WO2020238954A1 - 呼吸暂停监测的方法及装置 - Google Patents

呼吸暂停监测的方法及装置 Download PDF

Info

Publication number
WO2020238954A1
WO2020238954A1 PCT/CN2020/092597 CN2020092597W WO2020238954A1 WO 2020238954 A1 WO2020238954 A1 WO 2020238954A1 CN 2020092597 W CN2020092597 W CN 2020092597W WO 2020238954 A1 WO2020238954 A1 WO 2020238954A1
Authority
WO
WIPO (PCT)
Prior art keywords
user
sound
apnea
sleep
power spectral
Prior art date
Application number
PCT/CN2020/092597
Other languages
English (en)
French (fr)
Inventor
黄晓萍
Original Assignee
华为技术有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 华为技术有限公司 filed Critical 华为技术有限公司
Priority to EP20812876.9A priority Critical patent/EP3954278A4/en
Priority to US17/615,387 priority patent/US20220225930A1/en
Publication of WO2020238954A1 publication Critical patent/WO2020238954A1/zh

Links

Images

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4806—Sleep evaluation
    • A61B5/4818—Sleep apnoea
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/117—Identification of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4806—Sleep evaluation
    • A61B5/4812—Detecting sleep stages or cycles
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7253—Details of waveform analysis characterised by using transforms
    • A61B5/7257—Details of waveform analysis characterised by using transforms using Fourier transforms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient ; user input means
    • A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2560/00—Constructional details of operational features of apparatus; Accessories for medical measuring apparatus
    • A61B2560/04—Constructional details of apparatus
    • A61B2560/0475—Special features of memory means, e.g. removable memory cards
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
    • A61B2562/02—Details of sensors specially adapted for in-vivo measurements
    • A61B2562/0204—Acoustic sensors
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
    • A61B2562/04—Arrangements of multiple sensors of the same type
    • A61B2562/046—Arrangements of multiple sensors of the same type in a matrix array
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
    • A61B5/087—Measuring breath flow

Definitions

  • This application relates to the field of medical monitoring, and more specifically to a method and device for apnea monitoring.
  • sleep breathing monitoring instruments can be used to monitor the sleep process, so as to help the user know whether there is apnea disorder during sleep.
  • Commonly used sleep apnea monitoring methods include the use of polysomnography (PSG) to diagnose obstructive sleep apnea, use a home sleep apnea screener to monitor the user’s obstructive sleep apnea symptoms or use an oximeter to monitor the user Changes in blood oxygen during sleep to assist in monitoring the user's apnea status.
  • PSG polysomnography
  • the currently used instruments all need to be worn by the user, which has poor comfort, or needs to be monitored in a hospital, and it takes a long time to obtain reports.
  • the embodiments of the present application provide a method and device for apnea monitoring to solve the problem of poor comfort caused by the need to wear an instrument in the process of sleep respiration monitoring, and the inability to achieve simultaneous sleep respiration monitoring by multiple people.
  • a method for apnea monitoring including: collecting the first sound of at least one user through a multi-microphone array of a first terminal, wherein each user is located in a different beam coverage of the multi-microphone array Range; obtain the power spectral density corresponding to the first sound according to the first sound; when the power spectral density is lower than a first threshold, determine that the user has apnea.
  • the multi-microphone array can have directional beam forming by setting.
  • the microphone can collect the good audio signal of the user’s first voice, so that The audio signal is subsequently used to analyze the user's sleep apnea status.
  • the collecting the first voice of at least one user through the multi-microphone array of the first terminal includes: according to the location of the first terminal and the user, The sound pickup parameters of the multi-microphone array are set so that different beams of the multi-microphone array cover different users; the multi-microphone array collects first sounds of the users in different beam coverage areas.
  • the first sound collection is performed on the users located in the directional beam coverage of different multi-microphones through the multi-microphone array of the first terminal.
  • the beam corresponding to a sound source does distinguish the first sounds of different users, and then the apnea status of the user is determined according to the first sounds of different users.
  • the obtaining the power spectral density corresponding to the first sound according to the first sound includes: obtaining the first sound according to the first sound The time-domain signal; the time-domain signal is Fourier transformed to obtain the power spectral density of the first sound.
  • the microphone of the first terminal can obtain the time domain signal of the first sound according to the collected first sound.
  • the time domain signal can be converted into Power spectral density.
  • the method further includes: pre-collecting environmental noise of the environment where the user is located, and determining the frequency bandwidth of the environmental noise; Frequency bandwidth, filtering the environmental noise.
  • the user’s sleep sound can be better obtained, where the sleep sound can be breathing sound or snoring sound to reduce environmental noise Interference to the user's sleep sound analysis process.
  • the method further includes: using a voiceprint recognition algorithm to determine first voices corresponding to different users.
  • the voiceprint recognition algorithm can be used to more accurately distinguish the first voices of different users, so as to facilitate subsequent analysis of the sleep apnea conditions of different users.
  • the method further includes: determining the sleep duration of the user; and determining according to the number of times the apnea occurs during the sleep of the user and the sleep duration The user's breathing disorder AHI index.
  • the user’s AHI index is determined according to the number of apneas and sleep duration during a complete sleep of the user, so as to more intuitively and accurately determine the severity of the user’s apnea degree.
  • a method for monitoring apnea including: collecting first sounds of multiple users through a first terminal; determining first sounds corresponding to different users according to a voiceprint recognition algorithm; A sound acquires the power spectral density corresponding to the first sound; when the power spectral density is lower than a first threshold, it is determined that the user has apnea.
  • the voiceprint recognition algorithm can be used to more accurately distinguish the first voices of different users, so as to more accurately target different users’ voices. Analysis of sleep apnea status.
  • the first terminal has a multi-microphone array; the collecting the first sounds of multiple users through the first terminal includes: using the multi-microphone of the first terminal The array collects the first sounds of multiple users, where each user is located in a different beam coverage area of the multi-microphone array.
  • the multi-microphone array can have directional beam forming by setting.
  • the microphone can collect the good audio signal of the user’s first voice, so that The audio signal is subsequently used to analyze the user's sleep apnea status.
  • the collecting the first sounds of multiple users through the multi-microphone array of the first terminal includes: according to the positions of the first terminal and the user, The sound pickup parameters of the multi-microphone array are set so that different beams of the multi-microphone array cover different users; the multi-microphone array collects the first sounds of the users in different beam coverage areas.
  • the first sound collection is performed on users located within the coverage of the directional beams of different multi-microphones through the multi-microphone array of the first terminal.
  • the beam corresponding to the source does distinguish the first voices of different users, and then the apnea status of the user is determined according to the voices of different users.
  • the sleep apnea monitoring of one user or multiple users can be realized without the user wearing a monitoring instrument.
  • the obtaining the power spectral density corresponding to the first sound according to the first sound includes: obtaining the first sound according to the first sound The time-domain signal; the time-domain signal is Fourier transformed to obtain the power spectral density of the first sound.
  • the microphone can obtain the time domain signal of the first sound according to the collected first sound.
  • the time domain signal can be converted into power spectral density by Fourier transform.
  • the method further includes: determining the sleep duration of the user; determining according to the number of times the apnea occurs during the sleep of the user and the sleep duration The AHI index of the user.
  • the user's sleep sound can be better obtained, where the sleep sound can be breathing sound or snoring sound to reduce environmental noise Interference to the user's sleep sound analysis process.
  • an apparatus for detecting apnea including: a sound collecting unit for collecting a first sound of at least one user; and a data processing unit for acquiring information of the first sound according to the first sound Power spectral density; the data processing unit is further configured to determine that the user has apnea when the power spectral density is lower than a first threshold.
  • the obtaining the power spectral density of the first sound according to the first sound includes: obtaining the power spectral density of the first sound according to the first sound Time domain signal; the time domain signal is Fourier transformed to obtain the power spectral density of the first sound.
  • the sound collection unit is further configured to collect in advance the environmental noise of the environment where the user is located; the data processing unit is configured to determine the environmental noise According to the frequency bandwidth of the environmental noise, the environmental noise is filtered.
  • the data processing unit is further configured to use a voiceprint recognition algorithm to determine first voices corresponding to different users.
  • the data processing unit is further configured to determine the sleep duration of the user, and according to the number of apneas and the total number of apneas that occur during the sleep of the user.
  • the sleep duration determines the AHI index of the user.
  • an apparatus for apnea monitoring including at least one microphone and a processor, wherein the apparatus is used to execute the method according to any one of the implementation manners of the first aspect and the second aspect.
  • the apnea conditions of multiple users can be monitored simultaneously, and the users There is no need to wear equipment and will not affect sleep.
  • Figure 1 is a possible application scenario of the apnea monitoring method provided by this application.
  • Fig. 2 is a schematic flowchart of a method for apnea monitoring provided by an embodiment of the application.
  • FIG. 3 is a schematic flowchart of another method for monitoring apnea provided by an embodiment of the application.
  • FIG. 4 is a schematic flowchart of another method for monitoring apnea according to an embodiment of the application.
  • FIG. 5 is a schematic flowchart of another method for monitoring apnea according to an embodiment of the application.
  • Fig. 6 is a schematic structural diagram of an apnea monitoring device provided by an embodiment of the application.
  • Fig. 7 is a schematic structural diagram of another apnea monitoring device provided by an embodiment of the application.
  • Obstructive sleep apnea hypopnea syndrome generally refers to an adult’s normal sleep time (e.g. 7 hours) every night, the mouth and nose have stopped airflow for 10 seconds or more for more than 30 times. Times or more.
  • the traditional screening and diagnosis methods for obstructive sleep apnea include polysomnography (PSG) or the use of household sleep apnea screeners, blood oximeters and other equipment for monitoring. The following is a brief introduction to the currently adopted user sleep monitoring methods.
  • PSG is the "gold standard" for diagnosing sleep apnea syndrome. While monitoring obstructive sleep apnea, it can also identify many sleep-related diseases. The monitoring process is mainly that during the examination, the doctor pastes some electrodes on the examinee and connects them to the instrument. By detecting the user's nose and mouth airflow, snoring, chest and abdomen movements, it is judged whether there is apnea or hypoventilation. It happened. In addition, PSG can further obtain the maximum and average duration of apneas to distinguish between central, obstructive, or mixed apneas.
  • PSG needs to use a specific PSG instrument to monitor the obstructive sleep apnea of users, and the monitoring is cumbersome, the detection location environment is too restrictive, and it causes a poor sleep experience for the user, which is difficult to achieve in daily life. Convenient monitoring.
  • the oximeter assists in screening sleep apnea.
  • the blood oxygen of the user can be monitored during sleep to achieve the purpose of assisting in the screening of sleep apnea syndrome.
  • the drop in blood oxygen may be caused by a variety of reasons, such as heart failure.
  • the method of assisted monitoring by this oximeter cannot accurately identify the cause of the drop in blood oxygen, which leads to large errors in the monitoring results. .
  • the embodiments of the present application provide a method that does not need to be worn by the user and can simultaneously monitor the sleep of multiple people through one device.
  • the sleep monitoring method provided in the present application will be introduced below in conjunction with the drawings.
  • Figure 1 shows a possible application scenario of the apnea monitoring method provided by this application.
  • the microphone can have an omnidirectional pickup response, that is, the microphone can respond to sounds from all directions.
  • a multi-microphone array composed of multiple microphones can form a directional response or beam field pattern. After design, the microphone array can be more sensitive to sounds from one or more specific directions, and can collect better sound signals in that specific direction.
  • the sleep monitoring method uses the beamforming technology of the multi-microphone array to collect the audio signals of the sleep sounds of users located in different positions, and the sleep process of multiple users can be performed simultaneously without the user wearing an instrument. Monitor, and then determine whether the multiple users have obstructive sleep pause or the severity of obstructive sleep pause.
  • the sleep sound may be, for example, the breathing sound and snoring sound of the user during sleep.
  • the multi-microphone array may be an existing multi-microphone array in the existing equipment, that is, an existing terminal device equipped with a multi-microphone array can be used to collect sleep sounds of multiple users, such as a mobile phone or a recording device. Pen etc.
  • Fig. 2 shows a schematic flowchart of a method for apnea monitoring provided by an embodiment of the present application. The method includes the following steps:
  • S210 Collect a first voice of at least one user.
  • a first terminal configured with a microphone may be used to collect the first voice of the monitored user, where the first terminal may be, for example, a mobile phone or other recording device configured with a single microphone or multiple microphone arrays;
  • the sound is the sleep sound of the user during sleep, for example, it may be the breathing sound and snoring sound of the user during sleep.
  • the method for monitoring apnea provided by the embodiments of the present application can be used to monitor the sleep and breathing status of multiple users at the same time. Therefore, when the sleep sounds of multiple users are collected, in order to realize the subsequent sleep of each user. To analyze the apnea status, it is necessary to identify the sleep sounds corresponding to different users.
  • the embodiment of the present application mainly adopts two methods to distinguish the sleep sounds of different users: First, the first terminal equipped with a multi-microphone array is used to collect the first sounds of multiple users, and the beam is formed according to the directional beam of the multi-microphone array.
  • a first device configured with a multi-microphone array is used to record the first voice of at least one user.
  • the pickup parameters of the microphone array such as pickup direction, pickup angle width, etc.
  • the phase-adjusted signals of each unit are superimposed to obtain the main lobe signal in the direction specified by the user, so that the first device can accurately collect the first voice of the user at different positions .
  • the first sound can be determined according to the beam coverage corresponding to the source of the first sound.
  • the first voice corresponding to each user can be determined according to the beam coverage of the multiple users and the beam corresponding to the source of the multiple first sounds.
  • the first device configured with a multi-microphone array may be a mobile phone, a voice recorder or other terminal devices with a multi-microphone array, which is not limited in this application.
  • the first voice of the user is collected by the first device
  • the first voice of different users is determined by a voiceprint recognition algorithm.
  • voiceprint is a sound wave spectrum that carries speech information displayed by an electroacoustic instrument. Because people’s vocal organs differ greatly in size and shape, the voiceprint atlas of any two people are not the same. Under normal circumstances, different people can be distinguished by voiceprint information or to determine whether the collected sound belongs to The voice of the same person.
  • the process of determining the first voice corresponding to the user by means of voiceprint recognition may be, for example, in the process of performing a formal sleep breathing monitoring of the monitored user, collecting the first voice of each user, and obtaining short voices of different users.
  • the voiceprint recognition model can be an existing model, such as a Gaussian mixture model (GMM), a support vector machine (SVM) model, a channel model, an identity vector (i-vector) ) Model etc.
  • GMM Gaussian mixture model
  • SVM support vector machine
  • i-vector identity vector
  • the GMM model is taken as an example to further introduce the voiceprint recognition process.
  • the difference in voices of different people is mainly reflected in the difference in the short-term speech spectrum, and the difference in the short-term speech spectrum can be measured by its probability density function.
  • the GMM model can fit the probability density of the spatial distribution with the weighted sum of multiple Gaussian probability density functions.
  • the fitted Gaussian probability density function can smoothly approximate the probability density function of any shape, and is an easy-to-handle parameter model .
  • the parameter model may be a model in which the mean vector of each Gaussian component of the GMM model is arranged together to form a super vector as a certain speaker.
  • the voiceprint recognition model involved in this embodiment may be a GMM model based on Mel frequency cepstrum coefficient (MFCC).
  • the process of voiceprint recognition through the GMM model can be: (1) GMM model training. In the GMM training process, multiple voice signals can be collected, and the feature parameters of the voice signal can be extracted after preprocessing the multiple voice signals. Among them, the voice feature parameter can be, for example, MFCC; use the feature parameter of the voice signal as a sample training GMM model.
  • Voiceprint recognition process Obtain the characteristic parameters of the first sound according to the collected first sound, and compare the characteristic parameters of the first sound with the established GMM model, and determine the characteristic parameters of the first sound according to the recognition accuracy rate of the GMM The user corresponding to the first voice.
  • voiceprint recognition process used in the embodiments of the present application can also refer to other existing processes, and is not limited to the voiceprint recognition method mentioned above.
  • the process of eliminating or reducing environmental noise in the embodiment of the present application may be: before the user’s sleep sound signal is formally collected, the environmental noise of the environment where the user is located is first collected to obtain the sound wave corresponding to the environmental noise.
  • the multi-microphone In the follow-up process of sound recording for insomnia monitoring of users, the multi-microphone generates a sound wave with the opposite phase and the same frequency and amplitude as the pre-recorded environmental noise sound wave, so that it interferes with the environmental noise.
  • Phase cancellation, thereby eliminating environmental noise; or, according to the frequency bandwidth of the pre-recorded environmental noise set the filter frequency bandwidth of the filter so that the filter can filter the noise during the recording process.
  • the method of filtering environmental noise can also be other existing methods, and the application is not limited to this.
  • the first sound of the user during sleep is recorded through the microphone, and then the user’s sleep apnea is analyzed, so that the user can monitor sleep apnea without wearing a monitoring instrument, which improves the user’s monitoring Comfort in the process.
  • Step S220 Acquire the power spectral density corresponding to the first sound according to the first sound.
  • the sound signal obtained through the microphone is a time domain signal.
  • the time domain signal is converted into a frequency domain signal.
  • the obtained time-domain signals of sleep sounds of different users are obtained through Fourier transform to obtain their corresponding power spectra, that is, power spectral density.
  • the formula used in the Fourier transform process is: Among them, ⁇ is frequency, t is time, e- i ⁇ t is a complex variable function, f(t) can be the time domain signal of the first sound, and F( ⁇ ) can be the frequency domain signal after Fourier transform.
  • the first threshold may be a value set according to the user's normal breathing situation.
  • the process of obtaining the first threshold may be: performing time-frequency conversion according to the time-frequency signal of the sleep sound in the user's normal breathing state (for example, a breathing state in which no apnea has occurred just after falling asleep) to obtain the breathing sound in this state And take the average value of the respiratory spectrum peaks of multiple consecutive times (such as 5 to 10 times), and then take 30% to 50% of the average value as the first threshold to determine whether the user has apnea and breathing.
  • the severity of the suspension can be optimized and adjusted based on clinical test data and with reference to the PSG standard accuracy to achieve the best sensitivity and specificity.
  • the second threshold may be, for example, 10s.
  • the respiratory disorder (apnea-hypopnea index, AHI) index of the user is determined according to the number of times the power spectral density of the first sound of the different user is lower than the first threshold.
  • the AHI index of the user may be determined according to the number of times the power spectral density of the first sound is lower than the first threshold and the sleep duration of the user.
  • the user's sleep time may be recorded.
  • the sleep time may be the sleep duration of the user throughout the night.
  • the AHI index of the user is determined according to the power spectral density of the user's breathing sound and/or the power spectral density of the snoring sound and the sleep time of the user.
  • the power spectral density of the user when the power spectral density of the user’s breathing sound or the power spectral density of the snoring sound is lower than the first threshold, and the length of time lower than the first threshold reaches the second threshold, it is recorded as an apnea.
  • the severity of the apnea of the user is determined according to the user's AHI index.
  • the AHI index when the AHI is in the range less than or equal to 5, it is determined that the user sleeps normally, that is, no apnea is considered; when the AHI value is in the range of 5 to 15, the user is considered to have mild Sleep apnea status; when the AHI value falls within the range of 15 to 30, the user is considered to have moderate sleep apnea status; when the AHI is within the range greater than or equal to 30, the user is considered to have severe sleep Apnea condition.
  • the apnea status of multiple users can be monitored simultaneously, and the user does not need to wear an instrument , Will not affect sleep.
  • Fig. 3 shows a schematic flowchart of a method for apnea monitoring provided by an embodiment of the present application. It includes the following steps:
  • a first terminal configured with a microphone may be used to collect the first voice of the monitored user.
  • the first terminal may be, for example, a mobile phone or other recording device configured with a single microphone or multiple microphone arrays; the first voice is
  • the sleep sound of the user during sleep may be, for example, the breathing sound and snoring sound of the user during sleep.
  • multiple users to be monitored may be respectively located within the coverage of the directional beam of the multi-microphone array.
  • the sleep apnea monitoring function of the first device is turned on manually or in other ways.
  • the first terminal may also recognize the first voices of different users through a voiceprint recognition algorithm, so as to more accurately distinguish the first voices of different users.
  • the process of recording the first voice of at least one user with a first device configured with a multi-microphone array may include: setting the pickup parameters of the microphone array, such as pickup direction, pickup angle width, etc., combined The position of the first device and the user can be used within the beam coverage of the multi-microphone array; in addition, by adjusting the phase of the signal of the microphone array unit, the phase-adjusted signals of each unit are superimposed to obtain the user-specified direction The main lobe signal, so that the first device accurately collects the first sounds of users located in different positions.
  • the first sound can be determined according to the beam coverage corresponding to the source of the first sound.
  • the first voice corresponding to each user can be determined according to the beam coverage of the multiple users and the beam corresponding to the source of the multiple first sounds.
  • the process of eliminating or reducing environmental noise in the embodiment of the present application may be: before the user’s sleep sound signal is formally collected, the environmental noise of the environment where the user is located is first collected to obtain the sound wave corresponding to the environmental noise.
  • the multi-microphone In the follow-up process of sound recording for insomnia monitoring of users, the multi-microphone generates a sound wave with the opposite phase and the same frequency and amplitude as the pre-recorded environmental noise sound wave, so that it interferes with the environmental noise.
  • Phase cancellation, thereby eliminating environmental noise; or, according to the frequency bandwidth of the pre-recorded environmental noise set the filter frequency bandwidth of the filter so that the filter can filter the noise during the recording process.
  • the method of filtering environmental noise can also be other existing methods, and the application is not limited to this.
  • the first sound of the user during sleep is recorded through the microphone, and then the user’s sleep apnea is analyzed, so that the user can monitor sleep apnea without wearing a monitoring instrument, which improves the user’s monitoring Comfort in the process.
  • S320 Acquire a power spectral density of the first sound according to the first sound of the user.
  • the multi-microphone array can obtain the time domain signal of the first sound according to the collected first sound.
  • the Fourier transform can be used to convert the signal in the time domain into the power spectral density in the frequency domain.
  • the specific conversion process refer to step S220, and to avoid repetition, it will not be repeated here.
  • the first threshold may be a value set according to the user's normal breathing situation.
  • the process of obtaining the first threshold may be: performing time-frequency conversion according to the time-frequency signal of the sleep sound in the user's normal breathing state (for example, a breathing state in which no apnea has occurred just after falling asleep) to obtain the breathing sound in this state And take the average value of the respiratory spectrum peaks of multiple consecutive times (such as 5 to 10 times), and then take 30% to 50% of the average value as the first threshold to determine whether the user has apnea and breathing.
  • the severity of the suspension can be optimized and adjusted based on clinical test data and with reference to the PSG standard accuracy to achieve the best sensitivity and specificity.
  • the second threshold may be, for example, 10s or 15s.
  • the respiratory disorder AHI index of the user is determined.
  • the respiratory disorder AHI index of the user is determined according to the number of times the power spectral density of the first sound is lower than the first threshold and the sleep duration of the user.
  • the user's sleep time may be recorded.
  • the sleep time may be the sleep duration of the user throughout the night.
  • the user’s breath sound power spectral density or snoring sound power spectral density is lower than the first threshold, and the length of time lower than the first threshold reaches the second threshold, it is recorded as an apnea.
  • the user's apnea level can be judged according to the AHI index. For example, when the AHI is in the range less than or equal to 5, it is determined that the user sleeps normally, that is, no apnea is considered; when the AHI value is in the range of 5 to 15, the user is considered to have a light sleep Apnea condition; when the value of AHI is in the range of 15 to 30, the user is considered to have moderate sleep apnea; when the AHI is in the range greater than or equal to 30, the user is considered to have severe sleep apnea situation.
  • the sleep sound of a user is collected through a multi-microphone array, which can realize sleep apnea monitoring and risk assessment for multiple users in a non-contact manner.
  • FIG. 4 shows a schematic flowchart of another method for monitoring apnea provided by an embodiment of the present application. It includes the following steps:
  • S410 Collect first sounds of multiple users through the first terminal.
  • the first terminal may be a mobile phone, a voice recorder, etc., and may also be other terminal devices configured with a single or multiple microphones.
  • the first sound of the user is recorded through a microphone in the first device, where the first sound is breathing sound, snoring sound, etc. of the user during sleep.
  • the microphone in the first terminal may obtain the time domain signal of the first sound according to the collected first sound.
  • the process of eliminating or reducing environmental noise in the embodiment of the present application may be: before the user’s sleep sound signal is formally collected, the environmental noise of the environment where the user is located is first collected to obtain the sound wave corresponding to the environmental noise.
  • the multi-microphone In the subsequent sound recording process of the insomnia monitoring of the user, the multi-microphone generates a sound wave with the opposite phase and the same frequency and amplitude as the pre-recorded environmental noise sound wave, so that it interferes with the environmental noise to achieve Phase cancellation to eliminate environmental noise; or, according to the frequency bandwidth of the pre-recorded environmental noise, set the filter frequency bandwidth of the filter so that the filter can filter the noise during the recording process.
  • the method of filtering environmental noise can also be other existing methods, and the application is not limited to this.
  • the first sound of the user during sleep is recorded through the microphone, and then the user’s sleep apnea is analyzed, so that the user can monitor sleep apnea without wearing a monitoring instrument, which improves the user’s monitoring Comfort in the process.
  • S420 Determine the first voice corresponding to the user according to the voiceprint recognition algorithm.
  • the user’s muscles may not be in the pre-sleep phase.
  • a segment of the user's breathing and/or snoring sounds are collected.
  • the voice feature of the user's first voice is extracted, and voiceprint models of different users are established.
  • the first voice of different users is determined according to the first voice obtained in the formal monitoring process and the voiceprint models of different users.
  • voiceprint is a sound wave spectrum that carries speech information displayed by an electroacoustic instrument. Because people’s vocal organs differ greatly in size and shape, the voiceprint atlas of any two people are different. Under normal circumstances, different people can be distinguished by voiceprint information or to determine whether the collected sound belongs to The voice of the same person.
  • the process of determining the first voice corresponding to the user by means of voiceprint recognition may be, for example, collecting the first voice of each user in advance, for example, breathing sound and/ Or snoring, and extract the characteristics of the first voice of different users, for example, obtain the short-term speech spectrum of different users’ voices; establish a voiceprint recognition model based on the characteristics of the first voice of different users; collect the first voice of the user during sleep Voice, and determine the first voice of different users according to the established voiceprint recognition model.
  • the process of establishing a user's voiceprint recognition model may be, for example, obtaining the corresponding probability density function according to the characteristics of the user's first voice, such as a short-term speech spectrum, and establishing a Gaussian mixture model (GMM)
  • GMM Gaussian mixture model
  • the probability density of the spatial distribution is fitted with the weighted sum of multiple Gaussian probability density functions, so that the Gaussian mixture model can smoothly approximate the probability density function of any shape and obtain a parameter model that is easy to handle.
  • the parameter model obtained according to the probability density function of the short-term speech spectrum arranges the mean vector of each Gaussian component of the Gaussian mixture model together to form a super vector as a model of a certain user.
  • the first voices corresponding to different users can be determined.
  • the process of establishing different user parameter models through voiceprint recognition can refer to the existing process, which will not be repeated here.
  • S430 Acquire a power spectral density corresponding to the first sound according to the first sound.
  • Fourier transform is performed according to the time domain signal obtained by the microphone to obtain the power spectral density of the first sounds of different users.
  • the first threshold may be a value set according to the user's normal breathing situation.
  • the process of obtaining the first threshold may be: performing time-frequency conversion according to the time-frequency signal of the sleep sound in the user's normal breathing state (for example, a breathing state in which no apnea has occurred just after falling asleep) to obtain the breathing sound in this state And take the average value of the respiratory spectrum peaks of multiple consecutive times (such as 5 to 10 times), and then take 30% to 50% of the average value as the first threshold to determine whether the user has apnea and breathing.
  • the severity of the suspension can be optimized and adjusted based on clinical test data and with reference to the PSG standard accuracy to achieve the best sensitivity and specificity.
  • the second threshold may be, for example, 10s or 15s.
  • the respiratory disorder AHI index of the user is determined according to the number of times the power spectral density of the first voice of the different user is lower than the first threshold.
  • the user’s AHI index is determined according to the number of times the user has apneas and the user’s sleep duration, and the AHI index can intuitively reflect the severity of the user’s sleep apnea.
  • the power spectral density of the user’s breathing sound or the power spectral density of the snoring sound is lower than the first threshold, and the length of time lower than the first threshold reaches the second threshold, it is recorded as an apnea.
  • k the total number of apneas
  • the power spectral density of the sound is lower than the first threshold, and the number of times that the length of time lower than the first threshold reaches the second threshold.
  • step S330 For the manner of determining the sleep apnea status of the user according to the value of the AHI index, refer to step S330. To avoid repetition, details are not described herein again.
  • the first voices of different users are collected through a microphone, and the voiceprint recognition algorithm is used to distinguish the corresponding sleep sounds of different users, which can achieve non-contact sleep apnea for multiple users Monitoring and risk assessment.
  • Fig. 5 shows a schematic flowchart of a method for apnea monitoring provided by an embodiment of the present application.
  • the apnea monitoring method of the embodiment of the present application may further include the following steps.
  • the user when it is determined that the user has apnea or the apnea has occurred for a certain period of time, the user is awakened.
  • the second device may be a smart device surrounding the user, for example, a smart speaker, a smart light, a smart wearable device, a smart phone, etc., or it may be a smart device of the user's emergency contact, such as a smart phone.
  • the first device when it is monitored that the duration of the user's apnea has reached the third threshold, the first device sends a wake-up message to the second device, where the third threshold may be, for example, 20s.
  • S520 The second device wakes up the user who has experienced apnea by means of vibration, sound, light, etc.
  • the second device when it receives the wake-up message of the first device, it can wake up the user in different ways, for example, by means of vibration, sound, light, etc., to wake up the user who has experienced apnea.
  • the first device may send a wake-up call to linked devices around the user, such as smart speakers, smart lights, smart wearable devices, smart phones, etc. Messages enable peripheral smart devices to wake up users in time through vibration, sound, light and other corresponding methods.
  • the third threshold may be, for example, 20s or other values set with reference to a doctor's suggestion, which is not limited in this application.
  • Fig. 6 shows a device for monitoring sleep apnea provided by an embodiment of the present application.
  • the device 600 includes a sound collection unit 610 and a data processing unit 620.
  • the sound collection unit 610 is used to collect the first sound of at least one user.
  • the data processing unit 620 is configured to obtain the power spectral density of the first sound according to the first sound.
  • the data processing unit 620 may also be configured to determine the AHI index of the user according to the power spectrum density.
  • Fig. 7 shows another device for monitoring sleep apnea provided by an embodiment of the present application.
  • the device 700 includes at least one microphone 710 and a processor 720.
  • the microphone 710 is used to collect the first voice of at least one user.
  • the processor 720 is configured to obtain the power spectral density of the first sound according to the first sound.
  • the processor 720 may also be configured to determine the AHI index of the user according to the power spectral density.
  • the disclosed system, device, and method may be implemented in other ways.
  • the device embodiments described above are only illustrative.
  • the division of the units is only a logical function division, and there may be other divisions in actual implementation, for example, multiple units or components can be combined or It can be integrated into another system, or some features can be ignored or not implemented.
  • the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solutions of the embodiments.
  • each unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium.
  • the technical solution of this application essentially or the part that contributes to the existing technology or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in each embodiment of the present application.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk and other media that can store program code .

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Animal Behavior & Ethology (AREA)
  • General Health & Medical Sciences (AREA)
  • Biophysics (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Medical Informatics (AREA)
  • Molecular Biology (AREA)
  • Surgery (AREA)
  • Veterinary Medicine (AREA)
  • Pathology (AREA)
  • Public Health (AREA)
  • Physiology (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Psychiatry (AREA)
  • Signal Processing (AREA)
  • Mathematical Physics (AREA)
  • Pulmonology (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)

Abstract

一种呼吸暂停监测的方法及装置，涉及医疗监测领域。该呼吸暂停监测的方法包括：通过第一终端采集至少一个用户的第一声音，其中，利用第一终端配置的多麦克风阵列的指向性波束形成确定不同波束覆盖范围内用户对应的第一声音或者通过声纹识别算法区分不同用户的第一声音；根据第一声音获取第一声音对应的功率谱密度；当功率谱密度低于第一阈值时，确定用户发生呼吸暂停。根据该呼吸暂停监测的方法，可以在无需用户佩戴的条件下，实现一个或者多个用户的呼吸暂停监测，操作简单灵活，提升用户的监测体验。

Description

呼吸暂停监测的方法及装置
本申请要求在2019年5月31日提交中国国家知识产权局、申请号为201910471804.2的中国专利申请的优先权，发明名称为“呼吸暂停监测的方法及装置”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及医疗监测领域，更具体地涉及一种呼吸暂停监测的方法及装置。
背景技术
随着现在社会压力增大、体重超重以及生活不规律等因素的影响，20％以上的人群都存在睡眠呼吸暂停或者低通气的症状。在日常生活中，由于患有睡眠呼吸障碍的用户不易自主察觉发生呼吸暂停的时间和轻重程度，因而可能会导致该症状无法得到及时的治疗和改善，从而给用户健康带来极大的威胁。
目前，可以利用多种睡眠呼吸监测仪器监测睡眠过程，从而帮助用户获知在睡眠过程中是否存在呼吸暂停障碍。通常采用的睡眠呼吸监测方法有，利用多导睡眠监测(polysomnography，PSG)诊断阻塞性睡眠呼吸暂停，通过家用睡眠呼吸暂停筛查仪监测用户的阻塞性睡眠呼吸暂停症状或者利用血氧仪监测用户睡眠时的血氧变化，辅助监测用户的呼吸暂停状况。但是，目前采用的仪器均需用户佩戴，舒适性差，抑或需要在医院进行监测，且获取报告的时间长。
发明内容
本申请实施例提供一种呼吸暂停监测的方法及装置，以解决睡眠呼吸监测过程中需要仪器佩戴造成的舒适性差，且无法实现多人同时进行睡眠呼吸监测的问题。
第一方面，提供了一种呼吸暂停监测的方法，包括：通过第一终端的多麦克风阵列采集至少一个用户的第一声音，其中，每个所述用户位于所述多麦克风阵列的不同波束覆盖范围；根据所述第一声音获取所述第一声音对应的功率谱密度；当所述功率谱密度低于第一阈值时，确定所述用户发生呼吸暂停。
应理解，多麦克风阵列通过设置可以具有指向性波束形成，当用户分别位于使该多麦克风阵列的指向性波束的覆盖范围内时，麦克风可以采集到该用户第一声音的良好的音频信号，以便于后续采用该音频信号进行用户睡眠呼吸暂停状况分析。
结合第一方面，在第一方面的某些实现方式中，所述通过第一终端的多麦克风阵列采集至少一个用户的第一声音，包括：根据所述第一终端和所述用户的位置，设置所述多麦克风阵列的拾音参数，使得所述多麦克风阵列的不同波束覆盖不同的所述用户；所述多麦克风阵列分别采集不同波束覆盖范围的所述用户的第一声音。
根据本申请实施例提供的呼吸暂停监测的方法，通过第一终端的多麦克风阵列对位于不同多麦克风的指向性波束覆盖范围内的用户进行第一声音采集，可以根据用户所在波束覆盖范围以及第一声音来源对应的波束确区分不同用户的第一声音，进而根据不同用户的 第一声音确定用户的呼吸暂停状况。
结合第一方面，在第一方面的某些实现方式中，所述根据所述第一声音获取所述第一声音对应的功率谱密度，包括：根据所述第一声音获取所述第一声音的时域信号；将所述时域信号通过傅里叶变换获取所述第一声音的功率频谱密度。
应理解，第一终端的麦克风可以根据采集的第一声音获取该第一声音的时域信号，为了便于直观分析该第一声音的音频信号，可以通过傅里叶变换将该时域信号转换为功率频谱密度。
结合第一方面，在第一方面的某些实现方式中，所述方法还包括：预先采集所述用户所处环境的环境噪声，并确定所述环境噪声的频率带宽；根据所述环境噪声的频率带宽，过滤所述环境噪声。
根据本申请实施例提供的呼吸暂停监测的方法，通过过滤被监测用户所在环境的环境噪声，可以更好地获得用户的睡眠声音，其中，该睡眠声音可以为呼吸音或者鼾声，以降低环境噪声对用户睡眠声音分析过程中的干扰。
结合第一方面，在第一方面的某些实现方式中，所述方法还包括：通过声纹识别算法，确定不同所述用户分别对应的第一声音。
应理解，当同时对多用户进行睡眠呼吸暂停监测时，可以通过声纹识别算法更准确地区分不同用户的第一声音，以便于后续针对不同用户的睡眠呼吸暂停状况进行分析。
结合第一方面，在第一方面的某些实现方式中，所述方法还包括：确定所述用户的睡眠时长；根据所述用户睡眠过程中发生所述呼吸暂停的次数和所述睡眠时长确定所述用户的呼吸紊乱AHI指数。
根据本申请实施例提供的呼吸暂停监测的方法，通过根据用户一次完整睡眠过程中发生呼吸暂停的次数和睡眠时长确定该用户的AHI指数，从而更直观、更准确地判断该用户的呼吸暂停轻重程度。
第二方面，提供了一种呼吸暂停监测的方法，包括：通过第一终端采集多个用户的第一声音；根据声纹识别算法确定不同所述用户分别对应的第一声音；根据所述第一声音获取所述第一声音对应的功率频谱密度；当所述功率谱密度低于第一阈值时，确定所述用户发生呼吸暂停。
根据本申请实施例提供的呼吸暂停监测的方法，当同时对多用户进行睡眠呼吸暂停监测时，可以通过声纹识别算法更准确地区分不同用户的第一声音，从而更准确地针对不同用户的睡眠呼吸暂停状况进行分析。
结合第二方面，在第二方面的某些实现方式中，所述第一终端具有多麦克风阵列；所述通过第一终端采集多个用户的第一声音，包括：通过第一终端的多麦克风阵列采集多个用户的第一声音，其中，每个所述用户位于所述多麦克风阵列的不同波束覆盖范围。
应理解，多麦克风阵列通过设置可以具有指向性波束形成，当用户分别位于使该多麦克风阵列的指向性波束的覆盖范围内时，麦克风可以采集到该用户第一声音的良好的音频信号，以便于后续采用该音频信号进行用户睡眠呼吸暂停状况分析。
结合第二方面，在第二方面的某些实现方式中，所述通过第一终端的多麦克风阵列采集多个用户的第一声音，包括：根据所述第一终端和所述用户的位置，设置所述多麦克风阵列的拾音参数，使得所述多麦克风阵列的不同波束覆盖不同的所述用户；所述多麦克风阵列分别采集不同波束覆盖范围的所述用户的第一声音。
根据本申请实施例提供的呼吸暂停监测的方法，通过第一终端的多麦克风阵列对位于不同多麦克风的指向性波束覆盖范围内的用户进行第一声音采集，可以根据用户所在波束以及第一声音来源对应的波束确区分不同用户的第一声音，进而根据不同用户的声音确定用户的呼吸暂停状况。通过本实施例的方法，无需用户佩戴监测仪器，即可实现一个用户或者多个用户的睡眠呼吸暂停监测。
结合第二方面，在第二方面的某些实现方式中，所述根据所述第一声音获取所述第一声音对应的功率频谱密度，包括：根据所述第一声音获取所述第一声音的时域信号；将所述时域信号通过傅里叶变换获取所述第一声音的功率频谱密度。
应理解，麦克风可以根据采集的第一声音获取该第一声音的时域信号，为了便于直观分析该第一声音的音频信号，可以通过傅里叶变换将该时域信号转换为功率频谱密度。
结合第二方面，在第二方面的某些实现方式中，所述方法还包括：确定所述用户的睡眠时长；根据所述用户睡眠过程中发生所述呼吸暂停的次数和所述睡眠时长确定所述用户的AHI指数。
根据本申请实施例提供的呼吸暂停监测的方法，通过过滤被监测用户所在环境的环境噪声，可以更好地获得用户的睡眠声音，其中，该睡眠声音可以为呼吸音或者鼾声，以降低环境噪声对用户睡眠声音分析过程中的干扰。
第三方面，提供了一种呼吸暂停检测的装置，包括：声音采集单元，用于采集至少一个用户的第一声音；数据处理单元，用于根据所述第一声音获取所述第一声音的功率谱密度；所述数据处理单元，还用于当所述功率谱密度低于第一阈值时，确定所述用户发生呼吸暂停。
结合第三方面，在第三方面的某些实现方式中，所述根据所述第一声音获取所述第一声音的功率谱密度，包括：根据所述第一声音获取所述第一声音的时域信号；将所述时域信号通过傅里叶变换获取所述第一声音的功率频谱密度。
结合第三方面，在第三方面的某些实现方式中，所述声音采集单元，还用于预先采集所述用户所处环境的环境噪声；所述数据处理单元，用于确定所述环境噪声的频率带宽，并根据所述环境噪声的频率带宽，过滤所述环境噪声。
结合第三方面，在第三方面的某些实现方式中，所述数据处理单元，还用于通过声纹识别算法，确定不同所述用户分别对应的第一声音。
结合第三方面，在第三方面的某些实现方式中，所述数据处理单元，还用于确定所述用户的睡眠时长，并根据所述用户睡眠过程中发生所述呼吸暂停的次数和所述睡眠时长确定所述用户的AHI指数。
第四方面，提供了一种呼吸暂停监测的装置，包括至少一个麦克风和处理器，其中，所述装置用于执行如第一方面和第二方面中任一实现方式所述的方法。
根据本申请实施例提供的呼吸暂停监测的方法及装置，通过利用多麦克风阵列的指向性多波束形成或者声纹识别区分不同用户的声音信息，能够同时监测多个用户的呼吸暂停状况，且用户不需佩戴仪器，不会对睡眠造成影响。
附图说明
图1为本申请提供的呼吸暂停监测方法一种可能的应用场景。
图2为本申请实施例提供的一种呼吸暂停监测的方法的示意性流程图。
图3为本申请实施例提供的另一种呼吸暂停监测的方法的示意性流程图。
图4为本申请实施例提供的又一种呼吸暂停监测的方法的示意性流程图。
图5为本申请实施例提供的又一种呼吸暂停监测的方法的示意性流程图。
图6为本申请实施例提供的一种呼吸暂停监测装置的示意性结构图。
图7为本申请实施例提供的另一种呼吸暂停监测装置的示意性结构图。
具体实施方式
下面将结合附图，对本申请中的技术方案进行描述。
阻塞性睡眠呼吸暂停综合征(obstructive sleep apnea hypopnea syndrome，OSAHS)，一般是指成人每晚正常睡眠时间(如7小时)下，口、鼻气流停止流通达10秒或更长时间的次数超过30次以上。目前，传统的阻塞性睡眠呼吸暂停的筛查诊断的方法有多导睡眠检测PSG或者利用家用睡眠呼吸暂停筛查仪、血氧仪等仪器进行监测。以下对目前采用的用户睡眠监测方法进行简单介绍。
1、利用多导睡眠监测PSG诊断阻塞性睡眠呼吸暂停。
PSG是诊断睡眠呼吸暂停综合征的“金标准”，其在监测阻塞性睡眠呼吸暂停的同时，也能对许多睡眠相关性疾病进行鉴别。其监测过程主要是，在检查时，医生在被检查者身上粘贴一些电极，并连接到仪器上，通过检测用户的口鼻气流、鼾声、胸腹式运动，判断是否有呼吸暂停或者通气不足的情况发生。此外，PSG还可以进一步获取呼吸暂停的最长时间和平均时间，从而区分中枢型、阻塞型或者混合型呼吸暂停。由于PSG进行用户的阻塞性睡眠暂停监测时，需要使用特定的PSG仪器，并且监测繁琐，进行检测的地点环境限制性太强，并且给用户造成较差的睡眠体验，难以实现在日常生活中的便捷监测。
2、利用家用睡眠呼吸暂停筛查仪监测用户睡眠。
针对PSG的复杂性及使用的不便捷性，目前还存在一种保留了PSG核心监测能力，但在结构上做了小型化的家用便携睡眠呼吸暂停筛查仪。该家用睡眠呼吸暂停筛查仪监测用户是否出现呼吸暂停的原理与PSG类似。由于其也需要用户佩戴，同样会造成用户体验较差，此外也可能无法达到用同一台仪器同时监测多人的效果。
3、血氧仪辅助筛查睡眠呼吸暂停。
由于用户发生阻塞性呼吸暂停时，会导致血氧下降，因此，可以通过对用户睡眠时的血氧进行监测，实现辅助筛查睡眠呼吸暂停综合征的目的。但是，在实际应用过程中，由于血氧下降可能是多种原因造成的，例如心衰，该血氧仪辅助监测的方法无法准确识别造成血氧下降的原因，因而，导致监测结果误差较大。
针对目前对用户阻塞性睡眠呼吸暂停综合征的监测方法中的不足，本申请实施例提供了一种不需用户佩戴，且能通过一台装置同时监测多人睡眠的方法。以下结合附图对本申请提供的睡眠监测方法进行介绍。
图1示出了本申请提供的呼吸暂停监测方法一种可能的应用场景。
首先，应理解，麦克风可以具有全向拾音响应，也即麦克风可以响应来自四面八方的声音。而多个麦克风组合成的多麦克风阵列则可以形成定向响应或者波束场型。经过设计，麦克风阵列可以对来自一个或多个特定方向的声音更加敏感，能够在该特定方向上采集更好的声音信号。
而本申请实施例提供的睡眠监测的方法，利用多麦克风阵列的波束形成技术采集位于 不同位置的用户的睡眠声音的音频信号，不需用户佩戴仪器即可实现同时对多个用户的睡眠过程进行监测，进而判断该多个用户是否存在阻塞性睡眠暂停或者阻塞性睡眠暂停的轻重程度。其中，睡眠声音例如可以是用户睡眠过程中的呼吸声、鼾声等。此外，多麦克风阵列可以是现有设备中已有的多麦克风阵列，也即可以利用配置有多麦克风阵列的现有终端设备对多用户进行睡眠声音的采集，该终端设备例如可以是手机或者录音笔等。
图2示出了本申请实施例提供的一种呼吸暂停监测的方法的示意性流程图。该方法包括以下步骤：
S210，采集至少一个用户的第一声音。
其中，可以采用配置有麦克风的第一终端对被监测用户的第一声音进行采集，其中，该第一终端例如可以是配置有单个麦克风或者多个麦克风阵列的手机或者其他录音设备等；第一声音为用户睡眠过程中的睡眠声音，例如可以是用户睡眠过程中的呼吸声、鼾声等。
应理解，本申请实施例提供的呼吸暂停监测的方法可以用于同时监测多个用户的睡眠呼吸状况，因此，当采集有多个用户的睡眠声音时，为实现后续分别对每个用户的睡眠呼吸暂停状况进行分析，需要识别不同用户所对应的睡眠声音。其中，本申请实施例主要采取两种方式区分不同用户的睡眠声音：其一，利用配置有多麦克风阵列的第一终端采集多个用户的第一声音，根据多麦克风阵列具有的指向性波束形成采集位于不同波束范围内的用户的第一声音，再根据第一声音的来源和用户位置确定用户与第一声音之间的对应关系；其二，通过第一终端采集多个用户的第一声音后，根据声纹识别算法，确定不同用户的第一声音。
作为一个示例，利用配置有多麦克风阵列的第一设备录制至少一个用户的第一声音。应理解，通过设定麦克风阵列的拾音参数，例如拾音方向、拾音夹角宽度等，结合第一设备和用户的位置，可以使用于处于多麦克风阵列的波束覆盖范围内。此外，通过对麦克风阵列单元的信号进行相位调整，使得各单元经过相位调整后的信号叠加，得到用户指定方向的主瓣信号，从而使得第一设备准确的采集位于不同位置的用户的第一声音。
应理解，当同时监测多个用户的睡眠呼吸过程时，由于不同用户分别位于多麦克风阵列不同波束的覆盖范围，因此，可以根据采集到的第一声音的来源所对应的波束覆盖范围确定第一声音与用户之间的对应关系。也即，当采集到多个用户的多个第一声音时，可以根据该多个用户所处的波束覆盖范围和该多个第一声音的来源所对应的波束，确定各个用户所对应的第一声音，以便于后续针对不同用户的第一声音分别分析其呼吸暂停情况。
可选地，配置有多麦克风阵列的第一设备可以是具有多麦克风阵列的手机、录音笔或者其他终端设备，本申请对此并不限定。
作为另一个示例，当通过第一设备采集用户的第一声音之后，通过声纹识别算法确定不同用户的第一声音。应理解，声纹(voiceprint)是用电声学仪器显示的携带言语信息的声波频谱。由于人在发声时发声器官在尺寸、形态方面差异很大，因此，任何两个人的声纹图谱均不相同，一般情况下，可以通过声纹信息区分不同的人或者判断采集到的声音是否属于同一个人的声音。本实施例通过声纹识别的方式确定用户对应的第一声音的过程例如可以为：在对被监测用户进行正式的睡眠呼吸监测过程中，采集各个用户的第一声音，获取不同用户声音的短时语音谱；根据短时语音谱提取声纹特征；通过声纹识别模型以及用户的声纹特征确定该第一声音对应的用户。其中，声纹识别模型可以现有的模型，例如 可以是高斯混合模型(Gaussian mixture model，GMM)、支持向量机(support vector machine，SVM)模型、信道模型、单位向量(identity vector，i-vector)模型等。
其中，以GMM模型为例，对声纹识别过程进行进一步介绍。
应理解，不同人的声音的差异主要体现在短时语音谱的差异，而短时语音谱的差异可以通过其具有的概率密度函数来衡量。GMM模型可以将空间分布的概率密度用多个高斯概率密度函数的加权和来拟合，拟合后的高斯概率密度函数可以平滑地逼近任意形状的概率密度函数，并且为一个易于处理的参数模型。在具体表示上，该参数模型可以是将GMM模型的每个高斯分量的均值向量排列在一起组成一个超向量作为某一个发声者的模型。
示例性的，本实施例涉及的声纹识别模型可以是基于梅尔频率倒谱系数(Mel frequency cepstrum coefficient，MFCC)的GMM模型。通过GMM模型进行声纹识别的过程可以是：(1)GMM模型训练。在GMM训练过程中，可以采集多个语音信号、对多个语音信号进行预处理后提取该语音信号的特征参数，其中，该语音特征参数例如可以是MFCC；以语音信号的特征参数为样本训练GMM模型。(2)声纹辨识过程：根据采集到的第一声音获取该第一声音的特征参数，并将该第一声音的特征参数与已建立的GMM模型进行比较，根据GMM的识别正确率确定该第一声音对应的用户。
应理解，本申请实施例中采用的声纹识别过程还可以参见现有的其他流程，而并不仅限于上文提到的声纹识别方式。
应理解，在实际利用录制用户的睡眠声音的过程中，不可避免的会有环境噪声，因此，为了在后续分析过程中能获取准确、良好的睡眠声音信号，需要将环境噪声消除。
示例性的，本申请实施例中对环境噪声消除或降低的过程可以是：在正式对用户的睡眠声音信号进行采集之前，先对用户所处环境的环境噪声进行采集，获得该环境噪声声波对应的相位、频率、振幅等，在后续对用户失眠监测的声音录制过程中，多麦克风产生一个与预先录制的环境噪声声波相位相反，频率和振幅相同的声波，使其与环境噪声干涉，以实现相位抵消，从而消除环境噪声；或者，根据预先录制的环境噪声的频率带宽，对滤波器的过滤频率带宽进行设置，使得在录制过程中，滤波器可以将换将噪声过滤。其中，过滤环境噪声的方式还可以是现有的其他方式，并申请对此并不限定。
应理解，通过麦克风录制用户睡眠过程中的第一声音，进而分析用户的睡眠呼吸暂停情况，使得用户在不需佩戴监测仪器的情况下，即可对睡眠呼吸暂停进行监测，提高了用户在监测过程中的舒适性。
步骤S220，根据第一声音获取该第一声音对应的功率频谱密度。
一般而言，通过麦克风获取的声音信号为时域信号，为了更加直观的分析用户的声音信号，将该时域信号转换为频域信号。具体地，将获取的不同用户的睡眠声音的时域信号通过傅里叶变换获得其对应的功率谱，也即功率谱密度。其中，傅里叶变换过程所采用的公式为：
Figure PCTCN2020092597-appb-000001
其中，ω为频率，t为时间，e -iωt为复变函数，f(t)可以为第一声音的时域信号，F(ω)可以为经傅里叶变换后的频域信号。
S230，当该功率谱密度低于第一阈值时，确定用户发生呼吸暂停。
其中，当用户睡眠声音的功率谱密度低于第一阈值时，确定该用户发生呼吸暂停。
应理解，第一阈值可以为根据用户的正常呼吸情况设置的值。示例性的，第一阈值的获得过程可以是：根据用户的正常呼吸状态(如刚入睡未发生呼吸暂停的呼吸状态)下的睡眠声音的时频信号进行时频变换，获得该状态下呼吸声的频谱，并对连续多次(如5至 10次)的呼吸频谱峰值取平均值，再取该平均值的30％至50％作为第一阈值，用于后续判断用户是否存在呼吸暂停以及呼吸暂停的轻重程度。此外，第一阈值可以根据临床测试数据，参考PSG标准精度，做优化调整，以达到最佳敏感性和特异性。
可选地，当用户睡眠声音的功率谱密度低于第一阈值的时长达到第二阈值时，确定该用户发生一次呼吸暂停，其中，第二阈值例如可以为10s。
可选地，根据不同用户的第一声音的功率频谱密度低于第一阈值的次数，确定该用户的呼吸紊乱(apnea-hypopnea index，AHI)指数。具体地，可以根据该第一声音的功率谱密度低于第一阈值的次数以及用户的睡眠时长，确定该用户的AHI指数。
可选地，在监测用户睡眠时，可以记录用户的睡眠时间，例如，该睡眠时间可以是用户一整晚的睡眠时长。
作为一个示例，根据用户呼吸音的功率谱密度和/或鼾声的功率谱密度以及用户的睡眠时间确定该用户的AHI指数。示例性的，当用户的呼吸音功率谱密度或者鼾声的功率谱密度低于第一阈值，且低于第一阈值的时长达到第二阈值时，记为一次呼吸暂停，在用户整个睡眠过程中，记录其发生呼吸暂停的总次数(记为k)，并根据其整个睡眠时长(记为t)计算睡眠呼吸紊乱系数，其中AHI＝k/t；或者，记录每单位时间内，用户睡眠声音的功率谱密度低于第一阈值，且低于第一阈值的时间长度达到第二阈值的次数。
其中，根据用户的AHI指数判断该用户发生呼吸暂停的轻重程度。示例性的，当AHI处于小于或者等于5的范围内时，判定该用户睡眠正常，也即不认为存在呼吸暂停情况；当AHI的值属于5至15的范围内时，认为该用户具有轻度的睡眠呼吸暂停状况；当AHI的值属于15至30的范围内时，认为该用户具有中度的睡眠呼吸暂停状况；当AHI处于大于或等于30的范围内时，认为该用户具有重度的睡眠呼吸暂停状况。
根据本申请提供的呼吸暂停监测的方法，通过利用多麦克风阵列的指向性多波束形成或者声纹识别区分不同用户的声音信息，能够同时监测多个用户的呼吸暂停状况，且用户不需佩戴仪器，不会对睡眠造成影响。
以下结合附图对本申请的呼吸暂停监测的方法进行具体介绍。
图3示出了本申请实施例提供的一种呼吸暂停监测的方法的示意性流程图。包括以下步骤：
S310，利用多麦克风阵列的指向性波束形成，采集处于不同位置的用户的第一声音。
其中，可以采用配置有麦克风的第一终端对被监测用户的第一声音进行采集，该第一终端例如可以是配置有单个麦克风或者多个麦克风阵列的手机或者其他录音设备等；第一声音为用户睡眠过程中的睡眠声音，例如可以是用户睡眠过程中的呼吸声、鼾声等。
其中，被监测的多个用户可以分别位于该多麦克风阵列的指向性波束的覆盖范围内。
可选地，在对用户睡眠呼吸进行监测之前，采用手动或者其他方式开启第一设备的睡眠呼吸暂停监测功能。
可选地，当第一终端采集多个用户的第一声音后，还可以通过声纹识别算法对不同用户的第一声音进行识别，以便更准确地区分不同用户的第一声音。
作为一个示例，利用配置有多麦克风阵列的第一设备录制至少一个用户的第一声音的过程可以包括：通过设定麦克风阵列的拾音参数，例如拾音方向、拾音夹角宽度等，结合第一设备和用户的位置，可以使用于处于多麦克风阵列的波束覆盖范围内；此外，通过对麦克风阵列单元的信号进行相位调整，使得各单元经过相位调整后的信号叠加，得到用户 指定方向的主瓣信号，从而使得第一设备准确的采集位于不同位置的用户的第一声音。
应理解，当同时监测多个用户的睡眠呼吸过程时，由于不同用户分别位于多麦克风阵列不同波束的覆盖范围，因此，可以根据采集到的第一声音的来源所对应的波束覆盖范围确定第一声音与用户之间的对应关系。也即，当采集到多个用户的多个第一声音时，可以根据该多个用户所处的波束覆盖范围和该多个第一声音的来源所对应的波束，确定各个用户所对应的第一声音，以便于后续针对不同用户的第一声音分别分析其呼吸暂停情况。
应理解，在实际利用录制用户的睡眠声音的过程中，不可避免的会有环境噪声，因此，为了在后续分析过程中能获取准确、良好的睡眠声音信号，需要将环境噪声消除或降低。
示例性的，本申请实施例中对环境噪声消除或降低的过程可以是：在正式对用户的睡眠声音信号进行采集之前，先对用户所处环境的环境噪声进行采集，获得该环境噪声声波对应的相位、频率、振幅等，在后续对用户失眠监测的声音录制过程中，多麦克风产生一个与预先录制的环境噪声声波相位相反，频率和振幅相同的声波，使其与环境噪声干涉，以实现相位抵消，从而消除环境噪声；或者，根据预先录制的环境噪声的频率带宽，对滤波器的过滤频率带宽进行设置，使得在录制过程中，滤波器可以将换将噪声过滤。其中，过滤环境噪声的方式还可以是现有的其他方式，并申请对此并不限定。
应理解，通过麦克风录制用户睡眠过程中的第一声音，进而分析用户的睡眠呼吸暂停情况，使得用户在不需佩戴监测仪器的情况下，即可对睡眠呼吸暂停进行监测，提高了用户在监测过程中的舒适性。
S320，根据用户的第一声音获取该第一声音的功率谱密度。
其中，多麦克风阵列可以根据采集的第一声音获取该第一声音的时域信号。
可选地，利用傅里叶变换可以将时域上的信号转换为频域上的功率谱密度。其中，具体转换过程可以参见步骤S220，为避免重复，此处不再赘述。
S330，当该功率谱密度低于第一阈值时，确定用户发生呼吸暂停。
其中，当用户睡眠声音的功率谱密度低于第一阈值时，确定该用户发生呼吸暂停。
应理解，第一阈值可以是根据用户的正常呼吸情况设置的值。示例性的，第一阈值的获得过程可以是：根据用户的正常呼吸状态(如刚入睡未发生呼吸暂停的呼吸状态)下的睡眠声音的时频信号进行时频变换，获得该状态下呼吸声的频谱，并对连续多次(如5至10次)的呼吸频谱峰值取平均值，再取该平均值的30％至50％作为第一阈值，用于后续判断用户是否存在呼吸暂停以及呼吸暂停的轻重程度。此外，第一阈值可以根据临床测试数据，参考PSG标准精度，做优化调整，以达到最佳敏感性和特异性。
可选地，当用户睡眠声音的功率谱密度低于第一阈值的时长达到第二阈值时，确定该用户发生一次呼吸暂停，其中，第二阈值例如可以为10s或者15s等。
可选地，根据不同用户的第一声音的功率频谱密度低于第一阈值的次数，确定该用户的呼吸紊乱AHI指数。
可选地，根据不同用户的第一声音的功率频谱密度，确定该用户的呼吸紊乱AHI指数。具体地，可以根据该第一声音的功率谱密度低于第一阈值的次数以及用户的睡眠时长，确定该用户的AHI指数。
可选地，在监测用户睡眠时，可以记录用户的睡眠时间，例如，该睡眠时间可以是用户一整晚的睡眠时长。
作为一个示例，当用户的呼吸音功率谱密度或者鼾声的功率谱密度低于第一阈值，且 低于第一阈值的时间长度达到第二阈值时，记为一次呼吸暂停，在用户整个睡眠过程中，记录其发生呼吸暂停的总次数(记为k)，并根据其整个睡眠时长(记为t)计算睡眠呼吸紊乱系数，其中AHI＝k/t；或者，记录每单位时间内，用户睡眠声音的功率谱密度低于第一阈值，且低于第一阈值的时间长度达到第二阈值的次数。
应理解，当获取用户的AHI指数后，可以根据该AHI指数判断用户的呼吸暂停程度。例如，当AHI处于小于或者等于5的范围内时，判定该用户睡眠正常，也即不认为存在呼吸暂停情况；当AHI的值属于5至15的范围内时，认为该用户具有轻度的睡眠呼吸暂停状况；当AHI的值属于15至30的范围内时，认为该用户具有中度的睡眠呼吸暂停状况；当AHI处于大于或等于30的范围内时，认为该用户具有重度的睡眠呼吸暂停状况。
根据本申请实施例提供的呼吸暂停的方法，通过多麦克风阵列采集用户的睡眠声音，能够实现非接触式地对多个用户进行睡眠呼吸暂停监测以及风险评估。
图4示出了本申请实施例提供的另一种呼吸暂停监测的方法的示意性流程图。包括以下步骤：
S410，通过第一终端采集多个用户的第一声音。
可选地，第一终端可以是手机、录音笔等，也可以是配置有单个或者多个麦克风的其他终端设备。
可选地，通过该第一设备中的麦克风录制用户的第一声音，其中，第一声音为用户睡眠过程中的呼吸音、鼾声等。
可选地，该第一终端中的麦克风可以根据采集到的第一声音获取该第一声音的时域信号。
应理解，在实际利用录制用户的睡眠声音的过程中，不可避免的会有环境噪声，因此，为了在后续分析过程中能获取准确、良好的睡眠声音信号，需要将环境噪声消除或降低。
示例性的，本申请实施例中对环境噪声消除或降低的过程可以是：在正式对用户的睡眠声音信号进行采集之前，先对用户所处环境的环境噪声进行采集，获得该环境噪声声波对应的相位、频率、振幅等，在后续对用户失眠监测的声音录制过程中，多麦克风产生一个与预先录制的环境噪声声波相位相反，频率和振幅相同的声波，使其与环境噪声干涉，以实现相位抵消，从而消除环境噪声；或者，根据预先录制的环境噪声的频率带宽，对滤波器的过滤频率带宽进行设置，使得在录制过程中，滤波器可以将换将噪声过滤。其中，过滤环境噪声的方式还可以是现有的其他方式，并申请对此并不限定。
应理解，通过麦克风录制用户睡眠过程中的第一声音，进而分析用户的睡眠呼吸暂停情况，使得用户在不需佩戴监测仪器的情况下，即可对睡眠呼吸暂停进行监测，提高了用户在监测过程中的舒适性。
S420，根据声纹识别算法确定用户对应的第一声音。
可选地，在采集用户睡眠过程中的第一声音之前，也即预处理阶段，预先采集用户未发生睡眠呼吸暂停时的呼吸音或者鼾声，例如，可以在用户入睡前期阶段，肌肉还未处于充分松弛状态，未出现睡眠呼吸暂停时，采集用户一段的呼吸音和/或鼾声。
可选地，提取用户第一声音的声音特征，并建立不同用户的声纹模型。
可选地，根据正式监测过程中获取的第一声音以及不同用户的声纹模型，确定不同用户的第一声音。
作为一个示例，当通过第一设备采集用户的第一声音之后，通过声纹识别算法确定不 同用户的第一声音。应理解，声纹(voiceprint)是用电声学仪器显示的携带言语信息的声波频谱。由于人在发声时发声器官在尺寸、形态方面差异很大，因此，任何两个人的声纹图谱均不相同，一般情况下，可以通过声纹信息区分不同的人或者判断采集到的声音是否属于同一个人的声音。本实施例通过声纹识别的方式确定用户对应的第一声音的过程例如可以为：在对被监测用户进行正式的睡眠呼吸监测之前，预先采集各个用户的第一声音，例如，呼吸声和/或鼾声，并提取不同用户的第一声音的特征，例如，获取不同用户声音的短时语音谱；根据不同用户的第一声音的特征建立的声纹识别模型；采集用户睡眠过程中的第一声音，并根据已建立的声纹识别模型确定不同用户的第一声音。
示例性的，建立用户声纹识别的模型的过程例如可以是：根据用户第一声音的特征，如短时语音谱获取其对应的概率密度函数，并建立高斯混合模型(Gaussian mixture model，GMM)将空间分布的概率密度用多个高斯概率密度函数的加权和来拟合，使得该高斯混合模型可以平滑地逼近任意形状的概率密度函数，得到易于处理的参数模型。具体地，根据短时语音谱的概率密度函数获得的参数模型将高斯混合模型的每个高斯分量的均值向量排列在一起组成一个超向量作为某一用户的模型。根据不同用户对应的特定的参数模型，可以确定不同用户对应的第一声音。其中，通过声纹识别建立不同用户参数模型的过程可以参见现有流程，此处不再赘述。
S430，根据第一声音获取该第一声音对应的功率谱密度。
可选地，根据麦克风获取的时域信号进行傅里叶变换，以获得不同用户的第一声音的功率谱密度。
S440，当该功率谱密度低于第一阈值时，确定用户发生呼吸暂停。
其中，当用户睡眠声音的功率谱密度低于第一阈值时，确定该用户发生呼吸暂停。
应理解，第一阈值可以是根据用户的正常呼吸情况设置的值。示例性的，第一阈值的获得过程可以是：根据用户的正常呼吸状态(如刚入睡未发生呼吸暂停的呼吸状态)下的睡眠声音的时频信号进行时频变换，获得该状态下呼吸声的频谱，并对连续多次(如5至10次)的呼吸频谱峰值取平均值，再取该平均值的30％至50％作为第一阈值，用于后续判断用户是否存在呼吸暂停以及呼吸暂停的轻重程度。此外，第一阈值可以根据临床测试数据，参考PSG标准精度，做优化调整，以达到最佳敏感性和特异性。
可选地，当用户睡眠声音的功率谱密度低于第一阈值的时长达到第二阈值时，确定该用户发生呼吸暂停，其中，第二阈值例如可以为10s或者15s等。
可选地，根据不同用户的第一声音的功率频谱密度低于第一阈值的次数，确定该用户的呼吸紊乱AHI指数。具体地，根据用户发生呼吸暂停的次数以及用户的睡眠时长，确定用户的AHI指数，该AHI指数可以直观地反映出用户睡眠呼吸暂停的轻重程度。
作为一个示例，当用户的呼吸音功率谱密度或者鼾声的功率谱密度低于第一阈值，且低于第一阈值的时间长度达到第二阈值时，记为一次呼吸暂停，在用户整个睡眠过程中，记录其发生呼吸暂停的总次数(记为k)，并根据其整个睡眠时长(记为t)计算睡眠呼吸紊乱系数，其中AHI＝k/t；或者，记录每单位时间内，用户睡眠声音的功率谱密度低于第一阈值，且低于第一阈值的时间长度达到第二阈值的次数。
其中根据AHI指数的值确定用户睡眠呼吸暂停状况的方式可以参见步骤S330，为避免重复，此处不再赘述。
根据本申请实施例提供的呼吸暂停的方法，通过麦克风采集不同用户的第一声音，并 利用声纹识别算法区分不同用户对应的睡眠声音，能够实现非接触式地对多个用户进行睡眠呼吸暂停监测以及风险评估。
图5示出了本申请实施例提供的一种呼吸暂停监测的方法的示意性流程图。
其中，本申请实施例的呼吸暂停监测方法还可以包括以下步骤。
S510，当确定用户发生呼吸暂停时，第一设备向第二设备发送唤醒消息。
其中，当确定用户发生呼吸暂停或者发生呼吸暂停达到一定时长时，对用户进行唤醒。
可选地，第二设备可以是用户周边的智能设备，例如，智能音箱、智能灯、智能穿戴设备、智能手机等，或者也可以是用户的紧急联系人智能设备，如智能手机等。
可选地，当监测到用户发生呼吸暂停的时长达到第三阈值时，第一设备向第二设备发送唤醒消息，其中，第三阈值例如可以是20s。
S520，第二设备通过震动、声音、灯光等方式，唤醒发生呼吸暂停的用户。
其中，当第二设备接收到第一设备的唤醒消息时，可以利用不同方式唤醒用户，例如，通过震动、声音、灯光等方式，唤醒发生呼吸暂停的用户。
示例性的，当通过第一设备监测到用户发生呼吸暂停的时长达到第三阈值时，第一设备可以向用户周边的联动设备，例如智能音箱、智能灯、智能穿戴设备、智能手机等发送唤醒消息，使得周边智能设备通过震动、声音、灯光等相应的方式及时唤醒用户。其中，第三阈值例如可以是20s或者参考医生建议设置的其他值，本申请对此并不限定。
图6示出了本申请实施例提供的一种睡眠呼吸暂停监测的装置。该装置600包括声音采集单元610和数据处理单元620。
其中，声音采集单元610，用于采集至少一个用户的第一声音。
可选地，数据处理单元620，用于根据所述第一声音获取所述第一声音的功率谱密度。
可选地，数据处理单元620，还可以用于根据所述功率频谱密度确定所述用户的AHI指数。
图7示出了本申请实施例提供的另一种睡眠呼吸暂停监测的装置。该装置700包括至少一个麦克风710处理器720。
可选地，麦克风710，用于采集至少一个用户的第一声音。
可选地，处理器720，用于根据所述第一声音获取所述第一声音的功率谱密度。
可选地，处理器720，还可以用于根据所述功率频谱密度确定所述用户的AHI指数。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的系统、设备和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在本申请所提供的几个实施例中，应该理解到，所揭露的系统、装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间 接耦合或通信连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。
所述功能如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(Read-Only Memory，ROM)、随机存取存储器(Random Access Memory，RAM)、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述，仅为本申请的具体实施方式，但本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本申请的保护范围之内。因此，本申请的保护范围应以所述权利要求的保护范围为准。

Claims (17)

  1. 一种呼吸暂停监测的方法，其特征在于，包括：
    通过第一终端的多麦克风阵列采集至少一个用户的第一声音，其中，每个所述用户位于所述多麦克风阵列的不同波束覆盖范围；
    根据所述第一声音获取所述第一声音对应的功率谱密度；
    当所述功率谱密度低于第一阈值时，确定所述用户发生呼吸暂停。
  2. 根据权利要求1所述的方法，其特征在于，所述通过第一终端的多麦克风阵列采集至少一个用户的第一声音，包括：
    根据所述第一终端和所述用户的位置，设置所述多麦克风阵列的拾音参数，使得所述多麦克风阵列的不同波束覆盖不同的所述用户；
    所述多麦克风阵列分别采集不同波束覆盖范围的所述用户的第一声音。
  3. 根据权利要求1或2所述的方法，其特征在于，所述根据所述第一声音获取所述第一声音对应的功率谱密度，包括：
    根据所述第一声音获取所述第一声音的时域信号；
    将所述时域信号通过傅里叶变换获取所述第一声音的功率频谱密度。
  4. 根据权利要求1-3中任一项所述的方法，其特征在于，所述方法还包括：
    预先采集所述用户所处环境的环境噪声，并确定所述环境噪声的频率带宽；
    根据所述环境噪声的频率带宽，过滤所述环境噪声。
  5. 根据权利要求1-4中任一项所述的方法，其特征在于，所述方法还包括：
    通过声纹识别算法，确定不同所述用户分别对应的第一声音。
  6. 根据权利要求1-5中任一项所述的方法，其特征在于，所述方法还包括：
    确定所述用户的睡眠时长；
    根据所述用户睡眠过程中发生所述呼吸暂停的次数和所述睡眠时长确定所述用户的呼吸紊乱AHI指数。
  7. 一种呼吸暂停监测的方法，其特征在于，包括：
    通过第一终端采集多个用户的第一声音；
    根据声纹识别算法确定不同所述用户分别对应的第一声音；
    根据所述第一声音获取所述第一声音对应的功率频谱密度；
    当所述功率谱密度低于第一阈值时，确定所述用户发生呼吸暂停。
  8. 根据权利要求7所述的方法，其特征在于，所述第一终端具有多麦克风阵列；
    所述通过第一终端采集多个用户的第一声音，包括：
    通过第一终端的多麦克风阵列采集多个用户的第一声音，其中，每个所述用户位于所述多麦克风阵列的不同波束覆盖范围。
  9. 根据权利要求8所述的方法，其特征在于，所述通过第一终端的多麦克风阵列采集多个用户的第一声音，包括：
    根据所述第一终端和所述用户的位置，设置所述多麦克风阵列的拾音参数，使得所述多麦克风阵列的不同波束覆盖不同的所述用户；
    所述多麦克风阵列分别采集不同波束覆盖范围的所述用户的第一声音。
  10. 根据权利要求7-9中任一项所述的方法，其特征在于，所述根据所述第一声音获取所述第一声音对应的功率频谱密度，包括：
    根据所述第一声音获取所述第一声音的时域信号；
    将所述时域信号通过傅里叶变换获取所述第一声音的功率频谱密度。
  11. 根据权利求7-10中任一项所述的方法，其特征在于，所述方法还包括：
    确定所述用户的睡眠时长；
    根据所述用户睡眠过程中发生所述呼吸暂停的次数和所述睡眠时长确定所述用户的AHI指数。
  12. 一种呼吸暂停监测的装置，其特征在于，包括：
    声音采集单元，用于采集至少一个用户的第一声音；
    数据处理单元，用于根据所述第一声音获取所述第一声音的功率谱密度；
    所述数据处理单元，还用于当所述功率谱密度低于第一阈值时，确定所述用户发生呼吸暂停。
  13. 根据权利要求12所述的装置，其特征在于，所述根据所述第一声音获取所述第一声音的功率谱密度，包括：
    根据所述第一声音获取所述第一声音的时域信号；
    将所述时域信号通过傅里叶变换获取所述第一声音的功率频谱密度。
  14. 根据权利要求12或13所述的装置，其特征在于，所述声音采集单元，还用于预先采集所述用户所处环境的环境噪声；
    所述数据处理单元，用于确定所述环境噪声的频率带宽，并根据所述环境噪声的频率带宽，过滤所述环境噪声。
  15. 根据权利要求12-14中任一项所述的装置，其特征在于，所述数据处理单元，还用于通过声纹识别算法，确定不同所述用户分别对应的第一声音。
  16. 根据权利要求12-15中任一项所述的装置，其特征在于，所述数据处理单元，还用于确定所述用户的睡眠时长，并根据所述用户睡眠过程中发生所述呼吸暂停的次数和所述睡眠时长确定所述用户的AHI指数。
  17. 一种呼吸暂停监测的装置，其特征在于，包括至少一个麦克风和处理器，其中，所述装置用于执行如权利要求1至11中任一项所述的方法。
PCT/CN2020/092597 2019-05-31 2020-05-27 呼吸暂停监测的方法及装置 WO2020238954A1 (zh)

Priority Applications (2)

Application Number Priority Date Filing Date Title
EP20812876.9A EP3954278A4 (en) 2019-05-31 2020-05-27 METHOD AND DEVICE FOR MONITORING APNEA
US17/615,387 US20220225930A1 (en) 2019-05-31 2020-05-27 Apnea monitoring method and apparatus

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201910471804.2 2019-05-31
CN201910471804.2A CN110301890B (zh) 2019-05-31 2019-05-31 呼吸暂停监测的方法及装置

Publications (1)

Publication Number Publication Date
WO2020238954A1 true WO2020238954A1 (zh) 2020-12-03

Family

ID=68075191

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2020/092597 WO2020238954A1 (zh) 2019-05-31 2020-05-27 呼吸暂停监测的方法及装置

Country Status (4)

Country Link
US (1) US20220225930A1 (zh)
EP (1) EP3954278A4 (zh)
CN (1) CN110301890B (zh)
WO (1) WO2020238954A1 (zh)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110301890B (zh) * 2019-05-31 2021-09-07 华为技术有限公司 呼吸暂停监测的方法及装置
JP7405660B2 (ja) * 2020-03-19 2023-12-26 Ｌｉｎｅヤフー株式会社 出力装置、出力方法及び出力プログラム
CN113440127B (zh) * 2020-03-25 2022-10-18 华为技术有限公司 呼吸数据的采集方法、装置和电子设备
CN111507010A (zh) * 2020-04-23 2020-08-07 苏州聚分享电子商贸有限公司 一种ai人工智能检测口气的数据模型
CN111795477A (zh) * 2020-07-16 2020-10-20 珠海格力电器股份有限公司 电器遥控器、睡眠质量监控系统及方法
CN115120197B (zh) * 2022-06-17 2024-07-02 歌尔股份有限公司 监测睡眠时呼吸状况的方法和装置、电子设备及存储介质

Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6142952A (en) * 1997-10-29 2000-11-07 The Board Of Regents, The University Of Texas System Method and apparatus for detection and diagnosis of airway obstruction degree
CN201639751U (zh) * 2010-03-11 2010-11-17 成都丹玛尼科技有限公司 基于多麦克风阵列的定向定距语音采集系统
WO2012042453A1 (en) * 2010-10-01 2012-04-05 Koninklijke Philips Electronics N.V. Apparatus and method for diagnosing obstructive sleep apnea
CN104378570A (zh) * 2014-09-28 2015-02-25 小米科技有限责任公司 录音方法及装置
CN105662417A (zh) * 2015-12-31 2016-06-15 沈阳迈思医疗科技有限公司 一种基于压力信号特征识别鼾声的控制方法及装置
CN106913335A (zh) * 2017-03-07 2017-07-04 南京工业职业技术学院 一种呼吸暂停检测系统的检测方法
CN108392186A (zh) * 2018-04-19 2018-08-14 广西欣歌拉科技有限公司 一种非接触式睡眠呼吸暂停症检测方法及系统
CN108419168A (zh) * 2018-01-19 2018-08-17 广东小天才科技有限公司 拾音设备的指向性拾音方法、装置、拾音设备及存储介质
CN108962272A (zh) * 2018-06-21 2018-12-07 湖南优浪语音科技有限公司 拾音方法和系统
CN109300475A (zh) * 2017-07-25 2019-02-01 中国电信股份有限公司 麦克风阵列拾音方法和装置
CN109805895A (zh) * 2019-02-18 2019-05-28 杭州电子科技大学 智能卧室睡眠监测系统
CN110301890A (zh) * 2019-05-31 2019-10-08 华为技术有限公司 呼吸暂停监测的方法及装置

Family Cites Families (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1067867A1 (en) * 1998-04-08 2001-01-17 Karmel Medical Acoustic Technologies Ltd. Determination of apnea type
US6882971B2 (en) * 2002-07-18 2005-04-19 General Instrument Corporation Method and apparatus for improving listener differentiation of talkers during a conference call
WO2012025892A2 (en) * 2010-08-26 2012-03-01 Ben Gurion University Of The Negev Research And Development Authority Apparatus and method for diagnosing obstructive sleep apnea
CN102429662B (zh) * 2011-11-10 2014-04-09 大连理工大学 家庭环境中睡眠呼吸暂停综合征的筛查系统
WO2013185041A1 (en) * 2012-06-07 2013-12-12 Clarkson Univeristy Portable monitoring device for breath detection
CN104853671B (zh) * 2012-12-17 2019-04-30 皇家飞利浦有限公司 使用非干扰性音频分析生成信息的睡眠呼吸暂停诊断系统
KR20170097519A (ko) * 2016-02-18 2017-08-28 삼성전자주식회사 음성 처리 방법 및 장치
CN105962894B (zh) * 2016-04-25 2018-10-23 东北大学 一种基于鼾声的睡眠打鼾时头部姿态实时识别装置及方法
CN106073722A (zh) * 2016-08-30 2016-11-09 孟玲 用于监测睡眠呼吸暂停综合症的健康分析系统
US20180206762A1 (en) * 2017-01-25 2018-07-26 Intel Corporation Sleep apnea therapy enhancement method and apparatus

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6142952A (en) * 1997-10-29 2000-11-07 The Board Of Regents, The University Of Texas System Method and apparatus for detection and diagnosis of airway obstruction degree
CN201639751U (zh) * 2010-03-11 2010-11-17 成都丹玛尼科技有限公司 基于多麦克风阵列的定向定距语音采集系统
WO2012042453A1 (en) * 2010-10-01 2012-04-05 Koninklijke Philips Electronics N.V. Apparatus and method for diagnosing obstructive sleep apnea
CN104378570A (zh) * 2014-09-28 2015-02-25 小米科技有限责任公司 录音方法及装置
CN105662417A (zh) * 2015-12-31 2016-06-15 沈阳迈思医疗科技有限公司 一种基于压力信号特征识别鼾声的控制方法及装置
CN106913335A (zh) * 2017-03-07 2017-07-04 南京工业职业技术学院 一种呼吸暂停检测系统的检测方法
CN109300475A (zh) * 2017-07-25 2019-02-01 中国电信股份有限公司 麦克风阵列拾音方法和装置
CN108419168A (zh) * 2018-01-19 2018-08-17 广东小天才科技有限公司 拾音设备的指向性拾音方法、装置、拾音设备及存储介质
CN108392186A (zh) * 2018-04-19 2018-08-14 广西欣歌拉科技有限公司 一种非接触式睡眠呼吸暂停症检测方法及系统
CN108962272A (zh) * 2018-06-21 2018-12-07 湖南优浪语音科技有限公司 拾音方法和系统
CN109805895A (zh) * 2019-02-18 2019-05-28 杭州电子科技大学 智能卧室睡眠监测系统
CN110301890A (zh) * 2019-05-31 2019-10-08 华为技术有限公司 呼吸暂停监测的方法及装置

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP3954278A4

Also Published As

Publication number Publication date
CN110301890B (zh) 2021-09-07
EP3954278A4 (en) 2022-06-15
US20220225930A1 (en) 2022-07-21
EP3954278A1 (en) 2022-02-16
CN110301890A (zh) 2019-10-08

Similar Documents

Publication Publication Date Title
WO2020238954A1 (zh) 呼吸暂停监测的方法及装置
US7559903B2 (en) Breathing sound analysis for detection of sleep apnea/popnea events
Jin et al. Adventitious sounds identification and extraction using temporal–spectral dominance-based features
Taplidou et al. Wheeze detection based on time-frequency analysis of breath sounds
CA2836196C (en) Breathing disorder identification, characterization and diagnosis methods, devices and systems
Matos et al. An automated system for 24-h monitoring of cough frequency: the leicester cough monitor
Almazaydeh et al. Apnea detection based on respiratory signal classification
US20200093423A1 (en) Estimation of sleep quality parameters from whole night audio analysis
US20120071741A1 (en) Sleep apnea monitoring and diagnosis based on pulse oximetery and tracheal sound signals
US20130144190A1 (en) Sleep apnea detection system
Patil et al. The physiological microphone (PMIC): A competitive alternative for speaker assessment in stress detection and speaker verification
US20120172676A1 (en) Integrated monitoring device arranged for recording and processing body sounds from multiple sensors
WO1998014116A2 (en) A phonopneumograph system
JP2013518607A (ja) 携帯型モニタリングのための生理学的信号の品質を分類する方法およびシステム
US10004452B2 (en) System and methods for estimating respiratory airflow
EP4153043A1 (en) Methods and apparatus for detection and monitoring of health parameters
CN107981844A (zh) 一种基于压电薄膜的鼾声识别方法及系统
Romero et al. Deep learning features for robust detection of acoustic events in sleep-disordered breathing
CN109712647A (zh) 一种皮肤采音设备和日常嗓音检测方法
Doheny et al. Estimation of respiratory rate and exhale duration using audio signals recorded by smartphone microphones
CN111613210A (zh) 一种各类呼吸暂停综合征的分类检测系统
CA2585824A1 (en) Breathing sound analysis for detection of sleep apnea/hypopnea events
KR20230026349A (ko) 인체 측정 정보와 기관 호흡음을 사용하여 각성 중 폐색성 수면 무호흡증 보유 여부를 스크리닝하는 시스템 및 방법
Xin et al. A bone-conduction transducer-based detection system for sleep apnea screening in the family units
Fang et al. Monitoring of Sleep Breathing States Based on Audio Sensor Utilizing Mel‐Scale Features in Home Healthcare

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20812876

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2020812876

Country of ref document: EP

Effective date: 20211108

NENP Non-entry into the national phase

Ref country code: DE