CN114176512A - Method for detecting obstructive sleep apnea through terminal APP - Google Patents

Method for detecting obstructive sleep apnea through terminal APP Download PDF

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Publication number
CN114176512A
CN114176512A CN202111320745.2A CN202111320745A CN114176512A CN 114176512 A CN114176512 A CN 114176512A CN 202111320745 A CN202111320745 A CN 202111320745A CN 114176512 A CN114176512 A CN 114176512A
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sleep apnea
snore
obstructive sleep
human body
terminal
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孙建国
韩芳
他得安
李旦
朱志斌
张晓民
彭智峰
齐菲
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Daobo Medical Technology (Suzhou) Co.,Ltd.
Daobo Medical Technology Beijing Co ltd
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    • 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
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1118—Determining activity level
    • 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
    • A61B5/4803—Speech analysis specially adapted for diagnostic purposes
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
    • G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
    • G10L25/66—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination for extracting parameters related to health condition

Abstract

A method for detecting obstructive sleep apnea through a terminal APP relates to the technical field of sleep disordered breathing detection, and comprises the steps of recording sound emitted in the sleeping process of a user by using the terminal APP, and judging whether the human body has symptoms of obstructive sleep apnea or not through snore and human body action detection.

Description

Method for detecting obstructive sleep apnea through terminal APP
Technical Field
The invention relates to the technical field of sleep disordered breathing detection, in particular to a method for detecting obstructive sleep apnea through a terminal APP.
Background
Sleep is the indispensable activity of life, and the sleep quality is not good can lead to daytime lassitude, increases the risk of taking place the accident. Such as obstructive sleep apnea, which greatly afflicts the young and the old, the sleep apnea can interrupt breathing repeatedly during sleep, and cardiovascular and cerebrovascular complications are very easy to cause. Obstructive Sleep Apnea (OSA) generally refers to a 7-hour sleep period for an adult, with 30 or more episodes per episode, with each episode having a cessation of oral and nasal airflow for 10 seconds or more, accompanied by a decrease in blood oxygen saturation, etc.
In recent years, with the progress of various testing methods, the OSA is intensively studied, and the disease is found to be complicated, variable and ubiquitous. Therefore, OSA cannot be generally regarded as a nuisance sound affecting rest of people in social interaction, but a clinical condition requiring careful examination, which can cause a number of serious complications. Hypomnesis, failure to concentrate attention, reduced working efficiency, and emotional and behavioral changes. The children may have the symptoms of intelligence reduction, learning achievement reduction, sleepwalking, nightmares and the like. Severely persistent patients may be complicated by hypertension, arrhythmia, exhaustion of heart and lung functions, etc.
Since it is difficult to capture onset symptoms of sleep-related diseases in a wake state, it is necessary to monitor the sleep condition of a patient all night and automatically locate abnormalities. Night-time monitoring and accurate assessment of sleep plays a crucial role in the diagnosis and treatment of sleep-related disorders, as well as in the analysis and prediction of psychological and behavioral manifestations. The physiological signals which can be measured by the traditional sleep monitoring multipurpose polysomnography equipment comprise electroencephalogram, electrocardiogram, electromyogram, electrooculogram, blood oxygen saturation, oral airflow, nasal airflow and the like, and are the gold standards for sleep monitoring in sleep medicine. The equipment needs the patient to fall asleep under the conditions of sticking electrode plates on the body and binding a plurality of sensors, so that the sleep of the patient is interfered; on the other hand, the device can only be used in hospitals and clinics, and needs special equipment and trained personnel to operate, so that the cost is high, and the device cannot be popularized as household equipment.
The design of a portable sleep information monitoring system is recorded by Beijing collaboration of Chinese medical academy of sciences and institute of biomedical engineering of medical academy, head-mounted hardware equipment for recording information such as pulse waves, heart rate, blood oxygen saturation, abnormal respiration times, sleeping postures and the like is designed, a terminal APP for starting and stopping operations of the hardware equipment, receiving reports and the like is designed, objective evaluation of the sleep quality, efficiency and the occurrence of disordered breathing of a patient at home is realized, physical sign data of the recorded sleep process can be referred by a doctor, and the condition of the patient can be diagnosed more accurately. However, although the design reduces the expertise level for matching with the patient to use by himself, the patient still needs to wear a mold during the whole sleeping process when the patient is used at home, the comfort level is reduced, and certain interference is caused to the sleeping.
The traditional method for detecting obstructive sleep apnea syndrome by the sleep monitoring multipurpose polysomnography is comprehensively judged through parameters such as electroencephalogram, electrocardio, myoelectricity, electrooculogram, blood oxygen saturation and the like, the diagnosis method indirectly judges whether symptoms of obstructive sleep apnea exist by knowing changes of organisms in a sleep period, and is complex in operation and low in efficiency. The traditional Chinese patent with publication number CN109350014A discloses a snore identification method and system, which detects snore segments by combining a double-threshold method combining zero crossing rate and short-time energy with an adaptive threshold method, and automatically identifies the snore by a convolutional neural network. A more effective method is provided for accurate detection of snore, the snore can be classified, the categories can be automatically identified, and diagnosis of respiratory diseases is assisted. Also, for example, chinese patent publication No. CN113288065A discloses a snore-based real-time apnea and hypopnea prediction method, which establishes a respiratory event prediction mechanism based on snore and other sleep real-time indicators, accurately performs early warning on the existence of a respiratory event after a decision point in real time, and designs an active intervention method in combination with a characteristic importance principle and the like. The two prior art methods also need to be matched with corresponding devices for operation, one of the two methods focuses on snore identification and classification, the other focuses on predicting subsequent respiratory events based on snore, the related operations are complex and difficult to be suitable for home use, and common users are difficult to operate easily and accurately, so that the methods have a place to be improved.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a method for detecting obstructive sleep apnea through a terminal APP, which comprises the following specific steps:
a method for detecting obstructive sleep apnea through a terminal APP includes utilizing the terminal APP to record sound emitted in a sleeping process of a user, and judging whether a human body has symptoms of obstructive sleep apnea or not through snore and human body action detection.
Further, the method comprises the following steps:
step 1: before sleeping, the terminal is placed near a human body, and the corresponding APP is started;
step 2: the APP continuously transmits ultrasonic waves with fixed central frequency through a loudspeaker;
and step 3: the APP receives an echo signal of ultrasonic waves and a snore signal through a microphone;
and 4, step 4: all the signal data acquired in the step 3 are sent to a server-side program;
and 5: and the server-side program processes and analyzes the signal data and judges whether symptoms of obstructive sleep apnea exist and the duration of each apnea.
Further, the step 5 specifically includes the following steps:
step 5.1: the server-side program carries out normalization processing on the snore signals to carry out signal reconstruction, so that a snore signal curve is formed, and the snore signal curve obtains corresponding energy values which can contain the audio intensity and the duration based on a normalization energy value algorithm;
step 5.2: judging that the energy waveform with the energy value larger than the calibration line and the duration time larger than 0.5 second and smaller than 3 seconds is a snore waveform;
step 5.3: judging whether the interval between every two snore waveforms is more than 10 seconds and less than 90 seconds is sleep apnea time;
step 5.4: by detecting the change of the central frequency of an echo signal of ultrasonic waves, judging the relative motion between a human body and a terminal according to the Doppler effect and the energy loss theorem of reflected signals, thereby detecting different human body actions;
step 5.5: counting the number of sleep apnea times, the total duration time of each sleep apnea time, and the total number of relative movement times of the human body and the terminal;
step 5.6: and 5.5, calculating the data obtained by statistics in the step 5.5 in a weighting manner, and judging whether the user has symptoms of obstructive sleep apnea according to the final score.
Furthermore, the human body actions comprise the movement of keeping hands away, the movement of keeping hands close, the rapid turning-over of the human body and the slow turning-over of the human body.
Further, the normalized energy value algorithm in the step 5.1 corresponds to the following formula:
Figure BDA0003345521650000041
Figure BDA0003345521650000042
wherein n represents time, AiRepresenting the ith data, C, on the curve of the snore signaldBIs an energy constant;
Ejis a single sampleEnergy value at a point, AijIs the ith data on the jth number on the reconstructed signal coefficients.
Further, in the step 1, the distance between the terminal and the human body is 30-50 cm.
Further, in the step 2, the frequency of the ultrasonic wave is 12-16 KHz.
Compared with the prior art, the invention has the following beneficial effects:
(1) through adopting the terminal for example the cell-phone to the form that cell-phone APP detected has solved traditional medical auxiliary equipment and has worn inconvenient problem, need not to adopt head-mounted or other direct wearing methods to cause the interference to user's sleep, and terminals such as cell-phones are the equipment that modern people used commonly, anytime and anywhere alright download APP, and the user can anytime and anywhere, easily use APP when being in the bedroom and reach the effect that detects the sleep.
In addition, snore is a basic judgment element in obstructive sleep apnea syndrome, and people suffering from obstructive sleep apnea have light sleep and poor quality, and are difficult to keep a static state for a long time in the sleep process, so that human body actions are auxiliary judgment factors in the obstructive sleep apnea syndrome.
Drawings
FIG. 1 is a diagram illustrating the effect of an original snore signal in the present invention;
FIG. 2 is a diagram showing the effect of an energy value signal of an original snore signal after being processed by a normalized energy value algorithm;
FIG. 3 is a diagram showing the effect of identification of obstructive sleep apnea;
FIG. 4 is a diagram showing the effect of hand movement detection during sleep;
FIG. 5 is a diagram showing the effect of rapid turn-over detection during sleep;
fig. 6 is a diagram showing the effect of slow turn-over detection during sleep.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited to these examples.
As shown in fig. 1, a method for detecting obstructive sleep apnea through a terminal APP is adapted to a large crowd monitoring sleep of the large crowd and others in cooperation with a portable terminal, so as to determine whether a human body has a potential obstructive sleep apnea syndrome.
In the existing various sleep APPs, such as snail sleep and dolphin sleep, the sleep APPs generally only have the basic functions of assisting sleep, recording sleep time length, making a sleep plan and the like, the function is single, in addition, the sleep APP of a snore rabbit is provided, the software records the snore situation of each sleep, the number of times of snore in one night is accurately counted, the time length and the decibel are calculated, meanwhile, a snore stopping setting option in the APPs can be opened, and the snore rabbit can slightly help to stop snore through vibration after the snore is detected. In addition, the user can judge whether the OSA is suspected to be the obstructive sleep apnea by data in software every other day, although the software has a certain OSA screening function, the judgment needs to be subjective, and the judgment is only carried out according to the single element of the snore, so that the detection accuracy of the obstructive sleep apnea is low.
The method further discloses that the sound emitted during the sleeping process of the user is recorded by utilizing the terminal APP, and whether the human body has the symptom of obstructive sleep apnea or not is judged by snore and human body action detection. In detail, snore is a basic judgment element in obstructive sleep apnea syndrome, and people suffering from obstructive sleep apnea have light sleep and poor quality, and are difficult to keep a static state for a long time in the sleep process, so that human body actions are auxiliary judgment factors in the obstructive sleep apnea syndrome.
Therefore, the method of the invention specifically comprises the following steps:
step 1: before sleeping, the terminal is placed near a human body, and the corresponding APP is started;
step 2: the APP continuously transmits ultrasonic waves with fixed central frequency through a loudspeaker;
and step 3: the APP receives an echo signal of ultrasonic waves and a snore signal through a microphone;
and 4, step 4: all the signal data acquired in the step (3) are sent to a server-side program;
and 5: and the server-side program processes and analyzes the signal data, and judges whether symptoms of obstructive sleep apnea exist and the duration of each apnea.
In the above content, the terminal may adopt a handheld device such as an existing mobile phone and a tablet, so that a user can place the terminal nearby when sleeping, for example, the mobile phone belongs to an intelligent terminal, and a speaker and a microphone are built in the mobile phone, so as to have the functions of broadcasting and receiving sound. In the step 1, the distance between the terminal and the human body is 30-50cm, and within the distance range, sound waves of snore can be accurately received by the mobile phone after being transmitted in the air. In the step 2, the frequency of the ultrasonic wave is 12-16KHz, the ultrasonic wave is optimized, 16KHz can be adopted, and the 16KHz belongs to high frequency, so that most of the frequency can be heard by the ears of adults without influencing normal life and harming body health.
During the sleep of a user, the terminal is continuously in contactless interactive cooperation with the human body, the sleep of the user is not required to be interfered by head wearing or other direct wearing modes, snore and actions generated during the sleep of the human body are collected in the mode of transmitting and receiving ultrasonic waves by the terminal, and then the snore and the actions are processed and judged by an artificial intelligence algorithm. Therefore, step 5 specifically includes the following steps:
step 5.1: the server-side program carries out normalization processing on the snore signals to carry out signal reconstruction, so that a snore signal curve is formed, and the snore signal curve obtains corresponding energy values which can contain the audio intensity and the duration based on a normalization energy value algorithm;
step 5.2: judging that the energy waveform with the energy value larger than the calibration line and the duration time larger than 0.5 second and smaller than 3 seconds is a snore waveform;
step 5.3: judging whether the interval between every two snore waveforms is more than 10 seconds and less than 90 seconds is sleep apnea time;
step 5.4: by detecting the change of the central frequency of an echo signal of ultrasonic waves and judging the relative motion between a human body and a terminal according to the Doppler effect and the energy loss theorem of reflected signals, different human body actions are detected, wherein the human body actions comprise hand far-away movement, hand near movement, rapid human body turning-over and slow human body turning-over;
step 5.5: counting the number of sleep apnea times, the total duration time of each sleep apnea time, and the total number of relative movement times of the human body and the terminal;
step 5.6: and 5.5, calculating the data obtained by the step 5.5 in a weighting manner, and judging whether the user has symptoms of obstructive sleep apnea according to the final score.
In this embodiment, the normalized energy value algorithm in step 5.1 has the following formula:
Figure BDA0003345521650000081
Figure BDA0003345521650000082
wherein n represents time, AiRepresenting the ith data, C, on the curve of the snore signaldBIs an energy constant;
Ejis the amount of energy at a single sampling point, AijIs the ith data on the jth number on the reconstructed signal coefficients.
For example, fig. 1 shows the original snore signal, fig. 2 shows the energy value signal processed by the step 5.1 normalization energy value algorithm,as can be seen from FIG. 2, this is a sliding window algorithm, a window of fixed size is set on the signal curve graph, which tends to slide continuously from left to right according to a fixed step length, j is the serial number of the sliding window, each sliding window represents a sampling point, and each point on the signal curve in the graph is AiThe abscissa represents the total time n, and the energy constant C can be calculated from the above first summation formuladB。AijCalculating E for the ith data in the jth sliding window in the graph according to the second summation formulaj，EjThe energy value of a certain sliding window is convenient to compare with the calibration line, snore waveforms exist in the sliding window and after the comparison of the sliding window and the calibration line, a plurality of snore waveforms exist in the figure 2, the shapes of different snore waveforms are different, and the number of times of sleep apnea can be obtained.
Referring to fig. 3 for the identification of obstructive sleep apnea, the sleep apnea time can be calculated from the corresponding time point below the waveform.
In this embodiment, the doppler effect and the energy loss of the reflected signal in step 5.4 are illustrated, for example, fig. 4 shows doppler detection and hand movement energy detection of hand movement during sleep of a human body, two times of hand movements can be clearly seen from the figure, for example, fig. 5 shows doppler detection and fast turn-over energy detection of fast turn-over during sleep of a human body, for example, fig. 6 shows doppler detection and slow turn-over energy detection of slow turn-over during sleep of a human body, and for example, it can clearly show slow turn-over of multiple times from the figure. Therefore, the accuracy of human motion detection can be improved by the Doppler effect and the energy loss theorem of the reflected signal. Furthermore, the snore detection and human body action detection combined method is adopted to comprehensively judge whether the user has potential obstructive sleep apnea, so that the detection accuracy of the obstructive sleep apnea in the sleep process is better improved, and the higher identification rate of the obstructive sleep apnea is achieved.
Optimally, the terminal APP in the invention can realize snore detection and human body action detection, and can also be used for identifying whether the human body has the problem of mandible retraction, the prior art can know that the reasons for causing obstructive sleep apnea are many, the most common factor related to the oral cavity maxillofacial area is mandible retraction, and the APP can also be comprehensively used for judging whether the obstructive sleep apnea exists.
The terminal APP provides a face photographing function, when a human body enters a sleep stage, a side face of the human body is photographed, after a server program obtains a face picture, the artificial intelligence deep learning technology is combined to recognize coordinates of each main characteristic point of the face, including a nose tip point, a lip point and a mandible point, the server program connects the nose tip point and the lip point through a straight line, the server program automatically judges whether the nose tip point, the lip point and the mandible point are on the same straight line, if the mandible point is behind the straight line, the mandible retraction is judged, and the mandible retraction is counted in a weight calculation formula for judging apnea risk factors. In summary, the invention comprehensively detects whether the human body has obstructive sleep apnea from the reasons of the obstructive sleep apnea syndrome and the different modes, and not only judges whether the obstructive sleep apnea exists, but also judges whether the potential risk of the obstructive sleep apnea exists.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may occur to those skilled in the art without departing from the principle of the invention, and are considered to be within the scope of the invention.

Claims (7)

1. A method for detecting obstructive sleep apnea through a terminal APP is characterized in that the method includes the steps that the terminal APP is used for recording sound emitted in the sleeping process of a user, and whether the human body has symptoms of the obstructive sleep apnea or not is judged through snore and human body action detection.
2. Method for the detection of obstructive sleep apnea by a terminal APP according to claim 1, characterized in that it comprises the following steps:
step 1: before sleeping, the terminal is placed near a human body, and the corresponding APP is started;
step 2: the APP continuously transmits ultrasonic waves with fixed central frequency through a loudspeaker;
and step 3: the APP receives an echo signal of ultrasonic waves and a snore signal through a microphone;
and 4, step 4: all the signal data acquired in the step 3 are sent to a server-side program;
and 5: and the server-side program processes and analyzes the signal data and judges whether symptoms of obstructive sleep apnea exist and the duration of each apnea.
3. The method for detecting Obstructive Sleep Apnea (OSA) by terminal APP as recited in claim 2, wherein said step 5 specifically comprises the following steps:
step 5.1: the server-side program carries out normalization processing on the snore signals to carry out signal reconstruction, so that a snore signal curve is formed, and the snore signal curve obtains corresponding energy values which can contain the audio intensity and the duration based on a normalization energy value algorithm;
step 5.2: judging that the energy waveform with the energy value larger than the calibration line and the duration time larger than 0.5 second and smaller than 3 seconds is a snore waveform;
step 5.3: judging whether the interval between every two snore waveforms is more than 10 seconds and less than 90 seconds is sleep apnea time;
step 5.4: by detecting the change of the central frequency of an echo signal of ultrasonic waves, judging the relative motion between a human body and a terminal according to the Doppler effect and the energy loss theorem of reflected signals, thereby detecting different human body actions;
step 5.5: counting the number of sleep apnea times, the total duration time of each sleep apnea time, and the total number of relative movement times of the human body and the terminal;
step 5.6: and 5.5, calculating the data obtained by statistics in the step 5.5 in a weighting manner, and judging whether the user has symptoms of obstructive sleep apnea according to the final score.
4. The method for detecting obstructive sleep apnea, via a terminal APP, of claim 3, characterized in that the body actions include hand-away movement, hand-close movement, rapid body turn, slow body turn.
5. Method for the detection of obstructive sleep apnea by terminal APP according to claim 3, characterized in that the normalized energy value algorithm in step 5.1 corresponds to the following formula:
Figure FDA0003345521640000021
Figure FDA0003345521640000022
wherein n represents time, AiRepresenting the ith data, C, on the curve of the snore signaldBIs an energy constant;
Ejis the amount of energy at a single sampling point, AijIs the ith data on the jth number on the reconstructed signal coefficients.
6. The method for detecting Obstructive Sleep Apnea (OSA) by terminal APP according to claim 2, is characterized in that, in step 1, the distance between the terminal and the human body is 30-50 cm.
7. Method for the detection of obstructive sleep apnea by a terminal APP according to claim 2, characterized in that in said step 2, the frequency of said ultrasound is 12-16 KHz.
CN202111320745.2A 2021-11-09 2021-11-09 Method for detecting obstructive sleep apnea through terminal APP Pending CN114176512A (en)

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