with reference to the accompanying drawings, in which like reference numbers designate corresponding or identical elements throughout the various drawings.

[0011] The sleep disordered breathing estimation device according to the embodiment of the present invention calculates an index for estimating the prevalence of sleep disordered breathing. The device includes one or more microphones 104, one or more receiving circuits 106, a sound intensity conversion filter 108, an intensity periodicity measurement filter 110, an intensity periodicity filter 112, a snore sound detection filter 114, and a system controller 118 for controlling the receiving circuit 106, the sound intensity conversion filter 108, the intensity periodicity measurement filter 110, the intensity periodicity evaluation filter 112, and the snore sound detection filter 114.

[0012] A sleep disordered breathing estimation device according to an embodiment of the present invention calculates an index for estimating the prevalence of sleep disordered breathing. The device includes one or more microphones 104, one or more receiving circuits 106, a sound intensity conversion filter 108, an intensity periodicity measurement filter 110, an intensity periodicity evaluation filter 112, a snore sound detection filter 114, a sleep disordered breathing estimation filter 116, and a system controller 118 for controlling the receiving circuit 106, the sound intensity conversion filter 108, the intensity periodicity measurement filter 110, the intensity periodicity evaluation filter 112, the snore sound detection filter 114, and the sleep disordered breathing estimation filter 116.

[0013] The system controller 118 may be a computer including a central processing unit (CPU) and memory such as read-only memory (ROM) and random access memory (RAM). The controller's CPU may be a single-core processor (containing a single processing unit) or a multi-core processor. The computer may be a mobile device such as a personal digital assistant (PDA), a laptop computer, a field programmable gate array, or a cellular telephone.

[0014] FIG. 1 shows a schematic diagram for sleep disordered breathing estimation according to an embodiment of the present invention. One or more microphones 104 with one or more receiving circuits 106 convert a plurality of sounds generated by a subject 100, including a snoring sound 102, into a plurality of receiving signals. A microphone with a receiving circuit in a cell phone is also applicable for acquiring a plurality of receiving signals. A sound intensity conversion filter 108 converts the plurality of receiving signals into a plurality of sound intensity signals. An intensity periodicity measurement filter 110 measures the periodicity of the sound intensity using one or more sound intensity signals. An intensity periodicity evaluation filter 112 evaluates the validity of the periodicity of the sound intensity with respect to the respiratory rate. A snore sound detection filter 114 detects snore sounds using the sound intensity acquired by the intensity periodicity evaluation filter 112 and the validity of the periodicity. A sleep disordered breathing estimation filter 116 calculates one or more indexes to estimate the prevalence of sleep disordered breathing. The indices include frequency of loud snoring per hour, total duration of loud snoring per hour, total duration of sleep per night, and number of awakenings per night.

[0015] A snoring sound detection method according to an embodiment of the present invention includes obtaining a plurality of sounds produced by a subject, converting the sounds produced by the subject into a plurality of received signals, converting the received signals into a plurality of sound intensity signals, measuring a periodicity of the sound intensity signals using one or more of the sound intensity signals, evaluating a validity of the periodicity of the sound intensity signals with respect to a respiratory rate, and detecting snoring sounds using the sound intensity and the validity of the periodicity of the sound intensity signals with respect to a respiratory rate.

[0016] FIG. 2 shows a schematic diagram of a sleep disordered breathing estimation method according to an embodiment of the present invention. In process 200, the method acquires and stores sounds generated by a subject. In process 201, the method converts the sounds into a received signal. In process 202, the method stores the received signal. The method can store the filtered received signal and other information including the time of acquisition and subject information. When the method stores the filtered received signal, the method stores the filtered received signal after applying a filter to the received signal. For example, when the method stores a sound intensity signal, the storage process is located after process 203. In process 203, the method converts a plurality of received signals into a plurality of sound intensity signals. In process 204, the method measures the periodicity of the sound intensity using one or more sound intensity signals. In process 206, the method evaluates the validity of the periodicity of the sound intensity with respect to the respiratory rate. In process 208, the method detects snoring sounds using the sound intensity acquired by process 206 of evaluating the validity of the periodicity of the sound intensity and the validity of the periodicity. In process 210, the method calculates one or more indices to estimate the prevalence of sleep disordered breathing.

[0017] In process 204, a Fourier transform can be used to measure the periodicity of the sound intensity signal. Figure 3 shows a schematic diagram of a method for sleep disordered breathing estimation according to an embodiment of the present invention. In process 200, the method acquires and stores sounds generated by a subject. In process 201, the method converts the sounds into received signals. In process 202, the method stores the received signals. In process 300, the method extracts the received signals using a window function with a time window duration of 10 seconds or more. In process 302, the method determines the intensity of the extracted received signals. In process 203, the method converts the multiple received signals into multiple sound intensity signals. In process 304, the method applies a Fourier transform to the extracted received signals. In process 306, the method searches for local maxima in a low frequency band. In process 308, the method determines whether the frequency of the maximum value of the local maxima is close to the breathing frequency or twice the breathing frequency. In process 310, the method determines whether the maximum value of the local maxima is sufficiently large compared to the intensity of the surrounding frequencies. The criteria for being sufficiently large include that the maximum value of the maxima is greater than three times the standard deviation of the maxima. Instead of the three times threshold, thresholds of two or two and a half times the standard deviation can also be used. In process 210, the method calculates one or more indices to estimate the prevalence of sleep-disordered breathing. The period of the intensity change of the received signal caused by the snoring sound is considered to be closely related to the breathing frequency. Since the breathing frequency of an adult is 0.2 to 0.3 Hz, the sampling interval in the frequency domain may be 0.1 Hz or less. Therefore, the duration of the time window for the Fourier transform may be 10 seconds or more. Generally, one of the window functions including a rectangular window, a B-spline window, a Hann window, a Hamming window, and a Tukey window is applied before the Fourier transform.

[0018] It may be necessary to increase the number of points in the frequency domain in order to search for local maxima in the frequency domain in process 306. Adding zeros to the beginning and / or end of each received signal, called zero padding, may be used before process 304 to add more frequency points to the sound data in the frequency domain. Also, interpolation to the frequency domain sound intensity data after process 304 adds more frequency points to the frequency domain sound intensity data after the Fourier transform, i.e., the frequency domain sound intensity data.

[0019] In process 304, Fourier related transforms including wavelet transform, Laplace transform, fast Fourier transform, discrete Fourier transform, short-time Fourier transform, Z transform and singular value decomposition can be applied as an alternative to the Fourier transform.

[0020] In process 203, the signal envelope or its power can be used to convert the multiple received signals into multiple sound intensity signals. The signal envelope can be calculated using one of the envelope estimation algorithms including rectification followed by low pass filtering, analysis signal magnitude, peak envelope and root mean square envelope for the signal envelope calculation. One of the metrics using rectification followed by low pass filtering is defined by the following equation:

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[0021] In process 203, the absolute value of the received signals or a moving average of their power can be used to convert the multiple received signals into multiple sound intensity signals.

[0022] In process 206, the periodicity of the sound intensity is plausible as snoring sounds when the periodicity is close to the breathing frequency or twice the breathing frequency, as shown in Figure 3. A periodicity close to the breathing frequency indicates that snoring occurs only during inspiration. A periodicity close to twice the breathing frequency indicates that snoring occurs during both inspiration and expiration.

[0023] Since breathing rate varies greatly from subject to subject, and is particularly dependent on the subject's age, in process 206 the breathing frequency may be adjusted according to subject information.

[0024] As shown in FIG. 3, in process 206, the method may determine that the periodicity of the sound intensity is reasonable when the sound intensity signal in the frequency domain after applying the Fourier transform has one or more maxima within a frequency band of 0.1 to 5 Hz, and the frequency of the maximum of the maxima within the frequency band of 0.1 to 5 Hz is in the range of 0.15 to 2 Hz, because the breathing frequency and twice the breathing frequency are considered to be in the range of 0.15 to 2 Hz.

[0025] In process 210, since the apnea-hypopnea index is the number of apneas and hypopneas per hour of sleep, and the average apnea-hypopnea duration is in the range of 20 to 40 seconds, the method can estimate the apnea-hypopnea index using the sum of snore durations per hour normalized by a specific duration in the range of 20 to 40 seconds. Thus, the method sets the snore duration unit in the range of 20 to 40 seconds, the method calculates the sum of snore durations per hour determined to be valid in the snore sound detection process, and the method can estimate the apnea-hypopnea index using the sum of snore durations per hour normalized by the snore duration unit.

[0026] In process 300, the duration of each extracted received signal may range from 20 to 40 seconds, and the method may estimate the apnea-hypopnea index using the number of received signals per hour that are determined to be valid in the snore sound detection process.

[0027] In process 302, the method can exclude received signals of low intensity from the analysis. The method can exclude received signals for a particular time after sleep and / or a particular time before waking from the analysis. The method can exclude received signals while the subject is awake, including speech. The method can exclude received signals while the subject is assumed to be in REM sleep.

[0028] When calculating total snore duration, the method may underestimate snore duration when snoring continues for a period of time because simple snoring lasts for a long period of time and simple snoring does not imply hypopnea or apnea.

[0029] In process 310, the method can evaluate the validity of the periodicity of the sound intensity when the maximum of the local maxima in the frequency band from 0.15 to 2 Hz is sufficiently large compared to the intensity of the surrounding frequencies, since the dominance of snoring sounds may cause large intensities at frequencies close to the breathing frequency and twice the breathing frequency. The order of processes 308 and 310 may be reversed.

[0030] Since the frequency of a maximum due to snoring sounds is likely to be close to the frequency of a maximum at a nearby time, when the frequency of a maximum is close to the frequency of one or more nearby maxima of the received signal, the threshold for the maximum of the maximums in the 0.15 to 2 Hz frequency band may be reduced, for example from a threshold that the maximum of the maximums is greater than 3 times the standard deviation of the maximums to a threshold that the maximum of the maximums is greater than 2.5 times the standard deviation of the maximums.

[0031] Because patients with severe sleep apnea have unstable breathing frequencies, the method may increase the estimate of the apnea-hypopnea index when the frequency of the maximum of the local maxima in the 0.15 to 2 Hz frequency band varies significantly during sleep.

[0032] A snore sound detection device according to an embodiment of the present invention calculates an index to detect snoring. The device includes one or more microphones 104, one or more receiving circuits 106, a fundamental frequency estimation filter 400, a high-pass filter 402, an envelope calculation filter 404, an envelope periodicity estimation filter 406, an envelope periodicity evaluation filter 408, a snore detection filter 410, and a system controller 118 for controlling the receiving circuits 106, the fundamental frequency estimation filter 400, the high-pass filter application filter 402, the envelope calculation filter 404, the envelope periodicity estimation filter 406, the envelope periodicity evaluation filter 408, and the snore detection filter 410.

[0033] FIG. 4 shows a schematic diagram of an apparatus for snoring sound detection based on sound analysis. One or more microphones 104 with one or more receiving circuits 106 convert a plurality of sounds produced by a subject into a plurality of receiving signals. A fundamental frequency estimation filter 400 estimates a fundamental frequency of the receiving signal. A high-pass filter 402 applies a high-pass filter to the receiving signal. An envelope calculation filter 404 calculates an envelope of the high-pass filtered receiving signal. An envelope periodicity estimation filter 406 estimates the periodicity of the envelope of the high-pass filtered receiving signal. An envelope periodicity evaluation filter 408 evaluates the periodicity of the envelope of the high-pass filtered receiving signal with respect to the fundamental frequency of the receiving signal. A snore detection filter 410 calculates one or more indices to detect snoring.

[0034] FIG. 5 shows a schematic diagram of a method for snoring sound detection based on sound analysis. In process 200, the method acquires and stores sounds produced by a subject. In process 201, the method converts the sounds into a received signal. In process 202, the method stores the received signal. The method can store the filtered received signal and other information including the time of acquisition and subject information. When the method stores the filtered received signal, the method stores the filtered received signal after applying a filter to the received signal. For example, when the method stores a high-pass filtered received signal, the storage process is placed after process 502. In process 500, the method estimates the fundamental frequency of the received signal. In process 502, the method applies a high-pass filter to the received signal. In process 504, the method calculates the envelope of the high-pass filtered received signal. In process 506, the method estimates the periodicity of the envelope of the high-pass filtered received signal. In process 508, since the periodicity of the envelope of the snoring sound after removing the fundamental frequency of the snoring sound using a high-pass filter is considered to be close to the periodicity of the fundamental frequency, the method evaluates the periodicity of the envelope of the high-pass filtered received signal with respect to the fundamental frequency of the received signal. In process 510, the method calculates one or more indexes for detecting snoring.

[0035] In the process 500, a Fourier transform can be used to estimate the fundamental frequency of the received signal. The method can apply a Fourier transform to the received signal. The method can calculate the strength of the received signal in the frequency domain. The method can search for the fundamental frequency of the received signal. Since the snoring sound is one of the time-varying signals, the fundamental frequency of the snoring sound is considered to be calculated using a short time window of 1 second or less. Therefore, the duration of the time window of the Fourier transform is set to 1 second or less. Before the Fourier transform, one of window functions including a rectangular window, a B-spline window, a Hann window, a Hamming window, and a Tukey window may be applied.

[0036] In process 500, the addition of non-informative data to the beginning and / or end of each received signal, called zero padding, adds more frequency points to the sound data in the frequency domain. Interpolation to the strength of the received signal in the frequency domain after application of the Fourier transform also adds more frequency points to the strength of the received signal in the frequency domain.

[0037] In the process 500, Fourier-related transforms including wavelet transform, Laplace transform, fast Fourier transform, discrete Fourier transform, short-time Fourier transform, Z transform and singular value decomposition can be applied as alternatives to the Fourier transform.

[0038] In process 502, the method can apply a high-pass filter having a cutoff frequency to the received signal, the cutoff frequency of the high-pass filter being higher than the fundamental frequency of the received signal to remove the fundamental frequency of the snoring sound.

[0039] In process 600, the method can store the index, where the method can use the stored index for calculation of one or more indexes to detect snoring in process 510, as shown in FIG.

[0040] In the process 500, the method can detect local maxima of the strength of the received signal in the frequency domain, and the method can determine the local maxima with the lowest frequency as the fundamental frequency.

[0041] In process 500, the method can determine one of the maxima of the strength of the received signal in the frequency domain as the fundamental frequency, with factors used in the determination including the amplitude of each maximum, the strength of each maximum, the distance of each maximum to other maxima in the frequency domain, and the prominence of each maximum.

[0042] In process 500, the method can estimate the fundamental frequency of each received signal by calculating the periodicity of the signal amplitude in the time domain.

[0043] In process 500, it is assumed that the fundamental frequency of the snoring sound is in the range of 10 to 300 Hz, so the method can determine the fundamental frequency of the received signal using several conditions, including that the fundamental frequency is in the range of 10 to 300 Hz.

[0044] In process 504, the method may calculate the envelope of the high-pass filtered received signal, under the condition that the envelope frequency is within the range of 10 to 300 Hz.

[0045] In process 506, the method may apply a Fourier transform to the envelope of the high-pass filtered received signal, and the method may search for a maximum of the local maximum of the power of each received signal in the frequency domain after the Fourier transform to estimate the periodicity of the envelope of the high-pass filtered received signal.

[0046] The method can use the condition that the snoring sound has the second harmonic. Figure 7 shows a schematic diagram of the snore sound detection method using the condition that the snoring sound has the second harmonic. In process 200, the method obtains and stores the sound generated by the subject. In process 201, the method converts the sound into a received signal. In process 202, the method stores the received signal. In process 500, the method estimates the fundamental frequency of the received signal. In process 700, the method searches for the second harmonic of the received signal because the snoring sound is considered to have the second harmonic. In process 502, the method applies a high-pass filter to the received signal. In process 504, the method calculates the envelope of the high-pass filtered received signal. In process 506, the method estimates the periodicity of the envelope of the high-pass filtered received signal. In process 508, since the periodicity of the envelope of the snoring sound after removing the fundamental frequency of the snoring sound using a high-pass filter is considered to be close to the periodicity of the fundamental frequency, the method evaluates the periodicity of the envelope of the high-pass filtered received signal with respect to the fundamental frequency of the received signal. In process 510, to detect snoring, the method calculates one or more indexes.

[0047] In process 510, the method can search for a maximum value among the maximum values ​​of the intensity of each received signal strength in a frequency range from the fundamental frequency to the second harmonic of the received signal, where when both the intensity of the fundamental frequency and the intensity of the second harmonic of the received signal are higher than the maximum value among the maximum values ​​of the intensity of each received signal in a frequency range from the fundamental frequency to the second harmonic of the received signal, the method determines that the received signal may include snoring sounds.

[0048] In process 510, the method can search for a maximum value of the strength of each received signal within a frequency range less than the fundamental frequency of the received signal, where when the strength of the fundamental frequency of the received signal is greater than the maximum value of the strength of each received signal within the frequency range less than the fundamental frequency of the received signal, the method determines that the received signal may contain snoring sounds.

[0049] In process 806, since the low frequency components of the received signal are considered to be dominant, the method can investigate whether the low frequency components of the received signal are dominant. When the low frequency components are not dominant, the method determines that the received signal does not contain snoring sounds. FIG. 8 shows a schematic diagram of a snore sound detection method for investigating whether the low frequency components of the received signal are dominant. In process 200, the method obtains and stores a sound produced by a subject. In process 201, the method converts the sound into a received signal. In process 202, the method stores the received signal. In process 800, the method applies a Fourier transform to the received signal. In process 802, the method calculates the strength of the received signal in the frequency domain. In process 804, the method searches for the fundamental frequency of the received signal. In process 806, the method investigates whether the low frequency components of the received signal are dominant. In process 502, the method applies a high pass filter to the received signal. In process 504, the method calculates the envelope of the high-pass filtered received signal. In process 506, the method estimates the periodicity of the envelope of the high-pass filtered received signal. In process 508, the method evaluates the periodicity of the envelope of the high-pass filtered received signal with respect to the fundamental frequency of the received signal, since the periodicity of the envelope of the snoring sound after removing the fundamental frequency of the snoring sound using a high-pass filter is considered to be close to the periodicity of the fundamental frequency. In process 510, to detect snoring, the method calculates one or more indexes.

[0050] In process 510, when the strength of the fundamental frequency of the received signal is dominant in the frequency domain, the method may determine that the received signal may include snoring sounds.

[0051] In process 510, when the intensity of the fundamental frequency of the received signal accounts for 10% or more of the sum of the intensities of the received signals, the method may determine that the received signal may include snoring sounds.

[0052] In process 510, when the strength and / or prominence of the fundamental frequency is greater than the strength and prominence of other frequencies, the method may determine that the received signal may include snoring sounds.

[0053] The method may use autocorrelation. FIG. 9 shows a schematic diagram of a snoring sound detection method using autocorrelation. In process 200, the method acquires and stores sounds produced by a subject. In process 201, the method converts the sounds into a received signal. In process 202, the method stores the received signal. In process 900, the method applies a high-pass filter with a cutoff frequency of 20 Hz or less to the received signal. In process 902, the method calculates the autocorrelation coefficient of the received signal in the time domain using a sliding window with multiple window widths, and the time lag range of the autocorrelation coefficient calculation is included in the range of 1 / 2 the window width to twice the window width. In process 904, the method determines that the received signal contains snoring sounds when the autocorrelation coefficient of the received signal is maximized when using a window width of 20 milliseconds or more.

[0054] The method may store a plurality of received signals and / or filtered received signals. When the method stores filtered received signals, the method stores the filtered received signals after application of the filter to the received signals.

[0055] First Exemplary Embodiment FIG. 1 shows a schematic diagram of an apparatus for sleep disordered breathing estimation according to an embodiment of the present invention. One or more microphones 104 with one or more receiving circuits 106 convert a plurality of sounds generated by a subject 100, including a snoring sound 102, into a plurality of receiving signals. A microphone with a receiving circuit in a cell phone is also applicable for acquiring a plurality of receiving signals. A sound intensity conversion filter 108 converts the plurality of receiving signals into a plurality of sound intensity signals. An intensity periodicity measurement filter 110 measures the periodicity of the sound intensity using one or more sound intensity signals. An intensity periodicity evaluation filter 112 evaluates the validity of the periodicity of the sound intensity with respect to the respiratory rate. A snore sound detection filter 114 detects snore sounds using the sound intensity acquired by the intensity periodicity evaluation filter 112 and the validity of the periodicity. A sleep disordered breathing estimation filter 116 calculates one or more indexes to estimate the prevalence of sleep disordered breathing.

[0056] Second Exemplary Embodiment FIG. 2 shows a schematic diagram of a method for sleep disordered breathing estimation according to an embodiment of the present invention. In process 200, the method acquires and stores sounds generated by a subject. In process 201, the method converts the sounds into received signals. In process 202, the method stores the received signals. In process 203, the method converts a plurality of received signals into a plurality of sound intensity signals. In process 204, the one or more sound intensity signals are used to measure the periodicity of the sound intensity. In process 206, the method evaluates the validity of the periodicity of the sound intensity with respect to the respiratory rate. In process 208, the method detects snoring sounds using the sound intensity acquired by process 206 evaluating the validity of the periodicity of the sound intensity and the validity of the periodicity. In process 210, the method calculates one or more indices to estimate the prevalence of sleep disordered breathing.

[0057] Third Exemplary Embodiment FIG. 3 shows a schematic diagram of a method for sleep disordered breathing estimation according to an embodiment of the present invention. In process 200, the method acquires and stores sounds generated by a subject. In process 201, the method converts the sounds into received signals. In process 202, the method stores the received signals. In process 300, the method extracts the received signals using a window function with a time window duration of 10 seconds or more. In process 302, the intensity of the extracted received signals is determined. In process 203, the method converts the multiple received signals into multiple sound intensity signals by calculating the envelope of each received signal. In process 304, the method applies a Fourier transform to the extracted received signals. In process 306, the method searches for a local maximum in a low frequency band of 0.1 to 5 Hz. In process 308, the method determines whether the frequency of the maximum of the local maximum is in a frequency band of 0.2 to 2 Hz. In process 310, the method determines whether the maximum of the local maximum is sufficiently large compared to the intensity of the surrounding frequencies. Process 210 calculates one or more indices to estimate the prevalence of sleep disordered breathing.

[0058] Fourth Exemplary Embodiment FIG. 4 shows a schematic diagram of an apparatus for snoring sound detection based on sound analysis. One or more microphones 104 with one or more receiving circuits 106 convert a plurality of sounds produced by a patient into a plurality of receiving signals. A fundamental frequency estimation filter 400 estimates a fundamental frequency of the receiving signal. A high-pass filter 402 applies a high-pass filter to the receiving signal. An envelope calculation filter 404 calculates an envelope of the high-pass filtered receiving signal. An envelope periodicity estimation filter 406 estimates the periodicity of the envelope of the high-pass filtered receiving signal. An envelope periodicity evaluation filter 408 evaluates the periodicity of the envelope of the high-pass filtered receiving signal with respect to the fundamental frequency of the receiving signal. A snore detection filter 410 calculates one or more indices to detect snoring.

[0059] Fifth Exemplary Embodiment FIG. 5 shows a schematic diagram of a method for snore sound detection based on sound analysis. In process 200, the method acquires and stores a sound generated by a subject. In process 201, the method converts the sound into a received signal. In process 202, the method stores the received signal. In process 500, the method estimates a fundamental frequency of the received signal. In process 502, the method applies a high-pass filter to the received signal. In process 504, the method calculates an envelope of the high-pass filtered received signal. In process 506, the method estimates a periodicity of the envelope of the high-pass filtered received signal. In process 508, the method evaluates the periodicity of the envelope of the high-pass filtered received signal with respect to the fundamental frequency of the received signal, since the periodicity of the envelope of the snore sound after removing the fundamental frequency of the snore sound using a high-pass filter is considered to be close to the periodicity of the fundamental frequency. In process 510, the method calculates one or more indexes to detect snoring.

[0060] Sixth Exemplary Embodiment FIG. 6 shows a schematic diagram of a method for snore sound detection using a process of storing an index to calculate an index. In process 200, the method obtains and stores a sound produced by a subject. In process 201, the method converts the sound into a received signal. In process 202, the method stores the received signal. In process 500, the method estimates a fundamental frequency of the received signal. In process 502, the method applies a high-pass filter to the received signal. In process 504, the method calculates an envelope of the high-pass filtered received signal. In process 506, the method estimates a periodicity of the envelope of the high-pass filtered received signal. In process 508, the method evaluates the periodicity of the envelope of the high-pass filtered received signal with respect to the fundamental frequency of the received signal, since the periodicity of the envelope of the snore sound after removing the fundamental frequency of the snore sound using a high-pass filter is considered to be close to the periodicity of the fundamental frequency. In process 510, the method calculates one or more indexes to detect snoring. In process 600, the method may store the indices, and the method may use the stored indices for calculation of one or more indices to detect snoring in process 510.

[0061] Seventh Exemplary Embodiment FIG. 7 shows a schematic diagram of a snore sound detection method using the condition that the snore sound has a second harmonic. In process 200, the method acquires and stores a sound produced by a subject. In process 201, the method converts the sound into a received signal. In process 202, the method stores the received signal. In process 500, the method estimates the fundamental frequency of the received signal. In process 700, the method searches for the second harmonic of the received signal because the snore sound is considered to have a second harmonic. When the second harmonic does not exist, the method determines that the received signal does not contain a snore sound. In process 502, the method applies a high-pass filter to the received signal. In process 504, the method calculates the envelope of the high-pass filtered received signal. In process 506, the method estimates the periodicity of the envelope of the high-pass filtered received signal. In process 508, the method evaluates the periodicity of the envelope of the high-pass filtered received signal with respect to the fundamental frequency of the received signal, since the periodicity of the envelope of the snore sound after removing the fundamental frequency of the snore sound using a high-pass filter is considered to be close to the periodicity of the fundamental frequency. In process 510, the method calculates one or more indexes to detect snoring.

[0062] Eighth Exemplary Embodiment FIG. 8 shows a schematic diagram of a snoring sound detection method that checks whether the low frequency components of the received signal are dominant. In process 200, the method acquires and stores a sound produced by a subject. In process 201, the method converts the sound into a received signal. In process 202, the method stores the received signal. In process 800, the method applies a Fourier transform to the received signal. In process 802, the method calculates the strength of the received signal in the frequency domain. In process 804, the method searches for the fundamental frequency of the received signal. In process 806, the method investigates whether the low frequency components of the received signal are dominant. In process 502, the method applies a high pass filter to the received signal. In process 504, the method calculates the envelope of the high pass filtered received signal. In process 506, the method estimates the periodicity of the envelope of the high pass filtered received signal. In process 508, since the periodicity of the envelope of the snoring sound after removing the fundamental frequency of the snoring sound using a high-pass filter is considered to be close to the periodicity of the fundamental frequency, the method evaluates the periodicity of the envelope of the high-pass filtered received signal with respect to the fundamental frequency of the received signal. In process 510, the method calculates one or more indexes to detect snoring.

[0063] The present invention has the following aspects. 1. A snoring sound detection device comprising: one or more microphones for receiving a plurality of sounds produced by a subject; and a controller having circuitry configured to convert the plurality of sounds produced by the subject into a plurality of received signals, convert the plurality of received signals into a plurality of sound intensity signals, measure a periodicity of the sound intensity signals using one or more sound intensity signals, evaluate a validity of the periodicity of the sound intensity signals with respect to a respiratory rate, and detect snoring sounds using the sound intensity and the validity of the periodicity of the sound intensity signals with respect to a respiratory rate. 2. A sleep breathing disorder estimation device comprising: one or more microphones for receiving a plurality of sounds generated by a subject; and a controller having circuitry configured to convert the plurality of sounds generated by the subject into a plurality of received signals, convert the plurality of received signals into a plurality of sound intensity signals, measure a periodicity of the sound intensity signal using the one or more sound intensity signals, evaluate a validity of the periodicity of the sound intensity signal with respect to a respiratory rate, detect snoring sounds using the sound intensity and the validity of the periodicity of the sound intensity signal with respect to a respiratory rate, and calculate one or more indices to estimate a prevalence of sleep breathing disorder. 3. A method for detecting snoring sounds, comprising: acquiring a plurality of sounds produced by a subject; converting the plurality of sounds produced by the subject into a plurality of received signals; converting the plurality of received signals into a plurality of sound intensity signals; measuring a periodicity of the sound intensity signals using one or more sound intensity signals; evaluating a validity of the periodicity of the sound intensity signals with respect to a respiratory rate; and detecting snoring sounds using the sound intensity and the validity of the periodicity of the sound intensity signals with respect to a respiratory rate. 4. A method for estimating sleep disordered breathing, comprising: acquiring a plurality of sounds produced by a subject; converting the plurality of sounds produced by the subject into a plurality of received signals; converting the plurality of received signals into sound intensity signals; measuring a periodicity of the sound intensity signals using one or more sound intensity signals; evaluating a validity of the periodicity of the sound intensity signals with respect to a respiratory rate; detecting snoring sounds using the sound intensity and the validity of the periodicity of the sound intensity signals with respect to a respiratory rate; and calculating one or more indices to estimate a prevalence of sleep disordered breathing. 5. The method for detecting snoring sounds or estimating sleep-related breathing disorders according to claims 3 and 4, wherein the method includes measuring the periodicity of the sound intensity signal by a Fourier transform, the duration of the time window of the Fourier transform being 10 seconds or more, and one of the window functions including a rectangular window, a B-spline window, a Hann window, a Hamming window, and a Tukey window is used as the window function of the time window for the Fourier transform. 6. The method for detecting snoring sounds or estimating sleep disordered breathing as described in 5, further comprising adding non-informative data to the beginning and / or end of each received signal. 7. The method for detecting snoring sounds or estimating sleep disordered breathing according to claim 5, further comprising applying interpolation to the sound intensity data in the frequency domain after the Fourier transform. 8. The method for detecting snoring sounds or estimating sleep disordered breathing described in 5, wherein the method includes measuring the periodicity of the sound intensity signal by one of Fourier related transforms including wavelet transform, Laplace transform, fast Fourier transform, discrete Fourier transform, short-time Fourier transform, Z transform, and singular value decomposition instead of a Fourier transform. 9. A method for detecting snoring sounds or estimating sleep breathing disorders as described in 3 and 4, comprising converting a plurality of received signals into envelopes of the plurality of received signals or powers of the envelopes of the plurality of received signals as a plurality of sound intensity signals. 10. The snoring sound detection or sleep disordered breathing estimation method of claim 9, wherein the method includes calculating an envelope of a plurality of received signals by one of an envelope estimation algorithm including rectification followed by low pass filtering, a magnitude of the analysis signal, a peak envelope, and a root mean square envelope. 11. The method for detecting snoring sounds or estimating sleep breathing disorders described in 3 and 4, further comprising converting the plurality of received signals as the plurality of sound intensity signals into a plurality of moving averages of absolute values ​​of the plurality of received signals, or into powers of a plurality of moving averages of absolute values ​​of the plurality of received signals. 12. A method for detecting snoring sounds or estimating sleep breathing disorders as described in 3 and 5, comprising evaluating the validity of the periodicity of the sound intensity signal based on a criterion that the received signal is valid as a snoring sound when the periodicity of the sound intensity of the received signal is close to the breathing frequency or twice the breathing frequency. 13. The method for detecting snoring sounds or estimating sleep breathing disorders according to claim 12, further comprising adjusting possible breathing frequency according to subject information. 14. The method for detecting snoring sounds or estimating sleep breathing disorders described in 5, comprising evaluating the validity of the periodicity of the sound intensity signal based on a criterion that the received signal is valid as a snoring sound when it satisfies both of the following two conditions: one is that the sound intensity signal in the frequency domain after application of the Fourier transform has one or more maximum values ​​within a frequency band of 0.1 to 5 Hz, and the other is that the frequency of the maximum value among the maximum values ​​within the frequency band of 0.1 to 5 Hz is in the range of 0.15 to 2 Hz. 15. A method for estimating sleep breathing disorders as described in 4, wherein the method includes estimating an apnea-hypopnea index as an index for estimating the prevalence of the sleep breathing disorder, the apnea-hypopnea index being estimated using the sum of snoring durations per hour normalized by a snore duration unit, the snore duration unit being in the range of 20 to 40 seconds, and the snore duration being calculated by the duration of a received signal detected as a snore sound. 16. A method for estimating sleep breathing disorders as described in 4, wherein the method includes estimating an apnea-hypopnea index as an index for estimating the prevalence of the sleep breathing disorder, wherein the validity of the periodicity of the sound intensity signal with respect to the respiratory rate is evaluated, the duration of the sound intensity signal is in the range of 20 to 40 seconds, and the apnea-hypopnea index is estimated using the number of sound intensity signals per hour that are determined to be valid. 17. A method for detecting snoring sounds or estimating sleep-disordered breathing as described in 3 and 4, in which low-intensity received signals are excluded from the analysis. 18. A method for detecting snoring sounds or estimating sleep breathing disorders as described in 3 and 4, further comprising excluding from the analysis received signals converted from sounds acquired during a certain period of time after sleep and / or a certain period of time before waking up. 19. The method for detecting snoring sounds or estimating sleep disordered breathing described in 3 and 4, further comprising excluding received signals converted from sounds acquired while the subject was awake, including speech. 20. A method for detecting snoring sounds or estimating sleep disordered breathing as described in 3 and 4, further comprising excluding received signals converted from sounds acquired while the subject is estimated to be in REM sleep. 21. A method for estimating sleep breathing disorders as described in 15, wherein the calculation of the sum of snoring durations includes reducing the sum of snoring durations when snoring continues for a certain period of time. 22. A method for detecting snoring sounds or estimating sleep breathing disorders as described in 14, further comprising evaluating the validity of the periodicity of the sound intensity signal based on the criterion that the received signal is valid as a snoring sound when the maximum value among the local maxima in a frequency band of 0.15 to 2 Hz is sufficiently large compared with the intensity of surrounding frequencies. 23. A method for detecting snoring sounds or estimating sleep breathing disorders as described in 22, further comprising evaluating the validity of the periodicity of the sound intensity signal based on the criterion that the received signal is valid as a snoring sound when the maximum value among the local maxima in a frequency band of 0.15 to 2 Hz is large compared to the intensity of surrounding frequencies and the frequency of the maximum value is close to one or more frequencies of the maximum values ​​of nearby received signals. 24. A method for estimating sleep-related breathing disorders as described in claim 14, wherein the method increases the estimated apnea / hypopnea index when the frequency of the maxima in the frequency band from 0.15 to 2 Hz varies significantly during sleep. 25. A snoring sound detection device comprising: one or more microphones for receiving a plurality of sounds produced by a subject; and a controller comprising circuitry configured to convert the plurality of sounds produced by the subject into a plurality of received signals, estimate a fundamental frequency of each of the received signals, apply a high-pass filter to the received signals, calculate an envelope of the high-pass filtered received signals, evaluate a periodicity of the envelope of the high-pass filtered received signals with respect to the fundamental frequency of the received signals, and calculate one or more indices for detecting snoring. 26. A method for detecting snoring sounds, comprising: acquiring a plurality of sounds produced by a subject; converting the plurality of sounds produced by the subject into a plurality of received signals; storing the plurality of received signals and / or filtered received signals; estimating a fundamental frequency of the received signals; applying a high-pass filter to the received signals; calculating an envelope of the high-pass filtered received signals; estimating a periodicity of the envelope of the high-pass filtered received signals; evaluating the periodicity of the envelope of the high-pass filtered received signals with respect to the fundamental frequency of the received signals; and calculating one or more indices to detect snoring. 27. The snoring sound detection method according to claim 26, wherein the method includes applying a Fourier transform to the received signal, calculating the strength of the received signal in the frequency domain, and searching for a fundamental frequency of the received signal, wherein the time window for the Fourier transform has a duration of 1 second or less, and one of the following window functions is applied to the received signal before the Fourier transform: a rectangular window, a B-spline window, a Hann window, a Hamming window, and a Tukey window. 28. The method of claim 27, further comprising adding non-informative data to the beginning and / or end of each received signal. 29. The method of claim 27, further comprising applying interpolation to the strength of the received signal in the frequency domain after applying a Fourier transform. 30. The method for detecting snoring sounds as described in 27, wherein the method includes using one of the Fourier related transforms including a wavelet transform, a Laplace transform, a fast Fourier transform, a discrete Fourier transform, a short-time Fourier transform, a Z transform, and a singular value decomposition instead of the Fourier transform. 31. A method for detecting snoring sounds as described in 26, wherein the method includes applying a high-pass filter having a cutoff frequency to the received signal, the cutoff frequency of the high-pass filter being higher than a fundamental frequency of the received signal. 32. The snoring sound detection method of claim 26, further comprising storing the index and using the stored index for calculation of one or more indices to detect snoring. 33. A method for detecting snoring sounds as described in 26, wherein the method includes detecting local maxima of the strength of the received signal in the frequency domain and determining the local maxima having the lowest frequency as the fundamental frequency. 34. A method for detecting snoring sounds as described in claim 33, comprising determining one of the maxima of the intensity of the received signal in the frequency domain as the fundamental frequency using criteria that use the amplitude of each maximum, the intensity of each maximum, the distance of each maximum to other maxima in the frequency domain, and the prominence of each maximum. 35. A method for detecting snoring sounds as described in claim 26, wherein the method includes estimating the fundamental frequency of each received signal by calculating the periodicity of the signal amplitude in the time domain. 36. A method for detecting snoring sounds as described in 26, wherein the method includes determining a fundamental frequency of the received signal using a number of conditions including that the fundamental frequency be within the range of 10 to 300 Hz. 37. The method of claim 26, further comprising calculating an envelope of the high-pass filtered received signal, with the envelope frequency being within the range of 10 to 300 Hz. 38. A method for detecting snoring sounds as described in 26, wherein the method includes applying a Fourier transform to the envelope of the high-pass filtered received signals, and searching for a maximum of local maxima of the intensity of each received signal in the Fourier transformed frequency domain to estimate the periodicity of the envelope of the high-pass filtered received signals. 39. A method for detecting snoring sounds, comprising: acquiring a plurality of sounds produced by a subject; converting the plurality of sounds produced by the subject into received signals; storing the plurality of received signals and / or filtered received signals; estimating a fundamental frequency of the received signals; searching for a second harmonic of the received signals; applying a high-pass filter to the received signals; calculating an envelope of the high-pass filtered received signals; estimating a periodicity of the envelope of the high-pass filtered received signals; evaluating the periodicity of the envelope of the high-pass filtered received signals with respect to the fundamental frequency of the received signals; and calculating one or more indices to detect snoring. 40. The method for detecting snoring sounds as described in 39, wherein the method includes searching for a maximum value of the local maximum values ​​of the strength of each received signal in a frequency range from the fundamental frequency to the second harmonic of the received signal, and determining that the received signal may contain a snoring sound when both the strength of the fundamental frequency and the strength of the second harmonic of the received signal are greater than the maximum value of the local maximum values ​​of the strength of each received signal in the frequency range from the fundamental frequency to the second harmonic of the received signal. 41. A method for detecting snoring sounds as described in 39, the method including searching for a maximum value of a strength of each received signal in a frequency range less than the fundamental frequency of the received signal, and determining that the received signal may include a snoring sound when the strength of the fundamental frequency of the received signal is greater than the maximum value of a strength of each received signal in a frequency range less than the fundamental frequency of the received signal. 42. A method for detecting snoring sounds, comprising: acquiring a plurality of sounds produced by a subject; converting the plurality of sounds produced by the subject into received signals; storing the plurality of received signals and / or filtered received signals; applying a Fourier transform to the received signals; calculating a strength of the received signals in the frequency domain; searching for a fundamental frequency of the received signals; investigating whether low frequency components of the received signals are dominant; applying a high pass filter to the received signals; calculating an envelope of the high pass filtered received signals; estimating a periodicity of the envelope of the high pass filtered received signals; evaluating the periodicity of the envelope of the high pass filtered received signals with respect to the fundamental frequency of the received signals; and calculating one or more indices to detect snoring. 43. The method of detecting snoring sounds as described in 42, wherein the method includes determining that the received signal may contain snoring sounds when the strength of a fundamental frequency of the received signal is dominant in the frequency domain. 44. A method for detecting snoring sounds as described in 42, wherein the method includes determining that the received signal may contain snoring sounds when the intensity of the fundamental frequency of the received signal accounts for 10% or more of the sum of the intensities of the received signals. 45. A method for detecting snoring sounds as described in 42, wherein the method includes determining that the received signal may contain snoring sounds when the intensity and / or prominence of the fundamental frequency is greater than the intensity and prominence of other frequencies. 46. ​​A method for detecting snoring sounds, comprising: acquiring a plurality of sounds produced by a subject; converting the plurality of sounds produced by the subject into received signals; storing the plurality of received signals and / or filtered received signals; applying a high-pass filter having a cutoff frequency of 20 Hz or less to the received signals; calculating an autocorrelation coefficient of the received signals in the time domain using sliding windows of a plurality of window widths; and determining that the received signals contain snoring sounds when the autocorrelation coefficient of the received signals is maximized when a window width of 20 milliseconds or more is used, wherein a time lag range for the calculation of the autocorrelation coefficient is within a range of 1 / 2 to 2 times the window width. 47. The method for detecting snoring sounds or estimating sleep disordered breathing according to claims 3 and 4, further comprising storing a plurality of received signals and / or filtered received signals. Obviously, numerous modifications and variations of the present invention are possible in light of the above teachings.

[0064] It is therefore to be understood that within the scope of the appended claims, the invention may be practiced otherwise than as specifically described herein.

[0065] Explanation of symbols 100 Snorers 102 Snoring 104 Microphone 106 Receiving circuit 108 Sound Intensity Conversion Filter 110 Intensity Periodicity Measurement Filter 112 Intensity Periodicity Evaluation Filter 114 Snoring sound detection filter 116 Sleep-Respiratory Disorder Estimation Filter 118 System Controller Get 200 sounds 201 Converts sound into a received signal 202 Store received signals 203 Process 204 Process 206 Process 208 Process 210 Process 300 processes 302 Process 304 Process 306 Process 308 Process 310 Process 400 Fundamental Frequency Estimation Filter 402 High Pass Filter 404 Envelope Calculation Filter 406 Envelope Periodicity Estimation Filter 408 Envelope Periodicity Evaluation Filter 410 Snoring Detection Filter 500 processes 502 Process 504 Process 506 Process 508 Process 510 Process 600 processes 700 processes 800 processes 802 Process 804 Process 806 Process 900 Processes 902 Process 904 Process

Claims

1. A snoring sound detection device, comprising: one or more microphones for receiving a plurality of sounds produced by the subject; converting sounds produced by the subject into a plurality of received signals; estimating a fundamental frequency of each of the received signals; applying a high-pass filter having a cutoff frequency higher than the fundamental frequency to the received signal; Calculating an envelope of the received signal filtered by the high-pass filter; evaluating the periodicity of the envelope of the high-pass filtered received signal with respect to a fundamental frequency of the received signal; Calculate one or more indices for detecting snoring A controller comprising a circuit configured to: A snoring sound detection device comprising:

2. A method for detecting snoring sounds, comprising: acquiring a plurality of sounds produced by a subject; converting sounds produced by the subject into a plurality of received signals; storing the received signal and / or the filtered received signal; estimating a fundamental frequency of the received signal; applying a high-pass filter to the received signal, the high-pass filter having a cutoff frequency higher than the fundamental frequency; calculating an envelope of the high-pass filtered received signal; estimating the periodicity of the envelope of the high-pass filtered received signal; With respect to the fundamental frequency of the received signal, evaluating the periodicity of said envelope; Calculating one or more indices for detecting snoring; A method for detecting snoring sounds comprising:

3. The method for detecting snoring sounds includes applying a Fourier transform to a received signal, calculating a strength of the received signal in a frequency domain, and searching for a fundamental frequency of the received signal, the time window for the Fourier transform is less than or equal to one second in duration; One of the window functions including a rectangular window, a B-spline window, a Hann window, a Hamming window, and a Tukey window is applied to the received signal before the Fourier transform; The method for detecting snoring sounds according to claim 2.

4. 4. The method of claim 3, further comprising adding non-informative data to the beginning and / or end of each received signal.

5. The method of claim 3 further comprising applying an interpolation to the strength of the received signal in the frequency domain after applying a Fourier transform.

6. 4. The method of claim 3, further comprising using one of the following Fourier-related transforms instead of the Fourier transform: wavelet transform, Laplace transform, fast Fourier transform, discrete Fourier transform, short-time Fourier transform, Z transform, and singular value decomposition.

7. Memorizing the exponents, Using the stored indices for calculation of one or more indices for detecting snoring. The method of claim 2 , further comprising:

8. 3. The method of claim 2, comprising detecting maxima of the strength of the received signal in the frequency domain and determining the maxima having the lowest frequency as the fundamental frequency.

9. 9. The method of claim 8, comprising determining one of the maxima of the intensity of the received signal in the frequency domain as the fundamental frequency according to criteria using the amplitude of each maxima, the intensity of each maxima, the distance of each maxima from other maxima in the frequency domain, and the prominence of each maxima.

10. 3. A method according to claim 2, comprising estimating the fundamental frequency of each received signal by calculating the periodicity of the signal amplitude in the time domain.

11. 3. The method of claim 2, further comprising determining a fundamental frequency of the received signal using a plurality of conditions including that the fundamental frequency be within a range of 10 to 300 Hz.

12. 3. The method of claim 2, further comprising calculating an envelope of the high-pass filtered received signal under the condition that the envelope frequency is within the range of 10 to 300 Hz.

13. 3. The method of claim 2, comprising: applying a Fourier transform to an envelope of the high-pass filtered received signals; and searching for a maximum of local maxima of the intensity of each received signal in the frequency domain after the Fourier transform to estimate the periodicity of the envelope of the high-pass filtered received signals.

14. 1. A method for detecting snoring sounds, comprising: acquiring a plurality of sounds produced by a subject; converting sounds produced by the subject into received signals; storing the received signal and / or the filtered received signal; estimating a fundamental frequency of the received signal; searching for a second harmonic of the received signal; applying a high-pass filter to the received signal, the high-pass filter having a cutoff frequency higher than the fundamental frequency; calculating an envelope of the high-pass filtered received signal; estimating the periodicity of the envelope of the high-pass filtered received signal; With respect to the fundamental frequency of the received signal, evaluating the periodicity of said envelope; A method for detecting snoring sounds comprising calculating one or more indices for detecting snoring.

15. The snoring sound detection method includes searching for a maximum value of a maximum value of a strength of each received signal in a frequency range from a fundamental frequency to a second harmonic of the received signal, and determining whether both the strength of the fundamental frequency of the received signal and the strength of the second harmonic of the received signal are within a frequency range from the fundamental frequency to the second harmonic of the received signal. and determining that the received signal may include snoring sounds when a maximum of local maxima of the strength of each received signal in a frequency range of 100 kHz to 100 kHz is greater than a maximum of local maxima of the strength of each received signal in a frequency range of 100 kHz to 100 kHz.

16. 15. The method for detecting snoring sounds according to claim 14, comprising: searching for a maximum value of a strength of each received signal in a frequency range less than a fundamental frequency of the received signal; and determining that the received signal may include a snoring sound when the strength of the fundamental frequency of the received signal is greater than the maximum value of a strength of each received signal in the frequency range less than the fundamental frequency of the received signal.

17. 1. A method for detecting snoring sounds, comprising: acquiring a plurality of sounds produced by a subject; converting sounds produced by the subject into received signals; storing the received signal and / or the filtered received signal; applying a Fourier transform to the received signal; Calculating the strength of the received signal in the frequency domain; searching for a fundamental frequency of the received signal; Investigating whether the received signal is dominated by low frequency components; applying a high-pass filter to the received signal, the high-pass filter having a cutoff frequency higher than the fundamental frequency; calculating an envelope of the high-pass filtered received signal; estimating the periodicity of the envelope of the high-pass filtered received signal; evaluating the periodicity of the envelope of the high-pass filtered received signal with respect to a fundamental frequency of the received signal; and calculating one or more indices for detecting snoring.

18. 20. A method for detecting snoring sounds according to claim 17, comprising determining that the received signal may contain snoring sounds when a strength of a fundamental frequency of the received signal is dominant in the frequency domain.

19. 18. The method for detecting snoring sounds according to claim 17, further comprising determining that the received signal may contain a snoring sound when the intensity of the fundamental frequency of the received signal accounts for 10% or more of the sum of the intensities of the received signals.

20. when the intensity and / or prominence of the fundamental frequency is greater than the intensity and prominence of other frequencies, the method of detecting snoring sounds comprises determining that the received signal may contain snoring sounds.

20. The method of claim 17, wherein the method comprises:

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