A sleep monitoring system, a sleep monitoring method, an electronic device, and a storage medium

Abstract

The application provides a sleep monitoring system, a sleep monitoring method, an electronic device and a storage medium. A data acquisition module is configured to acquire initial body data of a monitored user in real time. When it is detected that the monitored user correctly wears the device based on the initial body data, the sleep body data of the monitored user is acquired in real time, and the sleep body data is transmitted to a data analysis module in real time. The data analysis module is configured to receive the sleep body data transmitted by the data acquisition module, and perform sleep monitoring on the monitored user based on the sleep body data to obtain a sleep analysis report of the monitored user. According to the system and method, the accuracy of sleep monitoring of the monitored user is improved, the data of the sleep monitoring user is efficiently and uniformly managed, the physiological signal interpretation algorithm is optimized, and the encryption of sleep data transmission and storage is ensured. Moreover, the sleep data acquisition and storage integrity, rigor, diagnosis efficiency and diagnosis accuracy are improved.

Description

Technical Field

[0001] This application relates to the field of sleep monitoring technology, and in particular to a sleep monitoring system, sleep monitoring method, electronic device and storage medium. Background Technology

[0002] In recent years, the incidence of sleep disorders has been rising year by year, and sleep medicine, as an emerging discipline, has developed rapidly. Medical institutions are also gradually paying more attention to the diagnosis and treatment of sleep disorders. Sleep monitoring devices can record physiological signals and audio-visual data during sleep. They are the only instruments that can objectively and scientifically record and analyze sleep, allowing for understanding of sleep structure and efficiency, and determining whether there are conditions such as hypoventilation or apnea.

[0003] Traditional sleep monitors are complex to operate. When using sleep monitors at home, detailed sleep information is often recorded on paper. Doctors then need to repeatedly review the user's sleep data throughout the night to identify key indicators for analysis. This method is time-consuming, requires a high level of expertise, and has a relatively low accuracy rate. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a sleep monitoring system, sleep monitoring method, electronic device, and storage medium. Sleep body data is only sent to the data analysis module when the monitored user wears the device correctly, thus improving the accuracy of sleep monitoring for the monitored user in the subsequent data analysis module. Furthermore, the data analysis module tracks the entire sleep monitoring process, enabling unified and efficient management of sleep monitoring user data, optimizing physiological signal interpretation algorithms, and ensuring the encryption of sleep data transmission and storage. It also improves the integrity, security, diagnostic efficiency, and diagnostic accuracy of sleep data collection and storage.

[0005] In a first aspect, embodiments of this application provide a sleep monitoring system, which includes multiple data acquisition modules and a data analysis module, wherein each data acquisition module is communicatively connected to the data analysis module.

[0006] The data acquisition module is used to guide the monitored user to wear the device by playing guidance images or videos, and to collect the initial body data of the monitored user in real time. When the monitored user is detected to be wearing the device correctly based on the initial body data, the module collects the monitored user's sleep body data in real time and transmits the sleep body data to the data analysis module in real time.

[0007] The data analysis module is used to receive sleep body data sent by multiple data acquisition modules, and to perform sleep monitoring on the monitored user wearing the data acquisition module based on the sleep body data sent by each data acquisition module, so as to obtain sleep analysis reports of multiple monitored users.

[0008] Furthermore, the data acquisition module includes a headband data acquisition unit, a chest and abdomen data acquisition unit, a lower limb data acquisition unit, a signal analysis unit, and a data transmission unit;

[0009] The headband data acquisition unit is used to acquire the initial headband signal of the monitored user and send the initial headband signal to the signal analysis unit;

[0010] The chest and abdomen data acquisition unit is used to acquire the initial chest and abdomen signals of the monitored user and send the initial chest and abdomen signals to the signal analysis unit.

[0011] The lower limb data acquisition unit is used to acquire the initial lower limb electromyographic signals of the monitored user and send the initial lower limb electromyographic signals to the signal analysis unit;

[0012] The signal analysis unit is configured to generate a sleep data acquisition signal and send it to the headband data acquisition unit, chest and abdomen data acquisition unit, and lower limb data acquisition unit when it is detected that the monitored user is correctly wearing the headband data acquisition unit, chest and abdomen data acquisition unit, and lower limb data acquisition unit based on the initial headband signal, the initial chest and abdomen signal, and the initial lower limb electromyography signal.

[0013] The headband data acquisition unit is also used to send the sleep headband signal to the data transmission unit;

[0014] The chest and abdomen data acquisition unit is also used to send sleep chest and abdomen signals to the data transmission unit;

[0015] The lower limb data acquisition unit is also used to send the sleep lower limb electromyography signal to the data transmission unit;

[0016] The data transmission unit is used to send the sleep headband signal, the sleep chest and abdomen signal, and the sleep lower limb electromyography signal to the data analysis module.

[0017] Furthermore, the initial headband signal includes an impedance detection headband signal and a calibration detection headband signal. The impedance detection headband signal includes electroencephalogram (EEG) signals, electrooculogram (EOG) signals, and mandibular electromyography (EMG) signals. The calibration detection headband signal includes EOG signals, mandibular EMG signals, and audio signals. The initial chest and abdomen signal includes an impedance detection chest and abdomen signal and a calibration detection chest and abdomen signal. The impedance detection chest and abdomen signal includes an electrocardiogram (ECG) signal. The calibration detection chest and abdomen signal includes nasal airflow signals, thermal signals, and chest and abdomen band signals.

[0018] The signal analysis unit is used to detect whether the monitored user is correctly wearing the headband data acquisition unit, the chest and abdomen data acquisition unit, and the lower limb data acquisition unit through the following steps:

[0019] Based on a preset impedance signal threshold, the impedance values ​​corresponding to the impedance detection headband signal, the impedance detection chest and abdomen signal, and the initial lower limb electromyography signal are determined.

[0020] When the impedance value is determined to meet the signal impedance threshold, the calibration action voice is played, and the calibration detection headband signal, the calibration detection chest and abdomen signal, and the initial lower limb electromyography signal generated by the monitored user when performing the corresponding calibration specified action based on the calibration action voice are acquired. When the calibration detection headband signal, the calibration detection chest and abdomen signal, and the initial lower limb electromyography signal are determined to meet the calibration signal threshold, it is determined that the monitored user is correctly wearing the headband data acquisition unit, the chest and abdomen data acquisition unit, and the lower limb data acquisition unit.

[0021] Furthermore, after determining the impedance values ​​corresponding to the impedance detection headband signal, the impedance detection chest and abdomen signal, and the initial lower limb electromyography signal, the signal analysis unit is further configured to:

[0022] The impedance values ​​corresponding to the impedance detection headband signal, the impedance detection chest and abdomen signal, and the initial lower limb electromyography signal are sent to the data transmission unit, so that the data transmission unit sends the impedance values ​​to the data analysis module.

[0023] Furthermore, when the data analysis module is used to perform sleep monitoring on the monitored user based on the sleep body data to obtain a sleep analysis report for the monitored user, the data analysis module is specifically used for:

[0024] Feature extraction is performed on the sleep chest and abdomen signals and the sleep headband signals to determine the sleep events of the monitored user;

[0025] Based on the sleep headband signal, the sleep chest and abdomen signal, and the sleep lower limb electromyography signal, a second calculation is performed on the basis of the sleep events to determine the complex linkage events of the monitored user.

[0026] Based on the sleep events and the complex linkage events, a variety of sleep signal index data are extracted; among them, the sleep signal index data includes the highest heart rate during wakefulness, the lowest heart rate during wakefulness, the lowest blood oxygen saturation during wakefulness, the highest heart rate during sleep, the lowest heart rate during sleep, the lowest blood oxygen saturation during sleep, and the longest breathing time during sleep.

[0027] The sleep analysis report is generated based on the sleep signal index data.

[0028] Furthermore, after extracting multiple sleep signal indicators based on the sleep events and the complex linkage events, the data analysis module is also used for:

[0029] In response to the user's selection of any sleep signal indicator data, the target sleep signal indicator data is determined, and all data results corresponding to the target sleep signal indicator data are displayed on the display interface.

[0030] Secondly, embodiments of this application also provide a sleep monitoring method, which is applied to a sleep monitoring system, and the sleep monitoring method includes:

[0031] For each data acquisition module, the data acquisition module guides the monitored user to wear the device by playing guidance images or videos, and collects the monitored user's initial body data in real time. When the monitored user is detected to be wearing the device correctly based on the initial body data, the module collects the monitored user's sleep body data in real time and transmits the sleep body data to the data analysis module in real time.

[0032] The data analysis module receives sleep body data sent by multiple data acquisition modules, and performs sleep monitoring on the monitored user wearing the data acquisition module based on the sleep body data sent by each data acquisition module, so as to obtain sleep analysis reports of multiple monitored users.

[0033] Furthermore, the data acquisition module includes a headband data acquisition unit, a chest and abdomen data acquisition unit, a lower limb data acquisition unit, a signal analysis unit, and a data transmission unit; the sleep monitoring method further includes:

[0034] The headband data acquisition unit acquires the initial headband signal of the monitored user and sends the initial headband signal to the signal analysis unit;

[0035] The chest and abdomen data acquisition unit acquires the initial chest and abdomen signals of the monitored user and sends the initial chest and abdomen signals to the signal analysis unit;

[0036] The lower limb data acquisition unit acquires the initial lower limb electromyographic signals of the monitored user and sends the initial lower limb electromyographic signals to the signal analysis unit;

[0037] When the signal analysis unit detects that the monitored user is correctly wearing the headband data acquisition unit, the chest and abdomen data acquisition unit, and the lower limb data acquisition unit based on the initial headband signal, the initial chest and abdomen signal, and the initial lower limb electromyography signal, it generates a sleep data acquisition signal and sends it to the headband data acquisition unit, the chest and abdomen data acquisition unit, and the lower limb data acquisition unit.

[0038] The headband data acquisition unit sends the sleep headband signal to the data transmission unit during sleep.

[0039] The chest and abdomen data acquisition unit sends sleep chest and abdomen signals to the data transmission unit;

[0040] The lower limb data acquisition unit sends the sleep lower limb electromyography signal to the data transmission unit;

[0041] The data transmission unit is used to send the sleep headband signal, the sleep chest and abdomen signal, and the sleep lower limb electromyography signal to the data analysis module.

[0042] Thirdly, embodiments of this application also provide an electronic device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, the steps of the sleep monitoring method described above are performed.

[0043] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the sleep monitoring method described above.

[0044] This application provides a sleep monitoring system, sleep monitoring method, electronic device, and storage medium. The sleep monitoring system includes multiple data acquisition modules and a data analysis module, with each data acquisition module communicatively connected to the data analysis module. The data acquisition module guides the monitored user to wear the device by playing instructional images or videos, and collects the user's initial body data in real time. When the user is detected to be wearing the device correctly based on the initial body data, the module collects the user's sleep body data in real time and transmits the sleep body data to the data analysis module in real time. The data analysis module receives the sleep body data sent by the multiple data acquisition modules and performs sleep monitoring on the monitored user wearing the data acquisition module based on the sleep body data sent by each data acquisition module, to obtain multiple sleep analysis reports for the monitored user.

[0045] This system ensures that sleep data is only sent to the data analysis module when the user wears the device correctly, improving the accuracy of subsequent sleep monitoring. Furthermore, the data analysis module tracks the entire sleep monitoring process, enabling unified and efficient management of user data, optimizing physiological signal interpretation algorithms, and guaranteeing the encryption of sleep data transmission and storage. This also improves the integrity, security, diagnostic efficiency, and accuracy of sleep data collection and storage. It adapts to the future needs of polysomnography development, providing a convenient and efficient monitoring system for the continuously growing demand for sleep monitoring and diagnosis.

[0046] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0047] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a schematic diagram of the structure of a sleep monitoring system provided in an embodiment of this application;

[0049] Figure 2 This is a schematic diagram of the structure of a data acquisition module provided in an embodiment of this application;

[0050] Figure 3 A flowchart illustrating a sleep monitoring method provided in an embodiment of this application;

[0051] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. Based on the embodiments of this application, every other embodiment obtained by those skilled in the art without inventive effort falls within the scope of protection of this application.

[0053] First, the applicable scenarios for this application will be introduced. This application can be applied to the field of sleep monitoring technology.

[0054] In recent years, the incidence of sleep disorders has been rising year by year, and sleep medicine, as an emerging discipline, has developed rapidly. Medical institutions are also gradually paying more attention to the diagnosis and treatment of sleep disorders. Sleep monitoring devices can record physiological signals and audio-visual data during sleep. They are the only instruments that can objectively and scientifically record and analyze sleep, allowing for understanding of sleep structure and efficiency, and determining whether there are conditions such as hypoventilation or apnea.

[0055] Research has found that traditional sleep monitors are complex to operate. When using sleep monitors at home, detailed sleep information is often recorded on paper. Doctors then need to repeatedly review the user's sleep data throughout the night to identify key indicators for analysis. This method is time-consuming, requires a high level of expertise, and has a low accuracy rate.

[0056] Based on this, the embodiments of this application provide a sleep monitoring system that improves the integrity, rigor, diagnostic efficiency, and diagnostic accuracy of sleep data collection and storage.

[0057] Please see Figure 1 , Figure 1 This is a schematic diagram of the structure of a sleep monitoring system provided in an embodiment of this application. Figure 1 As shown in the figure, the sleep monitoring system 100 provided in this application embodiment includes multiple data acquisition modules A1...An and a data analysis module B, and each of the data acquisition modules A1...An is communicatively connected to the data analysis module B.

[0058] The data acquisition module Ai is used to guide the monitored user to wear the device by playing guidance images or videos, and to collect the monitored user's initial body data in real time. When the monitored user is detected to be wearing the device correctly based on the initial body data, the module collects the monitored user's sleep body data in real time and transmits the sleep body data to the data analysis module B in real time.

[0059] It should be noted that the data acquisition module Ai includes multiple sensors for collecting sleep data. Initial body data refers to the initial data collected by the data acquisition module Ai when the monitored user is wearing it. Sleep body data, on the other hand, is the sleep data collected by the data acquisition module Ai when the monitored user is wearing the sensors within the module during sleep.

[0060] Here, the data acquisition module Ai is primarily used to collect the user's body data in real time. Specifically, when the user wears the data acquisition module Ai, it automatically plays guidance images or videos, allowing the user to wear the module independently. This method of guiding the user to wear the device saves on the labor costs associated with physician assistance. Simultaneously, the data acquisition module Ai collects the user's initial body data. Once it detects that the user is wearing the device correctly based on this initial data, it collects the user's sleep data in real time and transmits it to the data analysis module B. This method of sending sleep data to the data analysis module B only when the user is wearing the device correctly improves the accuracy of subsequent sleep monitoring by the data analysis module B.

[0061] Here, according to the embodiment provided in this application, information of multiple monitored users is registered through multiple data acquisition modules A1…An, digitizing the registration process for multiple monitored users. This allows users to view the sleep monitoring status of multiple monitored users in real time through the data analysis module B. Users can also quickly view detailed information about the monitored users, such as phone numbers and medical record numbers, as well as whether they have picked up or returned the equipment. This helps users efficiently manage the sleep monitoring data of the monitored users and facilitates subsequent information statistics and research data screening.

[0062] Please see Figure 2 , Figure 2 This is a schematic diagram of the structure of a data acquisition module provided in an embodiment of this application. Figure 2As shown, the data acquisition module Ai includes a headband data acquisition unit 111, a chest and abdomen data acquisition unit 112, a lower limb data acquisition unit 113, a signal analysis unit 114, and a data transmission unit 115. Here, the headband data acquisition unit 111, chest and abdomen data acquisition unit 112, and lower limb data acquisition unit 113 are respectively connected to the signal analysis unit 114, and the headband data acquisition unit 111, chest and abdomen data acquisition unit 112, and lower limb data acquisition unit 113 are respectively connected to the data transmission unit 115. The signal analysis unit 114 is connected to the data transmission unit 115.

[0063] Here, when the user wears the headband data acquisition unit 111, the chest and abdomen data acquisition unit 112, and the lower limb data acquisition unit 113, the data acquisition module AI first checks the USB permission. If the check is successful, it checks the USB port of the headband data acquisition unit 111 to check if the headband is inserted. If the insertion is successful, it obtains the Bluetooth device list and checks whether the chest and abdomen data acquisition unit 112 and the lower limb data acquisition unit 113 can be connected. If a connection is detected, it connects and checks the chest and abdomen data acquisition unit 112 and the lower limb data acquisition unit 113.

[0064] The headband data acquisition unit 111 is used to acquire the initial headband signal of the monitored user and send the initial headband signal to the signal analysis unit 114.

[0065] The chest and abdomen data acquisition unit 112 is used to acquire the initial chest and abdomen signals of the monitored user and send the initial chest and abdomen signals to the signal analysis unit 114.

[0066] The lower limb data acquisition unit 113 is used to acquire the initial lower limb electromyography (EMG) signal of the monitored user and send the initial lower limb EMG signal to the signal analysis unit 114.

[0067] The signal analysis unit 114 is configured to generate a sleep data acquisition signal and send it to the headband data acquisition unit 111, the chest and abdomen data acquisition unit 112, and the lower limb data acquisition unit 113 when it is detected that the monitored user is correctly wearing the headband data acquisition unit 111, the chest and abdomen data acquisition unit 112, and the lower limb data acquisition unit 113 based on the initial headband signal, the initial chest and abdomen signal, and the initial lower limb electromyography signal.

[0068] Here, the headband data acquisition unit 111 is mainly used to acquire the electroencephalogram (EEG) signals of the monitored user, the chest and abdomen data acquisition unit 112 is mainly used to acquire the electrocardiogram (ECG) signals of the monitored user, and the lower limb data acquisition unit 113 is mainly used to acquire the lower limb electromyography (EMG) signals of the monitored user. In specific implementation, after the monitored user has worn the headband data acquisition unit 111, chest and abdomen data acquisition unit 112, and lower limb data acquisition unit 113, the headband data acquisition unit 111 acquires the initial headband signal of the monitored user and sends it to the signal analysis unit 114. The chest and abdomen data acquisition unit 112 acquires the initial chest and abdomen signals of the monitored user and sends them to the signal analysis unit 114. The lower limb data acquisition unit 113 acquires the initial lower limb EMG signals of the monitored user and sends them to the signal analysis unit 114. The signal analysis unit 114 detects whether the monitored user has correctly worn the headband data acquisition unit 111, chest and abdomen data acquisition unit 112, and lower limb data acquisition unit 113 based on the received initial headband signal, initial chest and abdomen signal, and initial lower limb EMG signal. When the signal analysis unit 114 detects that the monitored user is wearing the device correctly, it generates a sleep data acquisition signal and sends it to the headband data acquisition unit 111, the chest and abdomen data acquisition unit 112, and the lower limb data acquisition unit 113.

[0069] Specifically, according to the embodiments provided in this application, the initial headband signal includes an impedance detection headband signal and a calibration detection headband signal. The impedance detection headband signal includes electroencephalogram (EEG) signals, electrooculogram (EOG) signals, and mandibular electromyography (EMG) signals. The calibration detection headband signal includes EOG signals, mandibular EMG signals, and audio signals. The initial chest and abdomen signal includes an impedance detection chest and abdomen signal and a calibration detection chest and abdomen signal. The impedance detection chest and abdomen signal includes an electrocardiogram (ECG) signal. The calibration detection chest and abdomen signal includes nasal airflow signals, thermal signals, and chest and abdomen band signals.

[0070] As an optional embodiment, the signal analysis unit 114 is used to detect whether the monitored user is correctly wearing the headband data acquisition unit 111, the chest and abdomen data acquisition unit 112, and the lower limb data acquisition unit 113 through the following steps:

[0071] Based on a preset impedance signal threshold, the impedance values ​​corresponding to the impedance detection headband signal, the impedance detection chest and abdomen signal, and the initial lower limb electromyography signal are determined.

[0072] In a circuit with resistance, inductance, and capacitance, the opposition to the flow of current is called impedance. Impedance needs to be measured and recorded before sleep monitoring to ensure the electrode impedance is within acceptable limits.

[0073] In this implementation, after receiving the initial headband signal, initial chest and abdomen signal, and initial lower limb electromyography (EMG) signal, the signal analysis unit 114 automatically tests the impedance. Based on a preset impedance signal threshold, it determines the impedance values ​​corresponding to the impedance-detected headband signal, the impedance-detected chest and abdomen signal, and the initial lower limb EMG signal. When the impedance value meets the signal impedance threshold, calibration detection continues. If the impedance value does not meet the signal impedance threshold, an image or video will automatically pop up to guide the monitored user to make adjustments.

[0074] When the impedance value is determined to meet the signal impedance threshold, the calibration action voice is played, and the calibration detection headband signal, the calibration detection chest and abdomen signal, and the initial lower limb electromyography signal generated by the monitored user when performing the corresponding calibration specified action based on the calibration action voice are acquired. When the calibration detection headband signal, the calibration detection chest and abdomen signal, and the initial lower limb electromyography signal are determined to meet the calibration signal threshold, it is determined that the monitored user is correctly wearing the headband data acquisition unit 111, the chest and abdomen data acquisition unit 112, and the lower limb data acquisition unit 113.

[0075] Here, calibration refers to objectively testing the machine's operational status, the proper functioning of each sensor, and the synchronous acquisition of audio and video data through a series of commands. Calibration reflects the basic information of the monitored user (basic EEG data, baseline blood oxygen saturation, etc.), ensuring the accuracy of the collected data and providing a more accurate reflection of the monitored user's information.

[0076] Here, the signal analysis unit 114 uses voice prompts to automatically play calibration action voice prompts, guiding the monitored user to perform the specified calibration actions. While the monitored user performs the calibration actions, the signal analysis unit 114 acquires the calibration detection headband signal, calibration detection chest and abdomen signal, and initial lower limb electromyography (EMG) signal generated by the monitored user when performing the corresponding calibration action based on the calibration action voice prompts. It analyzes these signals according to pre-set calibration signal thresholds, such as signal range and baseline multiples, to determine whether calibration is successful, and saves the calibration action result record. If the monitored user performs the corresponding action according to the voice prompts, and the signal analysis unit 114 determines it is successful, it plays the next voice prompt. If the signal analysis unit 114 determines the calibration action has failed, it repeats the voice prompts to continue guiding the monitored user to complete the calibration action. When the signal analysis unit 114 determines that the calibration detection headband signal, calibration detection chest and abdomen signal, and initial lower limb EMG signal meet the calibration signal thresholds, it considers the calibration successful. In this way, automatic voice calibration can effectively reduce the workload of doctors and lower the professional requirements of sleep monitoring devices. This method can save doctors' labor costs. When monitoring at home, the monitored user does not need to go to the hospital to wear the device, reducing the wearing time and solving the problem of discomfort and inconvenience caused by the journey from the hospital to home, which may lead to lead dislodgement.

[0077] As an optional embodiment, after determining the impedance values ​​corresponding to the impedance detection headband signal, the impedance detection chest and abdomen signal, and the initial lower limb electromyography signal, the signal analysis unit 114 is further configured to:

[0078] The impedance values ​​corresponding to the impedance detection headband signal, the impedance detection chest and abdomen signal, and the initial lower limb electromyography signal are sent to the data transmission unit, so that the data transmission unit sends the impedance values ​​to the data analysis module.

[0079] Here, the signal analysis unit 114 automatically records the impedance value every 30 seconds during sleep monitoring and sends the determined impedance value to the data transmission unit 115, so that the data transmission unit 115 can send the impedance value to the data analysis module B. This saves doctors time from manually detecting impedance and provides convenience for sleep data interpretation through real-time and efficient recording of impedance data.

[0080] The headband data acquisition unit 111 is also used to send the sleep headband signal to the data transmission unit 115.

[0081] The chest and abdomen data acquisition unit 112 is also used to send sleep chest and abdomen signals to the data transmission unit 115.

[0082] The lower limb data acquisition unit 113 is also used to send the sleep lower limb electromyography signal to the data transmission unit 115.

[0083] The data transmission unit 115 is used to transmit the sleep headband signal, the sleep chest and abdomen signal, and the sleep lower limb electromyography signal to the data analysis module B.

[0084] Here, the data transmission unit 115 is mainly used to send the received sleep data to the data analysis module. In specific implementation, after detecting that the hardware is correctly worn, the sleep monitoring of the monitored user begins. The headband data acquisition unit 111 collects the user's sleep headband signal in real time and sends the sleep headband signal to the data transmission unit 115. According to the embodiments provided in this application, the sleep headband signal collected by the headband data acquisition unit 111 includes sleep electrooculogram (EOG) signal, sleep electroencephalogram (EEG) signal, sleep mandibular muscle (EMG) signal, and sleep audio signal. The chest and abdomen data acquisition unit 112 collects the user's sleep chest and abdomen signal in real time and sends the sleep chest and abdomen signal to the data transmission unit 115. According to the embodiments provided in this application, the sleep chest and abdomen signal collected by the chest and abdomen data acquisition unit 112 includes sleep electrocardiogram (ECG) signal, sleep blood oxygenation signal, chest respiratory movement signal, abdominal respiratory movement signal, respiratory pressure signal, thermal signal, and body position signal. The lower limb data acquisition unit 113 collects the user's sleep lower limb EMG signal in real time and sends the sleep lower limb EMG signal to the data transmission unit 115. The data transmission unit 115 sends the received sleep headband signal, sleep chest and abdomen signal, and sleep lower limb electromyography signal to the data analysis module B. Specifically, in online mode, data is pushed by the hardware box, and in home mode, it is pulled from the PC's disk and connected to a persistent hard drive. In online mode, the data analysis module B acquires user sleep data in real time, including signal data, impedance, and blood oxygen saturation dropout alerts. The online device list can display the status of multiple devices being monitored in batches and show any problems encountered during monitoring. It efficiently alerts doctors to impedance and blood oxygen saturation dropout issues during sleep monitoring.

[0085] The data analysis module B is used to receive sleep body data sent by multiple data acquisition modules A1...An, and to perform sleep monitoring on the monitored user wearing the data acquisition module Ai based on the sleep body data sent by each data acquisition module Ai, so as to obtain sleep analysis reports of multiple monitored users.

[0086] Here, sleep body data specifically refers to sleep headband signals, sleep chest and abdomen signals, and sleep lower limb electromyography signals.

[0087] Here, data analysis module B is sleep monitoring software, primarily used to monitor the sleep of the monitored users. In practice, data analysis module B receives sleep data from multiple data acquisition modules A1…An, and performs sleep monitoring on the monitored users wearing each data acquisition module Ai based on the sleep data sent by each module Ai, thereby obtaining sleep analysis reports for multiple monitored users. In this way, data analysis module B can simultaneously monitor the sleep of multiple monitored users, batch display the status of multiple monitored devices, and display problem prompts during monitoring, thus improving the efficiency of sleep monitoring for multiple users.

[0088] As an optional embodiment, when the data analysis module B is used to perform sleep monitoring on the monitored user based on the sleep body data to obtain a sleep analysis report of the monitored user, the data analysis module B is specifically used for:

[0089] Feature extraction is performed on the sleep chest and abdomen signals and the sleep headband signals to determine the sleep events of the monitored user.

[0090] Here, after receiving the sleep chest and abdomen signal and the sleep headband signal, the data analysis module B extracts features from the sleep chest and abdomen signal and the sleep headband signal to determine the sleep events of the monitored user. Here, according to the embodiment provided in this application, sleep events specifically include the monitored user's sleep stages, sleep apnea events, and awakening events.

[0091] Based on the sleep headband signal, the sleep chest and abdomen signal, and the sleep lower limb electromyography signal, a secondary calculation is performed on the basis of the sleep events to determine the complex linkage events of the monitored user.

[0092] In this implementation, after the sleep events of the monitored user are determined, the data analysis module B performs secondary calculations based on the sleep headband signal, sleep chest and abdomen signal, and sleep lower limb electromyography signal to identify the complex linkage events of the monitored user. These complex linkage events include periodic leg movements and Cheyne-Stokes respiration. The calculation method for these complex linkage events is detailed in existing technologies and will not be repeated here.

[0093] Multiple sleep signal indicators are extracted based on the sleep events and the complex linkage events.

[0094] Here, the sleep signal index data includes the highest heart rate during wakefulness, the lowest heart rate during wakefulness, the lowest blood oxygen saturation during wakefulness, the highest heart rate during sleep, the lowest heart rate during sleep, the lowest blood oxygen saturation during sleep, and the longest breathing time during sleep.

[0095] In practice, data analysis module B identifies complex linkage events of the monitored user and extracts various sleep signal indicators based on sleep events and these complex linkage events. These include the highest heart rate during wakefulness, the lowest heart rate during wakefulness, the lowest blood oxygen saturation during wakefulness, the highest heart rate during sleep, the lowest heart rate during sleep, the lowest blood oxygen saturation during sleep, and the longest breathing time during sleep.

[0096] The sleep analysis report is generated based on the sleep signal index data.

[0097] In this specific implementation, after the data analysis module B determines the sleep signal indicator data of the monitored user, it generates a sleep analysis report for the monitored user based on the sleep signal indicator data. As an example, when generating the sleep analysis report, the same type of indicator data of the monitored user can be placed in the same table of the sleep analysis report, or the indicator data during the waking period can be placed in the same table, etc., and this application does not make specific limitations in this regard.

[0098] As an optional embodiment, after extracting sleep signal indicators based on the sleep events and the complex linkage events, the data analysis module is further used for:

[0099] In response to the user's selection of any sleep signal indicator data, the target sleep signal indicator data is determined, and all data results corresponding to the target sleep signal indicator data are displayed on the display interface.

[0100] In this implementation, in response to the user's selection of any sleep signal indicator data, the target sleep signal indicator data is determined, and all data results corresponding to the target sleep signal indicator data are displayed on the interface. This allows doctors to quickly understand the monitored user's condition through the key indicators displayed on the interface. Clicking on an indicator allows doctors to jump to the relevant section, facilitating efficient location and analysis of key indicators. This improves doctors' analytical efficiency and reduces manpower costs.

[0101] The sleep monitoring system provided in this application includes multiple data acquisition modules and a data analysis module, each of which is communicatively connected to the data analysis module. The data acquisition module guides the monitored user to wear the device by playing instructional images or videos, and collects the user's initial body data in real time. When the user is detected to be wearing the device correctly based on the initial body data, the module collects the user's sleep body data in real time and transmits the sleep body data to the data analysis module in real time. The data analysis module receives the sleep body data sent by the multiple data acquisition modules and performs sleep monitoring on the monitored user wearing the data acquisition module based on the sleep body data sent by each data acquisition module, thereby obtaining sleep analysis reports for the multiple monitored users.

[0102] This system ensures that sleep data is only sent to the data analysis module when the user wears the device correctly, improving the accuracy of subsequent sleep monitoring. Furthermore, the data analysis module tracks the entire sleep monitoring process, enabling unified and efficient management of user data, optimizing physiological signal interpretation algorithms, and guaranteeing the encryption of sleep data transmission and storage. This also improves the integrity, security, diagnostic efficiency, and accuracy of sleep data collection and storage. It adapts to the future needs of polysomnography development, providing a convenient and efficient monitoring system for the continuously growing demand for sleep monitoring and diagnosis.

[0103] Please see Figure 3 , Figure 3 This is a flowchart illustrating a sleep monitoring method provided in an embodiment of this application. Figure 3 As shown, the sleep monitoring method is applied to a sleep monitoring system, and the sleep monitoring method includes:

[0104] S301, for each data acquisition module, the data acquisition module guides the monitored user to wear the device by playing guidance images or guidance videos, and collects the initial body data of the monitored user in real time. When it is detected that the monitored user is wearing the device correctly based on the initial body data, the module collects the monitored user's sleep body data in real time and transmits the sleep body data to the data analysis module in real time.

[0105] S302, the data analysis module receives sleep body data sent by multiple data acquisition modules, and performs sleep monitoring on the monitored user wearing the data acquisition module based on the sleep body data sent by each data acquisition module, so as to obtain sleep analysis reports of multiple monitored users.

[0106] Furthermore, the data acquisition module includes a headband data acquisition unit, a chest and abdomen data acquisition unit, a lower limb data acquisition unit, a signal analysis unit, and a data transmission unit. The data acquisition module collects the initial body data of the monitored user in real time. When it detects that the monitored user is wearing the device correctly based on the initial body data, it collects the monitored user's sleep body data in real time and transmits the sleep body data to the data analysis module in real time, including:

[0107] The headband data acquisition unit acquires the initial headband signal of the monitored user and sends the initial headband signal to the signal analysis unit;

[0108] The chest and abdomen data acquisition unit is used to acquire the initial chest and abdomen signals of the monitored user and send the initial chest and abdomen signals to the signal analysis unit.

[0109] The lower limb data acquisition unit acquires the initial lower limb electromyographic signals of the monitored user and sends the initial lower limb electromyographic signals to the signal analysis unit;

[0110] When the signal analysis unit detects that the monitored user is correctly wearing the headband data acquisition unit, the chest and abdomen data acquisition unit, and the lower limb data acquisition unit based on the initial headband signal, the initial chest and abdomen signal, and the initial lower limb electromyography signal, it generates a sleep data acquisition signal and sends it to the headband data acquisition unit, the chest and abdomen data acquisition unit, and the lower limb data acquisition unit.

[0111] The headband data acquisition unit sends the sleep headband signal to the data transmission unit;

[0112] The chest and abdomen data acquisition unit sends sleep chest and abdomen signals to the data transmission unit;

[0113] The lower limb data acquisition unit sends the sleep lower limb electromyography signal to the data transmission unit;

[0114] The data transmission unit sends the sleep headband signal, the sleep chest and abdomen signal, and the sleep lower limb electromyography signal to the data analysis module.

[0115] Furthermore, the initial headband signal includes an impedance detection headband signal and a calibration detection headband signal. The impedance detection headband signal includes electroencephalogram (EEG) signals, electrooculogram (EOG) signals, and mandibular electromyography (EMG) signals. The calibration detection headband signal includes EOG signals, mandibular EMG signals, and audio signals. The initial chest and abdomen signal includes an impedance detection chest and abdomen signal and a calibration detection chest and abdomen signal. The impedance detection chest and abdomen signal includes an electrocardiogram (ECG) signal. The calibration detection chest and abdomen signal includes nasal airflow signals, thermal signals, and chest and abdomen band signals.

[0116] The signal analysis unit detects whether the monitored user is correctly wearing the headband data acquisition unit, the chest and abdomen data acquisition unit, and the lower limb data acquisition unit through the following steps:

[0117] Based on a preset impedance signal threshold, the impedance values ​​corresponding to the impedance detection headband signal, the impedance detection chest and abdomen signal, and the initial lower limb electromyography signal are determined.

[0118] When the impedance value is determined to meet the signal impedance threshold, the calibration action voice is played, and the calibration detection headband signal, the calibration detection chest and abdomen signal, and the initial lower limb electromyography signal generated by the monitored user when performing the corresponding calibration specified action based on the calibration action voice are acquired. When the calibration detection headband signal, the calibration detection chest and abdomen signal, and the initial lower limb electromyography signal are determined to meet the calibration signal threshold, it is determined that the monitored user is correctly wearing the headband data acquisition unit, the chest and abdomen data acquisition unit, and the lower limb data acquisition unit.

[0119] Furthermore, after the signal analysis unit determines the impedance values ​​corresponding to the impedance detection headband signal, the impedance detection chest and abdomen signal, and the initial lower limb electromyography signal, the sleep monitoring method further includes:

[0120] The impedance values ​​corresponding to the impedance detection headband signal, the impedance detection chest and abdomen signal, and the initial lower limb electromyography signal are sent to the data transmission unit, so that the data transmission unit sends the impedance values ​​to the data analysis module.

[0121] Furthermore, the data analysis module is used to perform sleep monitoring on the monitored user based on the sleep body data to obtain a sleep analysis report for the monitored user, including:

[0122] Feature extraction is performed on the sleep chest and abdomen signals and the sleep headband signals to determine the sleep events of the monitored user;

[0123] Based on the sleep headband signal, the sleep chest and abdomen signal, and the sleep lower limb electromyography signal, a second calculation is performed on the basis of the sleep events to determine the complex linkage events of the monitored user.

[0124] Based on the sleep events and the complex linkage events, a variety of sleep signal index data are extracted; among them, the sleep signal index data includes the highest heart rate during wakefulness, the lowest heart rate during wakefulness, the lowest blood oxygen saturation during wakefulness, the highest heart rate during sleep, the lowest heart rate during sleep, the lowest blood oxygen saturation during sleep, and the longest breathing time during sleep.

[0125] The sleep analysis report is generated based on the sleep signal index data.

[0126] Furthermore, after extracting multiple sleep signal indicators based on the sleep events and the complex linkage events, the sleep monitoring method further includes:

[0127] The data analysis module responds to the user's selection of any type of sleep signal indicator data, determines the target sleep signal indicator data, and displays all data results corresponding to the target sleep signal indicator data on the display interface.

[0128] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4 As shown, the electronic device 400 includes a processor 410, a memory 420, and a bus 430.

[0129] The memory 420 stores machine-readable instructions executable by the processor 410. When the electronic device 400 is running, the processor 410 communicates with the memory 420 via the bus 430. When the machine-readable instructions are executed by the processor 410, they can perform the operations described above. Figure 3 The steps of the sleep monitoring method in the illustrated method embodiment can be found in the method embodiment for specific implementation methods, which will not be repeated here.

[0130] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can perform the above-described actions. Figure 3 The steps of the sleep monitoring method in the illustrated method embodiment can be found in the method embodiment for specific implementation methods, which will not be repeated here.

[0131] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0132] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0133] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0134] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0135] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0136] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The scope of protection of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A sleep monitoring system, characterized in that, The sleep monitoring system includes multiple data acquisition modules and data analysis modules, and each of the data acquisition modules is communicatively connected to the data analysis module. The data acquisition module is used to guide the monitored user to wear the device by playing guidance images or videos, and to collect the initial body data of the monitored user in real time. When the monitored user is detected to be wearing the device correctly based on the initial body data, the module collects the monitored user's sleep body data in real time and transmits the sleep body data to the data analysis module in real time. The data analysis module is used to receive sleep body data sent by multiple data acquisition modules, and to perform sleep monitoring on the monitored user wearing the data acquisition module based on the sleep body data sent by each data acquisition module, so as to obtain sleep analysis reports of multiple monitored users. The sleep body data includes sleep headband signals, sleep chest and abdomen signals, and sleep lower limb electromyography signals. When the data analysis module performs sleep monitoring on the monitored user based on the sleep body data to obtain a sleep analysis report for the monitored user, the data analysis module is specifically used for: Feature extraction is performed on the sleep chest and abdominal signals and the sleep headband signals to determine the sleep events of the monitored user; wherein, the sleep events specifically include sleep stages, sleep apnea events, and awakening events; Based on the sleep headband signal, the sleep chest and abdomen signal, and the sleep lower limb electromyography signal, a secondary calculation is performed on the sleep events to determine the complex linkage events of the monitored user; wherein, the complex linkage events include periodic leg movements and Chen's breathing; Based on the sleep events and the complex linkage events, a variety of sleep signal index data are extracted; among them, the sleep signal index data includes the highest heart rate during wakefulness, the lowest heart rate during wakefulness, the lowest blood oxygen saturation during wakefulness, the highest heart rate during sleep, the lowest heart rate during sleep, the lowest blood oxygen saturation during sleep, and the longest breathing time during sleep. The sleep analysis report is generated based on the sleep signal index data.

2. The sleep monitoring system according to claim 1, characterized in that, The data acquisition module includes a headband data acquisition unit, a chest and abdomen data acquisition unit, a lower limb data acquisition unit, a signal analysis unit, and a data transmission unit; The headband data acquisition unit is used to acquire the initial headband signal of the monitored user and send the initial headband signal to the signal analysis unit; The chest and abdomen data acquisition unit is used to acquire the initial chest and abdomen signals of the monitored user and send the initial chest and abdomen signals to the signal analysis unit. The lower limb data acquisition unit is used to acquire the initial lower limb electromyographic signals of the monitored user and send the initial lower limb electromyographic signals to the signal analysis unit; The signal analysis unit is configured to generate a sleep data acquisition signal and send it to the headband data acquisition unit, chest and abdomen data acquisition unit, and lower limb data acquisition unit when it is detected that the monitored user is correctly wearing the headband data acquisition unit, chest and abdomen data acquisition unit, and lower limb data acquisition unit based on the initial headband signal, the initial chest and abdomen signal, and the initial lower limb electromyography signal. The headband data acquisition unit is also used to transmit the sleep headband signal to the data transmission unit. The chest and abdomen data acquisition unit is also used to send sleep chest and abdomen signals to the data transmission unit; The lower limb data acquisition unit is also used to send the sleep lower limb electromyography signal to the data transmission unit; The data transmission unit is used to send the sleep headband signal, the sleep chest and abdomen signal, and the sleep lower limb electromyography signal to the data analysis module.

3. The sleep monitoring system according to claim 2, characterized in that, The initial headband signal includes an impedance detection headband signal and a calibration detection headband signal. The impedance detection headband signal includes electroencephalogram (EEG) signals, electrooculogram (EOG) signals, and mandibular electromyography (EMG) signals. The calibration detection headband signal includes EOG signals, mandibular EMG signals, and audio signals. The initial chest and abdomen signal includes an impedance detection chest and abdomen signal and a calibration detection chest and abdomen signal. The impedance detection chest and abdomen signal includes an electrocardiogram (ECG) signal. The calibration detection chest and abdomen signal includes nasal airflow signals, thermal signals, and chest and abdomen band signals. The signal analysis unit is used to detect whether the monitored user is correctly wearing the headband data acquisition unit, the chest and abdomen data acquisition unit, and the lower limb data acquisition unit through the following steps: Based on a preset impedance signal threshold, the impedance values ​​corresponding to the impedance detection headband signal, the impedance detection chest and abdomen signal, and the initial lower limb electromyography signal are determined. When the impedance value is determined to meet the signal impedance threshold, the calibration action voice is played, and the calibration detection headband signal, the calibration detection chest and abdomen signal, and the initial lower limb electromyography signal generated by the monitored user when performing the corresponding calibration specified action based on the calibration action voice are acquired. When the calibration detection headband signal, the calibration detection chest and abdomen signal, and the initial lower limb electromyography signal are determined to meet the calibration signal threshold, it is determined that the monitored user is correctly wearing the headband data acquisition unit, the chest and abdomen data acquisition unit, and the lower limb data acquisition unit.

4. The sleep monitoring system according to claim 3, characterized in that, After determining the impedance values ​​corresponding to the impedance detection headband signal, the impedance detection chest and abdomen signal, and the initial lower limb electromyography signal, the signal analysis unit is further configured to: The impedance values ​​corresponding to the impedance detection headband signal, the impedance detection chest and abdomen signal, and the initial lower limb electromyography signal are sent to the data transmission unit, so that the data transmission unit sends the impedance values ​​to the data analysis module.

5. The sleep monitoring system according to claim 1, characterized in that, After extracting multiple sleep signal indicators based on the sleep events and the complex linkage events, the data analysis module is further used for: In response to the user's selection of any sleep signal indicator data, the target sleep signal indicator data is determined, and all data results corresponding to the target sleep signal indicator data are displayed on the display interface.

6. A sleep monitoring method, characterized in that, The sleep monitoring method is applied to the sleep monitoring system according to any one of claims 1-5, and the sleep monitoring method includes: For each data acquisition module, the data acquisition module guides the monitored user to wear the device by playing guidance images or videos, and collects the monitored user's initial body data in real time. When the monitored user is detected to be wearing the device correctly based on the initial body data, the module collects the monitored user's sleep body data in real time and transmits the sleep body data to the data analysis module in real time. The data analysis module receives sleep body data sent by multiple data acquisition modules, and performs sleep monitoring on the monitored user wearing the data acquisition module based on the sleep body data sent by each data acquisition module, so as to obtain sleep analysis reports of multiple monitored users; The sleep body data includes sleep headband signals, sleep chest and abdomen signals, and sleep lower limb electromyography signals; the data analysis module performs sleep monitoring on the monitored user based on the sleep body data to obtain a sleep analysis report for the monitored user, including: Feature extraction is performed on the sleep chest and abdominal signals and the sleep headband signals to determine the sleep events of the monitored user; wherein, the sleep events specifically include sleep stages, sleep apnea events, and awakening events; Based on the sleep headband signal, the sleep chest and abdomen signal, and the sleep lower limb electromyography signal, a secondary calculation is performed on the sleep events to determine the complex linkage events of the monitored user; wherein, the complex linkage events include periodic leg movements and Chen's breathing; Based on the sleep events and the complex linkage events, a variety of sleep signal index data are extracted; among them, the sleep signal index data includes the highest heart rate during wakefulness, the lowest heart rate during wakefulness, the lowest blood oxygen saturation during wakefulness, the highest heart rate during sleep, the lowest heart rate during sleep, the lowest blood oxygen saturation during sleep, and the longest breathing time during sleep. The sleep analysis report is generated based on the sleep signal index data.

7. The sleep monitoring method according to claim 6, characterized in that, The data acquisition module includes a headband data acquisition unit, a chest and abdomen data acquisition unit, a lower limb data acquisition unit, a signal analysis unit, and a data transmission unit; the sleep monitoring method further includes: The headband data acquisition unit acquires the initial headband signal of the monitored user and sends the initial headband signal to the signal analysis unit; The chest and abdomen data acquisition unit acquires the initial chest and abdomen signals of the monitored user and sends the initial chest and abdomen signals to the signal analysis unit; The lower limb data acquisition unit acquires the initial lower limb electromyography (EMG) signal of the monitored user and sends the initial lower limb EMG signal to the signal analysis unit; When the signal analysis unit detects that the monitored user is correctly wearing the headband data acquisition unit, the chest and abdomen data acquisition unit, and the lower limb data acquisition unit based on the initial headband signal, the initial chest and abdomen signal, and the initial lower limb electromyography signal, it generates a sleep data acquisition signal and sends it to the headband data acquisition unit, the chest and abdomen data acquisition unit, and the lower limb data acquisition unit. The headband data acquisition unit sends the sleep headband signal to the data transmission unit during sleep. The chest and abdomen data acquisition unit sends sleep chest and abdomen signals to the data transmission unit; The lower limb data acquisition unit sends the sleep lower limb electromyography signal to the data transmission unit; The data transmission unit is used to send the sleep headband signal, the sleep chest and abdomen signal, and the sleep lower limb electromyography signal to the data analysis module.

8. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus, and the machine-readable instructions are executed by the processor to perform the steps of the sleep monitoring method as described in any one of claims 6-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the sleep monitoring method as described in any one of claims 6-7.

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