Sleep monitoring method and device

Abstract

The invention relates to a sleep monitoring method and device. The method comprises the following steps: acquiring sleep monitoring information of a user in a monitoring period, wherein the sleep monitoring information comprises electroencephalogram data, myoelectricity data and electro-oculogram data; the method further comprises the step of determining user sleep information based on the electroencephalogram data, the myoelectricity data and the electro-oculogram data in the monitoring period, and the user sleep information comprises sleep time information and sleep monitoring information in a sober sleep stage, a non-fast eye movement sleep stage and a fast eye movement sleep stage. In addition, the method further comprises the step of determining sleep time information and sleep monitoring information under three sub-stages of the non-fast eye movement sleep stage in the sleep information of the user based on the electro-oculogram data under the non-fast eye movement sleep stage. Through the mode, the sleep staging distribution condition of the user in the current monitoring period can be determined based on the multi-dimensional data monitored by the user in the current monitoring period, and the sleep conditions of the user in different monitoring periods can be accurately reflected.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and more particularly, to a sleep monitoring method and device. Background Art

[0002] Sleep is not a static process, but a dynamic cycle consisting of multiple stages, usually divided into non-rapid eye movement sleep (NREM, referred to as N) and rapid eye movement sleep (REM, referred to as R). NREM1 is the transition from wakefulness to sleep and is the lightest sleep stage. NREM2 is a light sleep stage with a slow heart rate and a drop in body temperature. NREM3 is deep sleep, in which the body is restored to the greatest extent. The REM stage is characterized by rapid eye movement, increased brain activity, and is associated with dreaming.

[0003] As the pressure in life and work increases, the quality of sleep is greatly reduced, which in turn affects human health. Analyzing sleep structure is very important for evaluating sleep quality, which can improve the user's sleep condition in a targeted manner and even solve sleep-related diseases. Summary of the invention

[0004] The embodiments of the present disclosure provide a sleep monitoring method and device.

[0005] In the first aspect of the present disclosure, a sleep monitoring method is provided. The method includes obtaining sleep monitoring information of a user during a monitoring period, and the sleep monitoring information includes electroencephalogram data, electromyography data, and electrooculography data. The method also includes determining the user's sleep information based on the electroencephalogram data, electromyography data, and electrooculography data, and the user's sleep information includes sleep time information and sleep monitoring information under awake sleep stages, non-rapid eye movement stages, and rapid eye movement stages. In addition, the method also includes determining the sleep time information and sleep monitoring information under three sub-stages of the non-rapid eye movement sleep stage in the user's sleep information based on the electrooculography data under the non-rapid eye movement sleep stage.

[0006] In a second aspect of the present disclosure, a sleep monitoring device is provided. The device includes an acquisition module configured to acquire sleep monitoring information of a user during a monitoring period, and the sleep monitoring information includes electroencephalogram (EEG) data, electromyography (EMG) data, and electrooculogram (EOG) data. The device also includes a first determination module configured to determine the user's sleep information based on the EEG data, electromyography (EMG) data, and EOG data, and the user's sleep information includes sleep time information and sleep monitoring information under awake sleep stages, non-rapid eye movement (NREM) sleep stages, and rapid eye movement (REM) sleep stages. In addition, the device also includes a second determination module configured to determine the sleep time information and sleep monitoring information under three sub-stages of the non-REM sleep stage in the user's sleep information based on the EOG data under the non-REM sleep stage.

[0007] In a third aspect of the present disclosure, a computer program product is provided, comprising a computer program, the computer program being executed by a processor to implement the method according to the first aspect.

[0008] In a fourth aspect of the present disclosure, a machine-readable storage medium is provided, wherein the machine-readable storage medium stores machine-executable instructions, wherein the machine-executable instructions are executed by a processor to implement the method provided according to the first aspect of the present disclosure.

[0009] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein: Figure 1 A schematic diagram showing an example environment in which some embodiments of the present disclosure may be implemented; Figure 2 A flowchart of a sleep monitoring method according to some embodiments of the present disclosure is shown; Figure 3 A flowchart showing an exemplary process of determining user sleep information according to some embodiments of the present disclosure; Figure 4 A schematic diagram showing an example process of determining sleep time information and sleep monitoring information under awake sleep stages, non-rapid eye movement sleep stages, and rapid eye movement sleep stages according to some embodiments of the present disclosure; Figure 5 A schematic diagram showing an example process of determining sleep time information and sleep monitoring information under N1, N2, and N3 sub-stages under non-rapid eye movement sleep staging according to some embodiments of the present disclosure; Figure 6 A schematic diagram showing an example process of determining a sleep state according to some embodiments of the present disclosure; Figure 7 A schematic diagram showing an exemplary sleep report formed based on user sleep information according to some embodiments of the present disclosure; Figure 8 A block diagram showing a sleep monitoring device according to some embodiments of the present disclosure; and Fig. 9 A block diagram of an electronic device in which various embodiments of the present disclosure may be implemented is shown. DETAILED DESCRIPTION

[0011] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0012] In the description of the embodiments of the present disclosure, the term "including" and similar terms should be understood as open inclusion, that is, "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0013] Improving sleep quality has far-reaching significance for overall health and daily life, and the analysis of sleep structure is particularly important for the assessment of sleep quality. By analyzing the user's sleep structure, the user's sleep condition can be determined, and the duration of each sleep stage (referring to the various sleep stages mentioned below) of the user's complete sleep cycle from waking up to falling asleep to waking up again, as well as related physiological parameter data, can be known, and the user's physiological condition during sleep can be understood. After obtaining the user's sleep structure, the user's sleep quality can be evaluated, and even whether the user has problems that affect health, such as respiratory diseases, cardiovascular diseases, mental illnesses, etc.

[0014] In some related technologies, the sleep-wake cycle is promoted by recording physical activities through devices worn on the wrist (such as smart watches). In other related technologies, photoplethysmography is used through wearable devices (such as smart watches) to monitor heart rate changes, and the sleep structure is analyzed based on the heart rate characteristics of different sleep stages. Although this type of method is easy to detect, it mainly relies on one parameter (body movement signal or heart rate) to analyze the user's sleep structure. In this way, the analyzed sleep structure is not enough to accurately reflect the user's actual sleep condition, and it is impossible to know the user's physiological condition in each sleep stage, let alone detect whether the user has health problems. Even if the heart rate abnormality can be detected, it is only an occasional abnormality, and it is impossible to combine other physiological parameters to evaluate the impact of diseases related to abnormal heart rate.

[0015] In some related technologies, polysomnography (PSG) analyzes different stages of sleep by recording multiple physiological parameters. However, such methods are generally performed in professional sleep centers, using predefined rule sets to identify different sleep stages. For example, a predefined EGG signal rule set is constructed based on the frequency characteristics of the electroencephalogram (EEG) signal (such as delta waves, theta waves, etc.). The sleep monitoring analysis based on such technologies depends on a predetermined signal rule set. The accuracy of the signal rule set definition will affect the accuracy of sleep stage identification, and a large number of rules affect the efficiency of data analysis and processing. In particular, various sleep stages will be repeatedly switched during the sleep monitoring cycle. For example, the user slowly enters the REM sleep stage from the N1, N2, and N3 stages of the non-rapid eye movement sleep stage (i.e., the N1, N2, and N3 sub-stages mentioned later). After that, the REM sleep stage may enter the N3 stage of the non-rapid eye movement sleep stage again after a period of time, and then slowly return to the N2 stage, and may enter the N3 stage again. In such a complex change, it is necessary to rely on the signal rule set to make type-by-type judgments, and the analysis and processing efficiency is greatly affected.

[0016] To this end, an embodiment of the present disclosure proposes a sleep monitoring method, which can first determine the sleep time in the wakefulness, non-rapid eye movement and rapid eye movement sleep stages and their corresponding EEG, EMG and EO data based on the EEG, EMG and EO data in the monitoring cycle, and then further subdivide and determine the sleep time of the three sub-stages in the non-rapid eye movement sleep stage and their corresponding EEG, EMG and EO data based on the EO data in the non-rapid eye movement sleep stage.

[0017] In this way, the sleep structure that reflects the user's actual sleep condition can be accurately identified based on the three-dimensional data of EEG, EMG and EOG. After analyzing the three general sleep stages of wakefulness, non-rapid eye movement and rapid eye movement, based on the non-rapid eye movement EOG data, that is, data of an incomplete monitoring cycle, the sub-stages of the non-rapid eye movement sleep stage can be quickly identified based on less data. This way, the sleep structure analysis and processing is efficient and accurate.

[0018] Figure 1 1 is a schematic diagram of an example environment 100 in which some embodiments of the present disclosure may be implemented. Figure 1As shown, the environment 100 includes a sleep monitor 104, which has several electrodes, including electrodes for detecting EEG data (such as installed on the outside of the temporal lobe of the user 102, i.e., in front of the ear), electrodes for detecting electrooculographic data (such as installed near the brow bone of the user 102), and motors for detecting electromyographic data (such as installed on the chin of the user, i.e., the chin position). The sleep monitor 104 collects the physiological data of the user in each sleep stage during the monitoring cycle through electrodes attached to various positions of the user 102, including EEG data, electromyographic data, and electrooculographic data. In addition, the sleep monitor 104 also includes electrodes for detecting electrocardiographic data, such as installed on the chest of the user 102. The environment 100 also includes a server 106, which is equipped with a sleep application platform, and the sleep application platform is configured with a sleep monitoring algorithm. The server 106 is in communication with the sleep monitor 104, and the sleep monitor 104 sends the data collected by the electrodes to the server 106. The server 106 obtains the user's sleep structure according to the sleep monitoring algorithm analysis and processing, including the sleep time information and related physiological data under the awake sleep stage, non-rapid eye movement sleep stage and rapid eye movement sleep stage (i.e., the sleep monitoring information referred to below). In some embodiments, the server 106 first determines the W stage (awake sleep stage), N stage (non-rapid eye movement sleep stage), R stage (rapid eye movement sleep stage) and their corresponding physiological data under the monitoring cycle based on the physiological data under the monitoring cycle, such as the EEG, EMG, and EO data of T1 to T5. Afterwards, based on the determined physiological data of stage N (non-rapid eye movement sleep stage), stages N1, N2, and N3 and their corresponding physiological data are determined.

[0019] like Figure 1 The environment 100 also includes a terminal device 108, which can be an electronic device such as a mobile phone, a tablet, or a computer, which is installed with a client software that can access the sleep application platform. The user can bind the device number of the sleep monitor 104 to his personal account through the client software. The terminal device 108 can display the user's sleep structure analyzed by the server 106 in the form of information on the terminal device. The information presents the order in which the W stage, N1~N3 stage, and R stage appear in the monitoring cycle, the time corresponding to each stage, and the data corresponding to each stage ( Figure 1 In the figure, columns with oblique lines, dots, horizontal lines, blanks, and cross lines are used to mark W, R, N1, N2, and N3, respectively. In some embodiments, the terminal device 108 displays the above information in the form of a sleep report.

[0020] In this way, the sleep structure that can reflect the user's actual sleep condition can be determined based on the multi-dimensional physiological data within the monitoring period, so that health judgments based on the sleep structure are more accurate. This method first determines the approximate sleep stage based on the full data within the monitoring period, and then determines the various sub-stages of the non-rapid eye movement stage based on local data, that is, the eye movement data within the non-rapid eye movement stage. In this way, there is no need to perform a large amount of data calculation and analysis on each stage using a rule set, and the stage determination is performed in two steps. In the process of determining the sub-stage, based on the eye movement data, the time information and physiological data of each sleep stage within the monitoring period can be efficiently processed and analyzed.

[0021] It should be understood that the architecture and functions in the example environment 100 are presented for exemplary purposes only and do not imply any limitation on the scope of the present disclosure. Embodiments of the present disclosure may also be applied to other environments with different structures and / or functions.

[0022] Figure 2 FIG. 2 is a flowchart of a sleep monitoring method 200 according to some embodiments of the present disclosure. The method 200 may be, for example, Figure 1 The server 106 in the environment 100 shown is executed. Figure 2 As shown, in box 202, method 200 can obtain the sleep monitoring information of the user in the monitoring period, and the sleep monitoring information includes EEG data, EMG data, and EO data. In some embodiments, the sleep monitor is attached to various positions of the user's body through electrodes, and the electrodes are used to monitor physiological data during the entire process of the user being awake, falling asleep, sleeping, and waking up, such as EEG, EO, EMG, etc. (if necessary, including ECG, blood oxygen concentration) physiological data. The sleep monitor can store the collected physiological data in it, which can be stored in the form of chart data, sequence data, or array data. Among them, the monitoring period can be understood as the entire period from the user turning on the sleep monitor in the awake state to turning off the sleep monitor after waking up again. The sleep monitor sends the collected data of a monitoring period to the server.

[0023] In box 204, method 200 can determine the user's sleep information based on the EEG data, EMG data, and EO data during the monitoring cycle. The user's sleep information includes sleep time information and sleep monitoring information during awake sleep stages, non-rapid eye movement sleep stages, and rapid eye movement sleep stages. In some embodiments, the server can perform W, N, and R stage identification based on the acquired physiological data (including EEG data, EMG data, and EO data) during the monitoring cycle, that is, it can determine the sleep time information under each stage, including the start and end time, and can also determine the relevant physiological data under the corresponding stage, that is, it can determine the relevant sleep monitoring information under each stage. According to the start and end time of each stage, the order of each sleep stage can be determined. In addition, the server stores the determined sleep stage time information and sleep monitoring information in the form of user sleep information.

[0024] In block 206, method 200 may determine the sleep time information and sleep monitoring information of three sub-stages of the non-rapid eye movement sleep stage in the user sleep information based on the electrooculogram data of the non-rapid eye movement sleep stage. In some embodiments, the server may determine the sleep time information of three sub-stages N1, N2, and N3 of the N stage, including their end time, and the relevant physiological data of the corresponding stages, including EEG data, EMG data, and electrooculogram data, based on the electrooculogram data in the relevant physiological data of the N stage determined in block 204.

[0025] In this way, the distribution of the approximate sleep stages within the monitoring cycle can be determined based on the full data of the monitoring cycle. The arrangement of the awake sleep stages, non-rapid eye movement sleep stages, and rapid eye movement sleep stages within the monitoring cycle can be mainly clarified. The number of awake sleep stages, non-rapid eye movement sleep stages, and rapid eye movement sleep stages in a monitoring cycle can be determined, as well as their respective start and end times, and their corresponding sleep monitoring information. Afterwards, based on the electrooculographic data in the non-rapid eye movement sleep stages within the monitoring cycle, the non-rapid eye movement sleep stages are carefully identified to identify the sub-stages within the stage based on less data. In this way, the sleep structure that reflects the user's actual sleep condition can be more accurately identified based on multidimensional data, and the processing efficiency is high.

[0026] Figure 3A flowchart of an exemplary process 300 for determining user sleep information according to some embodiments of the present disclosure is shown. In box 302, the process 300 can obtain EEG data under a monitoring period T, and the EEG data includes brain waves, which are recorded by an electroencephalogram (EEG). In box 304, it is determined whether the average frequency of the EEG waves of the continuous frames in the EEG data is greater than or equal to the frequency threshold. If so, enter box 306 to determine that the sleep time period corresponding to the EEG data corresponding to the continuous frames is the sleep time information of the awake sleep stage W, and accordingly, the EEG data, electromyographic data, and electrooculographic data of the sleep time period are determined as the sleep monitoring information of the awake sleep stage W. Among them, a frame refers to a data segment of a fixed time length, generally referred to as an epoch, which is usually set to 30 seconds. Continuous frames can be set to multiple consecutive data segments of fixed time lengths, such as 4 or 5 consecutive, or even more. For the sake of recognition accuracy, it is generally not advisable to be too many, not more than 6. Take the continuous frame as a time window and perform a frequency judgment. The frequency threshold is configured based on the EEG characteristic of the awake sleep stage, and is generally set to 13HZ. In some embodiments, assuming that there are 20 frames in 10 minutes, and 4 consecutive frames are used as a time window, the average frequency of the brain waves of 4 consecutive frames (such as frame 0 to frame 3) is obtained based on the spectrum analysis technology. If it is greater than or equal to the frequency threshold, it is determined that the brain waves of the 4 consecutive frames belong to the awake sleep stage W. Next, the average frequency of the brain waves of the next 4 consecutive frames (such as frame 1 to frame 4) is obtained based on the frequency analysis technology. If it is greater than or equal to the frequency threshold, it is determined that the brain waves of frame 4 also belong to the awake sleep stage W. In this way, the sliding window obtains continuous frames for judgment until the end. In addition, the above process can also obtain multiple time windows by sliding the window and make judgments synchronously. In some embodiments, assuming that there are 20 frames in 10 minutes, and taking 4 consecutive frames as a time window, the average frequency of the brain waves of 4 consecutive frames (such as frame 0 to frame 3) is obtained based on the spectrum analysis technology. If it is greater than or equal to the frequency threshold, it is determined that the brain waves of the 4 consecutive frames belong to the awake sleep stage W. Next, the average frequency of the brain waves of the next 4 consecutive frames (such as frame 4 to frame 7) is obtained based on the frequency analysis technology. If it is greater than or equal to the frequency threshold, it is determined that the brain waves of frame 4 to frame 7 also belong to the awake sleep stage W. In this way, the sliding window obtains continuous frames for judgment until the end. In addition, the above process can also obtain multiple time windows by sliding the window and make judgments synchronously. In box 304, if it is determined that the average frequency of the brain waves of the continuous frames is less than the frequency threshold, the next continuous frame can be obtained to continue the judgment.

[0027] In box 308, the process 300 can obtain the electromyographic data and electrooculographic data of the remaining sleep time periods in the monitoring cycle T except for the awake sleep stage W determined in box 306. The electrooculographic data and electromyographic data are used to identify the sleep stages of the remaining sleep time periods in the monitoring cycle T. The electrooculographic data includes eye waves, which are recorded by electrooculography (EOG). The electromyographic data includes myoelectric waves, which are recorded by electromyography (EMG). In box 310, it is determined whether the myoelectric voltage changes corresponding to the continuous frames in the electromyographic data containing the minimum myoelectric voltage amplitude are stable. In box 312, it is determined whether the eye movement speed in the electrooculographic data in the sleep time period corresponding to the judgment in box 310 is greater than or equal to the eye movement threshold. Among them, the eye movement threshold is set based on the eye movement speed in the rapid eye movement sleep stage, which is generally 100deg / s. The acquisition process of the eye movement velocity in the electrooculographic data includes integrating the EOG signal, calculating the displacement of the integral, then converting the horizontal and vertical displacements into angles, calculating the angle change between adjacent time points and dividing it by the time interval, and determining the obtained angular velocity as the eye movement velocity. If both the judgments of box 310 and box 312 are yes, the sleep time information and sleep monitoring information of the rapid eye movement sleep stage R can be determined from the remaining sleep time period. Compared with other sleep stages, the human body is in the rapid eye movement sleep stage R, and the muscles are relaxed, the amplitude of myoelectric activity is low, and the eye movement speed rotates rapidly due to dreams or active neuronal activity. When the above two characteristics are met, the relevant information of the rapid eye movement sleep stage R can be determined. In this way, after the rapid eye movement sleep stage in the remaining sleep time period is determined, box 316 can determine that the other sleep time periods in the remaining sleep time period except the rapid eye movement sleep stage are non-rapid eye movement sleep stages. Accordingly, based on the time information of other sleep time periods, the corresponding sleep monitoring information is determined.

[0028] like Figure 3 The process 300 may determine the sleep time information and sleep monitoring information of the first and third sub-stages N1 and N3 of the non-rapid eye movement sleep stage in block 318 based on the electrooculogram data of the non-rapid eye movement sleep stage N determined in block 316. Next, based on the sleep time information of the first and third sub-stages N1 and N3 determined in block 318, the sleep time information and sleep monitoring information of the second sub-stage N2 of the non-rapid eye movement sleep stage is determined.

[0029] In this way, the human body can be identified according to its significant characteristics in different sleep stages. Based on the significant characteristics of the awake sleep stage and the non-rapid eye movement sleep stage, the former has significant changes in brain wave frequency, and the latter has significant changes in eye movement and electromyography. In this way, the awake sleep stage and the rapid eye movement sleep stage are identified from the monitoring cycle in turn based on their respective characteristics. Based on the elimination method, the unidentified part of the monitoring cycle can be determined as the non-rapid eye movement sleep stage. The above method does not need to rely on a large number of rule sets, and a rough sleep structure analysis of the monitoring cycle can be performed using a relatively good algorithm recognition and judgment. Afterwards, based on the determined electrooculographic data of the non-rapid eye movement sleep stage, the sub-stage of the non-rapid eye movement sleep stage between the awake sleep stage and the rapid eye movement sleep stage can be determined.

[0030] Figure 4 FIG. 4 is a schematic diagram showing an example process 400 for determining sleep time information and sleep monitoring information under awake sleep stages, non-rapid eye movement sleep stages, and rapid eye movement sleep stages according to some embodiments of the present disclosure. The process 400 may include a first acquisition unit 404 acquiring monitoring periods T1 to T2. N The EEG data 402 is obtained, and the continuous frames in the EEG 406 are sent to the first response unit 408 for processing. When it is determined that the continuous frames correspond to T1~T 1+i Sleep time and T N-j ~T N When the average brain wave frequency of the sleep period is greater than or equal to the frequency threshold, T1~T 1+i Sleep time and T N-j ~T N The sleep time periods are all awake sleep stages, where i, j, and N are natural numbers, and i and j are both less than N, 1+i<Nj. Then, the second acquisition unit 416 acquires the remaining sleep time period T from the electrooculographic data 412. 1+i ~T N-j The remaining sleep time period T is obtained from the electromyographic data 414. 1+i ~T N-j The second response unit 420 responds to the myoelectric voltage change corresponding to the continuous frames with the minimum myoelectric voltage amplitude and containing the minimum myoelectric voltage amplitude in the myoelectric data, and the eye movement speed of the electrooculogram data in the sleep time period corresponding to the myoelectric data is not less than the eye movement threshold, and determines that the relevant sleep time period is the rapid eye movement sleep stage, and the other sleep time periods are the non-rapid eye movement sleep stage. In the example in the figure, T 1+m ~T 1+n The sleep period is determined as the REM sleep stage, and other sleep periods T 1+i ~T 1+m and T1+n ~T N-j Determined as non-rapid eye movement sleep stage. Wherein, n and m are natural numbers and are less than N, 1+i<1+m<1+n<Nj. Afterwards, the determination unit 424 determines the user sleep information 426 based on the time information of each stage of the EEG 410 that determines the awake sleep stage, and the EEG 422-1 and 422-2 that determine the non-rapid eye movement sleep analysis and the rapid eye movement sleep stage. The user sleep information 426 includes EEG data, electrooculographic data, and electromyographic data, and each sleep stage corresponds to different wave data.

[0031] In some embodiments, the process of judging whether the myoelectric voltage change corresponding to the continuous frames with the minimum myoelectric voltage amplitude in the myoelectric data is stable includes determining the minimum myoelectric voltage amplitude from the remaining sleep time period, and two mutation points located before and after the minimum myoelectric voltage amplitude, the mutation point refers to the ratio of its voltage amplitude to the minimum myoelectric voltage amplitude is greater than the amplitude ratio threshold, and the amplitude ratio threshold is greater than or equal to 1.5. In one example, the myoelectric wave is shown in myoelectric voltage, and the minimum myoelectric voltage amplitude is determined by identifying the voltage amplitude at all troughs in the waveform, and then it can be determined that a period of time near this minimum myoelectric voltage amplitude may be a rapid eye movement sleep stage. By identifying the mutation points before and after the minimum myoelectric voltage amplitude, if the voltage amplitude of the previous mutation point is greater than or equal to 1.5 with the minimum myoelectric voltage amplitude, the time period before the previous mutation point is not in the rapid eye movement sleep stage, and if the voltage amplitude of the latter mutation point is greater than or equal to 1.5 with the minimum myoelectric voltage amplitude, the time period after the latter mutation point is not in the rapid eye movement sleep stage. The judgment process also includes, in response to the average voltage amplitude in the sleep time period between the two mutation points not being greater than the amplitude threshold, determining that the myoelectric voltage change corresponding to the continuous frames in the sleep time period between the two mutation points is stable. In one example, the average voltage amplitude is calculated for the data in the sleep time period between the two mutation points, and then compared with the amplitude threshold. If it does not exceed the amplitude threshold, it is stable. The amplitude threshold is determined based on the usual changes in myoelectricity during rapid eye movement sleep stages. The amplitude threshold is the myoelectric voltage value, which is generally 5μV.

[0032] In some embodiments, due to poor electrode contact or special circumstances of body position, the minimum EMG voltage may appear in other sleep stages except REM sleep stages. Determine the minimum EMG voltage amplitude from the remaining sleep time period, and the two mutation points before and after the minimum EMG voltage amplitude include first determining the minimum EMG voltage amplitude from the remaining sleep time period, when the mutation point is not identified before or after the minimum EMG voltage amplitude, that is, as long as one mutation point is not identified, then the minimum EMG voltage amplitude needs to be re-determined. Then determine the second smallest EMG voltage amplitude from the remaining sleep time period. Perform mutation point identification before and after the second smallest EMG voltage amplitude, if any, determine the second smallest EMG voltage amplitude as the target minimum EMG voltage amplitude, otherwise, repeat the above process until the target minimum EMG voltage amplitude is determined. Afterwards, based on the aforementioned embodiment, judge the stability of the EMG voltage change between the two mutation points.

[0033] Figure 5 A schematic diagram of an example process 500 for determining sleep time information and sleep monitoring information under the N1, N2, and N3 sub-stages of the non-rapid eye movement sleep stage according to some embodiments of the present disclosure is shown. The process 500 includes a time extraction unit 504 extracting sleep time information of each awake sleep stage and rapid eye movement sleep stage from any sleep monitoring information (such as electroencephalogram, electromyography, and electrooculogram data) in the user sleep information 502, and determining the start time (or end time) of the first sub-stage and the end time (or start time) of the third sub-stage of the non-rapid eye movement sleep stage located between the adjacent awake sleep stage and the rapid eye movement sleep stage in blocks 506-1 and 506-2, respectively. In some embodiments, the sleep structure generally has such a "W-N1-N2-N3-R-N3-N2-N1-W" situation, in which there are two sub-situations "W-N1-N2-N3-R" and "R-N3-N2-N1-W". By judging the order of appearance of adjacent W and R, it can be determined whether it is the former or the latter sub-case. In the former case, the end time of the wakefulness sleep stage can be determined as the start time of the first sub-stage of the non-rapid eye movement sleep stage (such as Figure 5 T shown in 1+i ), and the start time of the REM sleep stage is determined as the end time of the third substage of the non-REM sleep stage (such as Figure 5 T shown in 1+m ), which determines the start of the wakefulness sleep stage as the end of the first substage of the non-REM sleep stage (e.g. Figure 5 T shown in N-j ), and the end time of the REM sleep stage is determined as the start time of the third substage of the non-REM sleep stage (such as Figure 5 T shown in1+n ).

[0034] like Figure 5 The process 500 includes a response unit 510 based on the electrooculogram voltage change of consecutive frames in the electrooculogram data of the non-rapid eye movement sleep stage before the adjacent awake sleep stage and the rapid eye movement sleep stage (which may be T 1+i ~T 1+m The corresponding N stage, or T 1+n ~T N-j corresponding to the N stage), and one of the two sub-cases (the former sub-case corresponds to T 1+i ~T 1+m , the latter subcase corresponds to T 1+n ~T N-j ), in block 512, sleep time information of the first substage and the third substage of the non-rapid eye movement sleep stage is determined. In some embodiments, sleep time information of the N1 and N3 substages may be determined in parallel. In other embodiments, sleep time information of the N1 substage may be determined first, and then sleep time information of the N3 substage may be determined, or sleep time information of the N3 substage may be determined first, and then sleep time information of the N1 substage may be determined.

[0035] In some embodiments, for T 1+i ~T 1+m The corresponding N stage, in response to the electrooculographic data under the non-rapid eye movement sleep stage (such as Figure 5 When the electrooculogram voltage of consecutive frames in 508-1) remains unchanged, the end time of the third sub-stage of the non-rapid eye movement sleep stage (i.e., T 1+m ) as a benchmark, determine the sleep time period corresponding to the electrooculographic data corresponding to the continuous frame (assuming that the start time of the continuous frame is T x , then the sleep time period is T x ~T 1+m) is the sleep time information of the third sub-stage of the non-rapid eye movement sleep stage. In one example, based on the end time of the third sub-stage, continuous frames are intercepted forward. If the average value of the electrooculogram voltage of the continuous frames does not exceed the stable voltage threshold, and the stable voltage threshold is a value close to 0, such as 0.5 microvolts, it is considered that the electrooculogram voltage of the continuous frame remains unchanged. In this process, the interception judgment is first performed according to the preset length of the continuous frame, and then the length of the continuous frame is gradually increased, and the interception judgment is performed until the average value of the electrooculogram voltage of the continuous frame exceeds the stable voltage threshold. Then, the sleep time period corresponding to the electrooculogram data corresponding to the continuous frame length intercepted last is considered to be the sleep time information of the third sub-stage. In another example, based on the end time of the third sub-stage, the waveform of the continuous frame is intercepted forward. If the waveform line segment is a straight line segment or the overlap rate with the straight line segment reaches more than 95%, it is considered that the electrooculogram voltage of the continuous frame remains unchanged. The sleep time period corresponding to the electrooculogram data corresponding to the intercepted continuous frame length is the sleep time information of the third sub-stage.

[0036] In some embodiments, for T 1+n ~T N-j The corresponding N stage, in response to the electrooculographic data under the non-rapid eye movement sleep stage (such as Figure 5 When the electrooculogram voltage of consecutive frames in 508-2) remains unchanged, the start time of the third sub-stage of the non-rapid eye movement sleep stage (i.e., T 1+n ) as a benchmark, determine the sleep time period corresponding to the electrooculographic data corresponding to the continuous frame (assuming that the end time of the continuous frame is T y , then the sleep time period is T 1+n ~T y ) is the sleep time information of the third sub-stage of the non-rapid eye movement sleep stage. In one example, based on the start time of the third sub-stage, continuous frames are intercepted backwards. If the average value of the electrooculogram voltage of the continuous frames does not exceed the stable voltage threshold, and the stable voltage threshold is a value close to 0, such as 0.5 microvolts, it is considered that the electrooculogram voltage of the continuous frame remains unchanged. In this process, the interception judgment is first performed according to the preset length of the continuous frame, and then the length of the continuous frame is gradually increased, and the interception judgment is performed until the average value of the electrooculogram voltage of the continuous frame exceeds the stable voltage threshold. Then, the sleep time period corresponding to the electrooculogram data corresponding to the continuous frame length intercepted last is considered to be the sleep time information of the third sub-stage. In another example, based on the start time of the third sub-stage, the waveform of the continuous frame is intercepted backwards. If the waveform line segment is a straight line segment or the overlap rate with the straight line segment reaches more than 95%, it is considered that the electrooculogram voltage of the continuous frame remains unchanged. The sleep time period corresponding to the electrooculogram data corresponding to the intercepted continuous frame length is the sleep time information of the third sub-stage.

[0037] In the third substage of non-rapid eye movement sleep, the eye movement of the human body almost stops completely, but there is still fluctuation, close to 0. At this stage, the user is in a deep sleep stage. In this way, based on the significant difference between the eye movement of the N3 substage and other stages, the sleep time information of the third substage can be effectively identified from the non-rapid eye movement sleep stage.

[0038] In some embodiments, for T 1+i ~T 1+m The corresponding N stage, in response to the average interval time between the positive and negative amplitudes of the adjacent electrooculogram voltages in the consecutive frames of the electrooculogram data under the non-rapid eye movement sleep stage is not less than the interval threshold, the start time of the first sub-stage of the non-rapid eye movement sleep stage (i.e., T 1+i ) as a benchmark, determine the sleep time period corresponding to the electrooculographic data of the continuous frame (assuming that the end time of the continuous frame is T x’ , then the sleep time period is T 1+i ~ T x’ ) is the sleep time information of the first sub-stage of the non-rapid eye movement sleep staging. In one example, multiple positive and negative amplitudes appear in a continuous frame, and the interval time of the positive and negative amplitudes of adjacent electrooculogram voltages is calculated to obtain multiple interval times, and the average interval time of multiple interval times in the continuous frame is calculated. The interval threshold is generally set to 0.5 seconds. When the average interval time is greater than or equal to the interval threshold, it is determined that the sleep time period corresponding to the continuous frame is the sleep time information of the first sub-stage of the non-rapid eye movement sleep staging. In this process, firstly, according to the preset length of the continuous frame, the start time of the N1 stage is used as the starting point to intercept and judge backward, and then the length of the continuous frame is gradually increased, and the interception judgment is performed until the average interval time between the positive and negative amplitudes of the adjacent electrooculogram voltages of the continuous frame is less than the interval threshold, then it is considered that the sleep time period corresponding to the electrooculogram data corresponding to the continuous frame length intercepted last time is the sleep time information of the first sub-stage.

[0039] In some embodiments, for T 1+n ~T N-j The corresponding N stage, in response to the average interval time between the positive and negative amplitudes of the adjacent electrooculogram voltages in the consecutive frames of the electrooculogram data under the non-rapid eye movement sleep stage is not less than the interval threshold, the end time of the first sub-stage of the non-rapid eye movement sleep stage (i.e., T N-j ) as a benchmark, determine the sleep time period corresponding to the electrooculographic data of the continuous frame (assuming that the start time of the continuous frame is T y’ , then the sleep time period is T y’ ~T N-j) is the sleep time information of the first sub-stage of the non-rapid eye movement sleep staging. In one example, multiple positive and negative amplitudes appear in a continuous frame, and the interval time of the positive and negative amplitudes of adjacent electrooculogram voltages is calculated to obtain multiple interval times, and the average interval time of multiple interval times in the continuous frame is calculated. The interval threshold is generally set to 0.5 seconds. When the average interval time is greater than or equal to the interval threshold, it is determined that the sleep time period corresponding to the continuous frame is the sleep time information of the first sub-stage of the non-rapid eye movement sleep staging. In this process, firstly, according to the preset length of the continuous frame, the interception judgment is performed from the end time of the N1 stage forward, and then the length of the continuous frame is gradually increased, and the interception judgment is performed until the average interval time between the positive and negative amplitudes of the adjacent electrooculogram voltages of the continuous frame is less than the interval threshold, then it is considered that the sleep time period corresponding to the electrooculogram data corresponding to the continuous frame length intercepted last time is the sleep time information of the first sub-stage.

[0040] Generally, in the first substage of non-rapid eye movement, the eye voltage amplitude changes over a longer period of time with slow eye movement than in other sleep stages. In this way, the sleep time information of the first substage can be effectively identified from the non-rapid eye movement sleep stage based on the significant difference between the eye movement of the N1 substage and other stages.

[0041] like Figure 5 As shown, the process 500 includes a time determination unit 514, which can determine the sleep time information of the second sub-stage of the non-rapid eye movement sleep stage in block 516 according to the sleep time information of N1 and N3 determined in block 512. In some embodiments, the sleep time information of the second sub-stage of the non-rapid eye movement sleep stage is determined based on the end time of the first sub-stage of the non-rapid eye movement sleep stage located between the adjacent awake sleep stage and the rapid eye movement sleep stage and the start time of the third sub-stage. Figure 5 For example, the start time of the third sub-period is T x is the end time of the second sub-period, and the end time of the first sub-period is T x’ is the start time of the second sub-period, then the sleep time information of the second sub-period corresponding to the first sub-case is T x’ ~T x In some embodiments, the sleep time information of the second substage of the non-rapid eye movement sleep stage is determined based on the start time of the first substage of the non-rapid eye movement sleep stage and the end time of the third substage between the adjacent awake sleep stage and the rapid eye movement sleep stage. Figure 5 For example, the starting time of the first sub-period is T y’ is the end time of the second sub-period, and the end time of the third sub-period is T yis the start time of the second sub-period, then the sleep time information of the second sub-period corresponding to the second sub-case is T y ~ T y’ By identifying the first sub-stage and the second sub-stage, the sleep time information of the second sub-stage of the non-rapid eye movement sleep stage can be quickly determined without the need to be based on the physiological characteristics of the user under the second sub-stage. Afterwards, based on the sleep time information determined for each sub-stage, the sleep monitoring information related to the sleep time information is determined as the sleep monitoring information under the sub-stage corresponding to the non-rapid eye movement sleep stage.

[0042] In some embodiments, the sleep structure may also present such situations as "R-N2-N3-R", "R-N3-N2-N3-R", "R-N3-N2-N1-N2-N3-R", and "R-N2-N1-N2-N3-R". Based on the electrooculogram data under the non-rapid eye movement sleep stage, determining the sleep time information and sleep monitoring information under the three sub-stages of the non-rapid eye movement sleep stage also includes performing another set of process subdivisions on the non-rapid eye movement sleep stages between adjacent Rs by judging that there are adjacent Rs and there is no W in between. The process includes determining the end time or start time of the third sub-stage of the non-rapid eye movement sleep stage located between the adjacent rapid eye movement sleep stages based on the sleep time information of each of the adjacent rapid eye movement sleep stages in the monitoring cycle. The process also includes determining the sleep time information of the third substage of the non-rapid eye movement sleep stage between the adjacent rapid eye movement sleep stages based on the changes in the electrooculogram voltage of the continuous frames in the electrooculogram data under the non-rapid eye movement sleep stage between the adjacent rapid eye movement sleep stages, and the end time or start time of the third substage of the non-rapid eye movement sleep stage. The process includes identifying the first substage of the non-rapid eye movement sleep stage for the sleep time period between the adjacent rapid eye movement sleep stages excluding the third substage, and if it is not identified, determining the sleep time information of the second substage of the non-rapid eye movement sleep stage based on the time information of the rapid eye movement sleep stage and the sleep time information of the third substage of the non-rapid eye movement sleep stage. If it is identified, the sleep time information of the first sub-stage of the non-rapid eye movement sleep stage is first determined, and then the sleep time information of the second sub-stage of the non-rapid eye movement sleep stage is determined based on the time information of the rapid eye movement sleep stage, the sleep time information of the third sub-stage of the non-rapid eye movement sleep stage, and the sleep time information of the first sub-stage of the non-rapid eye movement sleep stage. Finally, based on the sleep time information of each sub-stage of the non-rapid eye movement sleep stage between adjacent Rs, the corresponding sleep monitoring information is determined.

[0043] In some embodiments, for the "R-N2-N3-R" or "R-N3-N2-N3-R" situation, first determine whether N3 exists, and then determine N2 after N1 is not identified. The N3 determination process can determine the sleep time information of N3 by judging whether the electrooculogram voltage of consecutive frames remains unchanged, and based on the time of the previous R or the next R and the consecutive frames involved in the stable change of electrooculogram voltage. If N3 does not exist and N1 is not identified, the non-rapid eye movement sleep stage between adjacent Rs is determined as N2. Taking "R-N2-N3-R" as an example, in one example, the start time of the next R is the end time of N3, and the starting point of the continuous frame with stable voltage change is the start time of N3, so that the sleep time information of N3 can be determined. Afterwards, based on the sleep time information of N3 and the sleep time information of R, the sleep time information of N2 can be determined. Taking "R-N2-N3-R" as an example, in another example, the end time of the previous R is the start time of N2, and the start time of N3 is the end time of N2, so that the sleep time information of N2 can be determined. Finally, based on the respective sleep time information, the corresponding sleep monitoring information can be determined.

[0044] In some embodiments, for the case of "R-N3-N2-N1-N2-N3-R" or "R-N2-N1-N2-N3-R", first determine whether N3 exists, and then identify N1 and N2. The N3 determination process can determine the sleep time information of N3 by judging whether the electrooculogram voltage of consecutive frames remains unchanged, and based on the time of the previous R or the next R and the consecutive frames involved in the stable change of the electrooculogram voltage. Then, from the electrooculogram data corresponding to the remaining sleep time period, identify the part of the electrooculogram waveform as a square wave. When the square wave duration is greater than 1 minute, the square wave duration period is determined to be the sleep time information of N1. The unidentified sleep time period is determined as the sleep time information of N2. Based on the fact that the eye movement during N1 is slow and generally regular, it generally presents square wave changes. The sleep time period in which N1 is located is determined by identifying the waveform. Afterwards, after determining the sleep time information of N3 and N1, the sleep time information of N2 can be quickly determined. When the duration of the square wave is no more than 1 minute, the sleep time periods between adjacent Rs except N3 are confirmed as the sleep time information of N2. In this case, the user briefly enters the N1 period and quickly enters the N2 period, and the user's sleep state is approximately defaulted to N2. If N3 is not identified, but there are N1 and N2, then after N1 is identified based on the above method, the remaining sleep time period is determined to be N2. Finally, based on the respective sleep time information, the corresponding sleep monitoring information can be determined.

[0045] In some embodiments, the sleep structure may not have N3 before N2 enters R during the period between adjacent W and R, or not have N3 before R leaves and enters N2 during the period between adjacent R and W. Based on the electrooculogram data of the non-rapid eye movement sleep stage between the adjacent awake sleep stage and the rapid eye movement sleep stage in the monitoring cycle, determining the sleep time information and sleep monitoring information of the three sub-stages of the non-rapid eye movement sleep stage also includes determining the sleep time information of the first sub-stage of the non-rapid eye movement sleep stage based on the electrooculogram voltage changes of consecutive frames in the electrooculogram data of the non-rapid eye movement sleep stage between the adjacent awake sleep stage and the rapid eye movement sleep stage, and the start time or end time of the first sub-stage of the non-rapid eye movement sleep stage in response to the non-rapid eye movement sleep stage not having the third sub-stage. Afterwards, based on the sleep time information of the first sub-stage of the non-rapid eye movement sleep stage and the sleep time information of the non-rapid eye movement sleep stage, the sleep time information of the second sub-stage of the non-rapid eye movement sleep stage is determined. That is, the sleep time period except the first sub-stage in the non-rapid eye movement sleep stage is determined as the sleep time information of the second sub-stage. In one example, some embodiments are used to determine that the electrooculogram voltage of the electrooculogram data under the non-rapid eye movement sleep stage does not have a continuous frame and remains unchanged, then it is determined that the non-rapid eye movement sleep stage does not have a third sub-stage. Afterwards, some embodiments are used to determine that the average interval time between the positive and negative amplitudes of the adjacent electrooculogram voltages of the continuous frames in the electrooculogram data under the non-rapid eye movement sleep stage is not less than the interval threshold, and the start time or end time of the first sub-stage of the non-rapid eye movement sleep stage determined based on some embodiments is used as a reference to determine that the sleep time period corresponding to the electrooculogram data corresponding to the continuous frame is the sleep time information of the first sub-stage of the non-rapid eye movement sleep stage. Finally, the remaining sleep time period in the non-rapid eye movement sleep stage is determined as the sleep time information of the second sub-stage of the non-rapid eye movement sleep stage. Taking the sleep structure of W first and then R as an example, the end time of N1 is used as the start time of N2, and the start time of R is used as the end time of N2. Taking the sleep structure of R first and then W as an example, the end time of R is used as the start time of N2, and the start time of N1 is used as the end time of N2.

[0046] Figure 6A schematic diagram of an example process 600 for determining a sleep state according to some embodiments of the present disclosure is shown. The process 600 may include, based on user sleep information 602 and ECG data 604 and blood oxygen saturation data 606 in a monitoring cycle, a data extraction unit 608 extracting sleep time information of non-rapid eye movement sleep stages and rapid eye movement sleep stages, and determining the ECG data and blood oxygen saturation data corresponding to the non-rapid eye movement sleep stages and the rapid eye movement sleep stages in box 610. The process 600 includes, based on the ECG data and blood oxygen saturation data corresponding to the non-rapid eye movement sleep stages and the rapid eye movement sleep stages, a state determination unit 612 performing state determination to determine whether the user is in a normal sleep state as shown in box 614, or in a hypoventilation sleep state as shown in box 616. In some embodiments, in response to the blood oxygen saturation data at multiple moments in the non-rapid eye movement sleep stages and the rapid eye movement sleep stages not being greater than the blood oxygen threshold, the ECG data at the corresponding multiple moments are obtained. Afterwards, in response to the change rate of the heart rate of the ECG data at multiple moments compared to the heart rate of the ECG data at the previous moment exceeding the ECG change rate threshold, it is determined that the user is in a hypoventilation sleep state, otherwise, the user is in a normal sleep state. In one example, the ECG wave can be processed to obtain the heart rate value, and the heart rate value H1 of the ECG data at multiple consecutive moments is obtained, and the heart rate value H0 of the ECG data at multiple consecutive moments before this is obtained. The change rate is obtained by calculating (H1-H0) / H0. Generally, the ECG change rate is about 1% under normal circumstances, or even close to 0. When the heart rate change threshold is set to 1% (the appropriate threshold can also be adjusted according to the heart rate state of each user, especially for users with irregular heart rates, the threshold can be increased), when the heart rate suddenly becomes very slow or very fast, the user is in a hypoventilation sleep state when the blood oxygen saturation data is not good. Among them, the ECG data is collected to the sleep monitor through the electrode sheet attached to the chest, and the relevant data is uploaded to the server. ECG data includes heart waves, which are recorded by electrocardiogram (ECG or EKG). The threshold of ECG change rate is determined based on the ECG data of a large number of users in normal sleep state. Blood oxygen saturation data is collected by pulse oximeter and uploaded to the server. The blood oxygen threshold is generally 94%.

[0047] In this way, by first detecting the blood oxygen saturation data at continuous moments, and then combining it with the ECG data at continuous moments, the user's sleep state can be identified more reliably and accurately. When a hypoventilation sleep state occurs, the user may have serious sleep health problems such as apnea in the later stage. The identification of the hypoventilation sleep state can prompt the user's physical condition, so that the user can improve the poor physical condition in time.

[0048] Figure 7A schematic diagram of an exemplary sleep report 700 formed based on user sleep information according to some embodiments of the present disclosure is shown. After the sleep structure is analyzed based on the aforementioned embodiments, the mobile terminal presents the user sleep information in the sleep report 700 based on the processing results of the server, such as awake sleep stage, non-rapid eye movement sleep stage (including N1, N2, N3 sub-stages), rapid eye movement sleep stage, including the time and duration of each sleep stage, and the arrangement of multiple sleep stages in chronological order. The sleep report 700 presents the sleep situation in the time period T1~T5 in the form of a bar chart, and uses bar blocks with oblique lines, stars, horizontal lines, blanks, and cross lines to mark W, R, N1, N2, and N3 respectively. In addition, the sleep report 700 performs a general analysis of the entire sleep structure, and can determine the user's actual sleep time, sleep duration, sleep duration, and average sleep cycle.

[0049] Figure 8 FIG. 8 is a block diagram of a sleep monitoring device 800 according to some embodiments of the present disclosure. Figure 8 As shown, the device 800 includes an acquisition module 802, which is configured to acquire the sleep monitoring information of the user during the monitoring period, and the sleep monitoring information includes EEG data, EMG data, and EO data. The device 800 also includes a first determination module 804, which is configured to determine the user's sleep information based on the EEG data, EMG data, and EO data during the monitoring period, and the user's sleep information includes sleep time information and sleep monitoring information under awake sleep stages, non-rapid eye movement stages, and rapid eye movement stages. In addition, the device 800 also includes a second determination module 806, which is configured to determine the sleep time information and sleep monitoring information under the three sub-stages of the non-rapid eye movement sleep stage in the user's sleep information based on the EO data under the non-rapid eye movement sleep stage.

[0050] In some embodiments, the first determination module 804 includes a first response unit, which is configured to determine that the sleep time period corresponding to the EEG data corresponding to the continuous frames is the sleep time information of the awake sleep stage in response to the fact that the average frequency of the EEG waves of the continuous frames in the EEG data is not less than the frequency threshold. The first determination module 804 includes a second response unit, which is configured to determine that the sleep time period corresponding to the EEG data corresponding to the continuous frames having the minimum EMG voltage amplitude and containing the minimum EMG voltage amplitude in the EMG data is stable, and the eye movement speed of the electrooculogram data under the sleep time period corresponding to the EMG data is not less than the eye movement threshold, from the remaining sleep time periods in the monitoring cycle except the awake sleep stage, the sleep time information that the relevant sleep time period is the rapid eye movement sleep stage, and the other sleep time periods are the non-rapid eye movement sleep stage. In addition, the first determination module 804 also includes a determination unit, which is configured to determine the sleep monitoring information related to this sleep time period as the sleep monitoring information under the sleep stage corresponding to the sleep time period based on the determined sleep time period.

[0051] In some embodiments, the second determination module 806 includes a time extraction unit, which is configured to determine the start time of the first sub-stage and the end time of the third sub-stage of the non-rapid eye movement sleep stage between the adjacent waking sleep stage and the rapid eye movement sleep stage based on the sleep time information of the adjacent waking sleep stage and the rapid eye movement sleep stage in the monitoring cycle, or determine the end time of the first sub-stage and the start time of the third sub-stage of the non-rapid eye movement sleep stage between the adjacent waking sleep stage and the rapid eye movement sleep stage. The second determination module 806 includes a response unit configured to determine the sleep time information of the third substage and the sleep time information of the first substage of the non-rapid eye movement sleep stage between the adjacent awake sleep stage and the rapid eye movement sleep stage based on the changes in the electrooculogram voltage of the continuous frames in the electrooculogram data under the non-rapid eye movement sleep stage between the adjacent awake sleep stage and the rapid eye movement sleep stage in the monitoring cycle, and the start time of the first substage and the end time of the third substage of the non-rapid eye movement sleep stage or the end time of the first substage and the start time of the third substage of the non-rapid eye movement sleep stage. The second determination module 806 includes a time determination unit configured to determine the sleep time information of the second substage of the non-rapid eye movement sleep stage based on the sleep time information of the third substage and the sleep time information of the first substage of the non-rapid eye movement sleep stage between the adjacent awake sleep stage and the rapid eye movement sleep stage. In addition, the second determination module 806 includes a determination unit configured to determine the sleep monitoring information related to the sleep time information as the sleep monitoring information under the sub-stage of the non-rapid eye movement sleep stage based on the sleep time information determined in each sub-stage.

[0052] Fig. 9 9 is a schematic block diagram of an example device 900 that can be used to implement embodiments of the present disclosure. Fig. 9 As shown, the device 900 includes a processor 901, which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) 902 and loaded into a random access memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the device 900 can also be stored. The processor 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0053] The various processes and processing described above, such as method 200, may be performed by processor 901. For example, in some embodiments, method 200 may be implemented as a software program that is tangibly contained in a machine-readable medium. In some embodiments, part or all of the software program may be loaded and / or installed on device 900 via ROM 902. When the software program is loaded into RAM 903 and executed by processor 901, one or more actions of method 200 described above may be performed.

[0054] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip systems (SOCs), load programmable logic devices (CPLDs), and the like.

[0055] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0056] The present disclosure may be a method, an apparatus, a system and / or a program product. The program product may include a machine-readable storage medium on which are loaded machine-readable program instructions for executing various aspects of the present disclosure. The machine-readable program instructions described herein may be downloaded from the machine-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network and / or a wireless network. The network may include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or a network interface in each computing / processing device receives a machine-readable program instruction from the network, and forwards the machine-readable program instruction for storage in a machine-readable storage medium in each computing / processing device.

[0057] The machine program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The machine-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect through the Internet). In some embodiments, by using the state information of the machine-readable program instructions to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit may execute the machine-readable program instructions, thereby implementing various aspects of the present disclosure.

[0058] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In addition, although each operation is depicted in a specific order, this should be understood as requiring such operations to be performed in the specific order shown or in a sequential order, or requiring that all illustrated operations should be performed to obtain the desired result. In certain environments, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Certain features described in the context of separate embodiments can also be implemented in a single implementation in combination. On the contrary, the various features described in the context of a single implementation can also be implemented in multiple implementations individually or in any suitable sub-combination.

[0059] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. A sleep monitoring method, characterized in that: include: Acquire the user's sleep monitoring information during the monitoring period, wherein the sleep monitoring information includes electroencephalogram data, electromyography data, and electrooculography data; Determine user sleep information based on the EEG data, the EMG data, and the EO data in the monitoring period, wherein the user sleep information includes sleep time information and sleep monitoring information in awake sleep stages, non-rapid eye movement sleep stages, and rapid eye movement sleep stages; and Based on the electrooculogram data in the non-rapid eye movement sleep stage, the sleep time information and sleep monitoring information in the three sub-stages of the non-rapid eye movement sleep stage in the user sleep information are determined.

2. The method according to claim 1, wherein determining the user's sleep information based on the EEG data, the EMG data, and the EO data during the monitoring period comprises: In response to the average frequency of the brain waves of the continuous frames in the brain wave data being not less than the frequency threshold, determining that the sleep time period corresponding to the brain wave data corresponding to the continuous frames is the sleep time information of the wake-sleep stage; In response to the myoelectric voltage change corresponding to the continuous frames having the minimum myoelectric voltage amplitude and containing the minimum myoelectric voltage amplitude in the myoelectric data being stable, and the eye movement speed of the electrooculographic data in the sleep time period corresponding to the myoelectric data being not less than the eye movement threshold, determining the sleep time information that the relevant sleep time period is the rapid eye movement sleep stage, and the other sleep time periods are the non-rapid eye movement sleep stages from the remaining sleep time periods of the monitoring period except the awake sleep stage; as well as Based on the determined sleep time period, the sleep monitoring information related to the sleep time period is determined as the sleep monitoring information under the sleep stage corresponding to the sleep time period.

3. The method according to claim 2, wherein the process of determining whether the myoelectric voltage change corresponding to the continuous frames having the minimum myoelectric voltage amplitude and containing the minimum myoelectric voltage amplitude in the myoelectric data is stable comprises: Determine a minimum myoelectric voltage amplitude and two mutation points located before and after the minimum myoelectric voltage amplitude from the remaining sleep time period, wherein the mutation point refers to a point where the ratio of its voltage amplitude to the minimum myoelectric voltage amplitude is greater than an amplitude ratio threshold, and the amplitude ratio threshold is greater than or equal to 1.5; as well as In response to the average voltage amplitude in the sleep time period between the two mutation points being not greater than the amplitude threshold, it is determined that changes in myoelectric voltage corresponding to continuous frames in the sleep time period between the two mutation points are stable.

4. The method according to claim 3, wherein determining the minimum myoelectric voltage amplitude and two mutation points before and after the minimum myoelectric voltage amplitude from the remaining sleep time period comprises: determining a minimum myoelectric voltage amplitude from the remaining sleep time period; In response to not identifying the mutation point before or after the minimum myoelectric voltage amplitude, determining the second smallest myoelectric voltage amplitude from the remaining sleep time period; as well as In response to the two mutation points identified before and after the second smallest EMG voltage amplitude, the second smallest EMG voltage amplitude is determined to be the target minimum EMG voltage amplitude; otherwise, the above process is repeated to redetermine the Nth smallest EMG voltage amplitude and the two mutation points before and after the Nth smallest EMG voltage amplitude.

5. The method according to claim 1 or 2, wherein determining the sleep time information and sleep monitoring information under three sub-stages of the non-rapid eye movement sleep stage in the user sleep information based on the electrooculogram data under the non-rapid eye movement sleep stage comprises: Based on the sleep time information of each of the adjacent waking sleep stage and the REM sleep stage in the monitoring cycle, determine the start time of the first sub-stage and the end time of the third sub-stage of the non-REM sleep stage between the adjacent waking sleep stage and the REM sleep stage, or determine the end time of the first sub-stage and the start time of the third sub-stage of the non-REM sleep stage between the adjacent waking sleep stage and the REM sleep stage; Determine the sleep time information of the third substage and the sleep time information of the first substage of the non-rapid eye movement sleep stage between the adjacent waking sleep stage and the rapid eye movement sleep stage based on the changes in the electrooculogram voltage of consecutive frames in the electrooculogram data of the non-rapid eye movement sleep stage between the adjacent waking sleep stage and the rapid eye movement sleep stage, and the start time of the first substage and the end time of the third substage of the non-rapid eye movement sleep stage or the end time of the first substage and the start time of the third substage of the non-rapid eye movement sleep stage; Determine the sleep time information of the second substage of the non-rapid eye movement sleep stage based on the sleep time information of the third substage of the non-rapid eye movement sleep stage located between the adjacent awake sleep stage and the rapid eye movement sleep stage and the sleep time information of the first substage; as well as Based on the sleep time information determined in each sub-stage, the sleep monitoring information related to the sleep time information is determined as the sleep monitoring information under the sub-stage of the non-rapid eye movement sleep stage.

6. The method according to claim 5, wherein based on the changes in electrooculogram voltage of consecutive frames in the electrooculogram data of the non-rapid eye movement sleep stage located between the adjacent awake sleep stage and the rapid eye movement sleep stage, and the start time of the first sub-stage and the end time of the third sub-stage of the non-rapid eye movement sleep stage or the end time of the first sub-stage and the start time of the third sub-stage of the non-rapid eye movement sleep stage, determining the sleep time information of the third sub-stage of the non-rapid eye movement sleep stage and the sleep time information of the first sub-stage includes: In response to the electrooculogram voltage of consecutive frames in the electrooculogram data under the non-rapid eye movement sleep staging remaining unchanged, taking the end time or the start time of the third sub-stage of the non-rapid eye movement sleep staging as a reference, determining that the sleep time period corresponding to the electrooculogram data corresponding to the consecutive frames is the sleep time information of the third sub-stage of the non-rapid eye movement sleep staging; as well as In response to the average interval time between the positive and negative amplitudes of adjacent electrooculogram voltages of consecutive frames in the electrooculogram data under the non-rapid eye movement sleep staging being not less than an interval threshold, based on the start time or end time of the first sub-stage of the non-rapid eye movement sleep staging, the sleep time period corresponding to the electrooculogram data corresponding to the consecutive frames is determined to be the sleep time information of the first sub-stage of the non-rapid eye movement sleep staging.

7. The method according to claim 5, wherein determining the sleep time information and sleep monitoring information in three sub-stages of the non-rapid eye movement sleep stage based on the electrooculogram data in the non-rapid eye movement sleep stage further comprises: In response to the non-rapid eye movement sleep stage located between the adjacent waking sleep stage and the rapid eye movement sleep stage not having a third sub-stage, based on changes in electrooculogram voltage of consecutive frames in the electrooculogram data of the non-rapid eye movement sleep stage located between the adjacent waking sleep stage and the rapid eye movement sleep stage, and the start time or end time of the first sub-stage of the non-rapid eye movement sleep stage, determining that the sleep time period corresponding to the electrooculogram data is the sleep time information of the first sub-stage of the non-rapid eye movement sleep stage; as well as The sleep time information of a second sub-stage of the non-rapid eye movement sleep stage is determined based on the sleep time information of the first sub-stage of the non-rapid eye movement sleep stage and the sleep time information of the non-rapid eye movement sleep stage.

8. The method according to claim 1, wherein the sleep monitoring information further includes electrocardiogram data and blood oxygen saturation data, and the method further includes: Based on the sleep time information of the non-rapid eye movement sleep stage and the rapid eye movement sleep stage in the user sleep information, determining the electrocardiogram data and blood oxygen saturation data under the non-rapid eye movement sleep stage and the rapid eye movement sleep stage from the sleep monitoring information; Based on the electrocardiogram data and the blood oxygen saturation data in the non-rapid eye movement sleep stage and the rapid eye movement sleep stage, the sleep state of the user is determined, and the sleep state includes a normal sleep state and a hypoventilation sleep state.

9. The method according to claim 8, wherein determining the sleep state of the user based on the electrocardiogram data and the blood oxygen saturation data in the non-rapid eye movement sleep stage and the rapid eye movement sleep stage comprises: In response to the blood oxygen saturation data at multiple moments being not greater than the blood oxygen threshold, acquiring the electrocardiogram data corresponding to the multiple moments; as well as In response to a change rate of the heart rate of the ECG data at the multiple moments compared to the heart rate of the ECG data at the previous multiple moments exceeding an ECG change threshold, it is determined that the user is in a hypoventilation sleep state; otherwise, the user is in a normal sleep state.

10. A sleep monitoring device, characterized in that: include: An acquisition module is configured to acquire sleep monitoring information of a user during a monitoring period, wherein the sleep monitoring information includes electroencephalogram data, electromyography data, and electrooculography data; A first determination module is configured to determine user sleep information based on the EEG data, the EMG data, and the EO data in the monitoring period, wherein the user sleep information includes sleep time information and sleep monitoring information in awake sleep stages, non-rapid eye movement sleep stages, and rapid eye movement sleep stages; as well as The second determination module is configured to determine the sleep time information and sleep monitoring information under three sub-stages of the non-rapid eye movement sleep stage in the user sleep information based on the electrooculogram data under the non-rapid eye movement sleep stage.

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