A sleep health management system, method, and computer device

Abstract

The application discloses a sleep health management system, method and computer device. The sleep health management system comprises a data acquisition module and a sleep quality evaluation module. The data acquisition module is configured to acquire electroencephalogram data of a target object and a sleep quality index scale about the target object. The sleep quality evaluation module is configured to obtain a subjective sleep quality evaluation report about the target object according to the sleep quality index scale, obtain an objective sleep quality evaluation report about the target object according to the electroencephalogram data, and obtain a sleep quality evaluation report about the target object according to the subjective sleep evaluation report and the objective sleep evaluation report. The sleep health management system in the embodiment of the application combines a subjective and objective double evaluation system, and the objective evaluation is evaluated by using electroencephalogram data, so that the accuracy of positioning a sleep disorder type is improved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a sleep health management system, method and computer device. Background Technology

[0002] With the aging population and changing lifestyles, the number of people suffering from sleep disorders continues to expand. The elderly, due to declining physiological functions, chronic diseases, and psychological anxiety, are a high-risk group for sleep problems. Sleep disorders not only affect daytime mental state but also trigger health risks such as cardiovascular disease and cognitive decline. Therefore, sleep health management is necessary.

[0003] In related technologies, relying on subjective feedback or basic data (such as heart rate and turning over) makes it difficult to accurately identify the type of sleep disorder. Summary of the Invention

[0004] To address the aforementioned issues, this application provides a sleep health management system, method, and computer device to improve the accuracy of locating sleep disorder types.

[0005] The embodiments of this application disclose the following technical solutions: In a first aspect, embodiments of this application provide a sleep health management system, including: a data acquisition module and a sleep quality assessment module; The data acquisition module is configured to acquire the target object's electroencephalogram (EEG) data, as well as a sleep quality index scale for the target object. The sleep quality assessment module is configured to generate a subjective sleep quality assessment report for the target subject based on a sleep quality index scale; an objective sleep quality assessment report for the target subject based on electroencephalogram (EEG) data; and a comprehensive sleep quality assessment report for the target subject based on both the subjective and objective sleep assessment reports.

[0006] In one possible implementation, the sleep quality assessment module is configured to extract the percentage of delta waves in a preset sleep cycle when delta waves are detected in EEG data; compare the percentage data with a preset deep sleep delta wave percentage threshold; and generate an objective sleep quality assessment report based on the comparison results, which includes a conclusion on whether the target subject has insufficient deep sleep.

[0007] In one possible implementation, the sleep quality assessment module is configured to, when alpha waves are detected in EEG data, count the duration of alpha waves during a preset sleep monitoring period, compare the duration with a preset sleep alpha wave duration threshold, and generate an objective sleep quality assessment report based on the comparison results, which includes a conclusion on whether the target subject has difficulty falling asleep.

[0008] In one possible implementation, the sleep quality assessment module is configured to, when beta waves are detected in EEG data, count the frequency of beta waves occurring within a preset sleep cycle, compare the frequency with a preset stable sleep beta wave frequency threshold, and generate an objective sleep quality assessment report based on the comparison results, which includes a conclusion on whether the target subject has sleep interruptions.

[0009] In one possible implementation, the sleep quality assessment module is configured to generate an objective sleep quality assessment report for the target subject based on the Pittsburgh Sleep Quality Index and any one of the following: the proportion of delta waves, the duration of alpha waves, or the frequency of beta waves.

[0010] In one possible implementation, the sleep quality assessment module is configured to generate a subjective sleep quality assessment report for the target subject based on at least one of the target subject's sleep quality, sleep onset time, sleep duration, sleep efficiency, sleep disorders, hypnotic drugs, and daytime functioning.

[0011] Secondly, embodiments of this application provide a sleep health management method, the method comprising: Obtain the target subject's electroencephalogram (EEG) data and a sleep quality index scale for the target subject; A subjective sleep quality assessment report for the target subjects was obtained based on the sleep quality index scale; an objective sleep quality assessment report for the target subjects was obtained based on electroencephalogram (EEG) data. Based on the subjective sleep assessment report and the objective sleep assessment report, a sleep quality assessment report on the target subjects is obtained.

[0012] In one possible implementation, an objective sleep quality assessment report on the target subject is obtained based on electroencephalogram (EEG) data, including: When delta waves are detected in EEG data, the proportion of delta waves within a preset sleep cycle is extracted; this proportion is compared with a preset deep sleep delta wave proportion threshold, and an objective sleep quality assessment report is generated based on the comparison results, including a conclusion on whether the target subject has insufficient deep sleep; and / or, When alpha waves are detected in EEG data, the duration of alpha waves during a preset sleep monitoring period is calculated. This duration is compared to a preset alpha wave duration threshold for sleep onset, and an objective sleep quality assessment report is generated based on the comparison results, including a conclusion on whether the target subject has difficulty falling asleep; and / or, When beta waves are detected in EEG data, the frequency of beta waves occurring within a preset sleep cycle is statistically analyzed. The frequency is then compared with a preset stable sleep beta wave frequency threshold, and an objective sleep quality assessment report is generated based on the comparison results, which includes a conclusion on whether the target subject has sleep interruptions.

[0013] In one possible implementation, a subjective sleep quality assessment report for the target subject is obtained based on a sleep quality index scale, including: A subjective sleep quality assessment report is obtained based on seven factors: sleep quality, sleep onset time, sleep duration, sleep efficiency, sleep disorders, hypnotic medication, and daytime functioning.

[0014] Thirdly, embodiments of this application provide a computer device, including: a memory, a processor, a communication interface, a bus, and a computer program stored in the memory and executable on the processor; The memory is configured to store EEG acquisition data, a preset EEG feature analysis model, and computer programs; The processor is configured to be electrically connected to memory for calling and executing computer programs; The communication interface is configured to establish a data transmission link with an external EEG acquisition device and receive the user's frontal EEG signals acquired by the external EEG acquisition device. The bus is configured to connect to the memory, processor, and communication interface respectively, and is used to realize data interaction between the components. When the computer program is executed by the processor, it performs the following EEG signal analysis steps: filtering and noise reduction preprocessing on the EEG signals received by the communication interface, and extracting feature parameters of alpha waves, beta waves, theta waves, and delta waves; inputting the extracted feature parameters into the EEG feature analysis model, and quantifying and outputting indicators of the user's emotional state, sleep quality, and mental fatigue through a preset algorithm; generating a visual analysis report containing the above indicators, and feeding it back to the terminal display device through the communication interface.

[0015] To improve the accuracy of identifying sleep disorder types, this application provides a sleep health management system, including: a data acquisition module and a sleep quality assessment module; the data acquisition module is configured to acquire electroencephalogram (EEG) data of a target subject and a sleep quality index scale for the target subject; the sleep quality assessment module is configured to obtain a subjective sleep quality assessment report for the target subject based on the sleep quality index scale; obtain an objective sleep quality assessment report for the target subject based on the EEG data; and obtain a comprehensive sleep quality assessment report for the target subject based on both the subjective and objective sleep assessment reports. The sleep health management system in this application combines a dual "subjective + objective" evaluation system, with the objective assessment using EEG data, thereby improving the accuracy of identifying sleep disorder types. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A schematic diagram of a sleep health management system provided in an embodiment of this application; Figure 2 A schematic diagram illustrating a sleep health management method provided in an embodiment of this application; Figure 3 This is a schematic diagram of a computer device provided in an embodiment of this application. Detailed Implementation

[0018] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0019] The terms "first" and "second," etc., used in the specification and claims of this application are used to distinguish different objects, not to describe a specific order of objects. For example, "first operation instruction" and "second operation instruction," etc., are used to distinguish different operation instructions, not to describe a specific order of operation instructions.

[0020] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0021] In the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more, for example, multiple processing units means two or more processing units, multiple elements means two or more elements, etc.

[0022] The technical solution of this application will be described below with reference to the accompanying drawings.

[0023] See Figure 1 The figure is a schematic diagram of a sleep health management system provided in an embodiment of this application.

[0024] like Figure 1 As shown, the sleep health management system includes: a data acquisition module 1000 and a sleep quality assessment module 2000.

[0025] The data acquisition module 1000 is configured to acquire the target object's electroencephalogram (EEG) data and a sleep quality index scale about the target object.

[0026] It should be noted that the acquisition of EEG data is carried out with the consent of the target subject, and the embodiments of this application do not specifically limit the method of acquiring EEG data. For example, electrodes coated with conductive paste are placed on the scalp, and the electrodes are connected to a computer through wires to record the activity of brain waves and thus obtain EEG data.

[0027] The sleep quality assessment module 2000 is configured to generate a subjective sleep quality assessment report for the target subject based on the sleep quality index scale; an objective sleep quality assessment report for the target subject based on electroencephalogram (EEG) data; and a sleep quality assessment report for the target subject based on both the subjective and objective sleep assessment reports.

[0028] The sleep health management system in this application combines a "subjective + objective" dual evaluation system, and the objective evaluation uses electroencephalogram (EEG) data to improve the accuracy of locating the type of sleep disorder.

[0029] The following section will introduce the process of generating a sleep quality assessment report using the sleep instruction assessment module.

[0030] In one possible implementation, a subjective sleep quality assessment report is obtained based on seven factors: sleep quality, sleep onset time, sleep duration, sleep efficiency, sleep disorders, hypnotic drugs, and daytime functioning.

[0031] The sleep quality index scale can be the Pittsburgh Sleep Quality Index (PSQI) scale, which includes the following items: 1. What time do you usually go to bed in the past month? 2. How long does it usually take you to fall asleep each night over the past month? 3. What time do you usually get up every day in the past month? 4. How much sleep did you actually get each day in the past month? 5. Have you had trouble sleeping in the past month due to any of the following issues (A: unable to fall asleep within 30 minutes; B: waking up during the night or waking up early; C: getting up to urinate at night; D: uncomfortable breathing; E: loud coughing or snoring; F: feeling cold; G: feeling too hot; H: having nightmares; I: experiencing pain; J: other things that affect sleep).

[0032] 6. Sleep quality score over the past month.

[0033] 7. Use of hypnotic drugs in the past month.

[0034] 8. Have you had difficulty staying alert while driving, eating, or participating in social activities in the past month?

[0035] 9. Have you encountered any difficulties in actively completing tasks over the past month?

[0036] 10. Do you share a bed with someone or have a roommate?

[0037] 11. Do you snore while sleeping?

[0038] 12. When you sleep, are there any long pauses in your breathing?

[0039] 13. Do your legs twitch or spasm while you sleep?

[0040] 14. Do you experience disorientation or confusion while sleeping?

[0041] 15. Do you experience any other restlessness or difficulty sleeping?

[0042] In the above projects, projects 1 and 2 correspond to sleep onset time, projects 2 and 4 correspond to sleep duration, project 6 corresponds to sleep quality, projects 5, 11, 12, 13, 14, and 15 correspond to sleep disorders, project 7 corresponds to hypnotic drugs, and project 8 corresponds to daytime functioning. It should be noted that none of the projects directly correspond to sleep efficiency, but sleep efficiency can be calculated based on projects 1, 3, and 4.

[0043] The target individuals can, based on their own circumstances, have professionals select and fill in the corresponding information for the items on the PSQI scale. The system will then use the selected information and the information provided by the professionals to obtain the corresponding PSQI scale score for the target individual, and thus generate a subjective sleep assessment report for the target individual. This report mainly reflects the target individual's subjective sleep quality.

[0044] In one possible implementation, the sleep quality assessment module is configured to extract the percentage of delta waves in a preset sleep cycle when delta waves are detected in the EEG data; compare the percentage data with a preset deep sleep delta wave percentage threshold; and generate an objective sleep quality assessment report based on the comparison results, which includes a conclusion on whether the target subject has insufficient deep sleep.

[0045] Delta waves are the lowest frequency (range 0.5–4 Hz) and highest amplitude (range 100–200 μV) brain waves generated by the human cerebral cortex.

[0046] Wake phase: Delta waves completely disappear, with beta waves (13-30Hz) and alpha waves (8-13Hz) dominating.

[0047] During the sleep onset period (N1 stage): delta waves are rarely seen, with theta waves (4-7 Hz) and residual alpha waves being the main features.

[0048] Light sleep stage (N2 stage): delta waves account for less than 20%, mainly consisting of sleep spindle waves (12-14Hz) and K-complex waves.

[0049] Deep sleep (N3 stage): Delta waves account for ≥20% and have the largest amplitude, making them the dominant brainwaves during deep sleep.

[0050] Rapid eye movement (REM) sleep: delta waves completely disappear, and the brain wave pattern is similar to that of wakefulness (mainly beta waves), accompanied by rapid eye movements and dreams.

[0051] For example, the proportion of delta wave (0.5~4Hz) duration to total sleep time (preset sleep cycle) in a normal adult is 15%~25% deep sleep. A proportion <15% is considered insufficient deep sleep; otherwise, there is no insufficient deep sleep.

[0052] In one possible implementation, the sleep quality assessment module is configured to, when alpha waves are detected in EEG data, count the duration of alpha waves during a preset sleep monitoring period, compare the duration with a preset sleep alpha wave duration threshold, and generate an objective sleep quality assessment report based on the comparison results, which includes a conclusion on whether the target subject has difficulty falling asleep.

[0053] Alpha waves are brain waves with a frequency range of 8–13 Hz and an amplitude range of 20–100 μV.

[0054] Wake period: When the eyes are closed, alpha waves dominate (especially in the occipital lobe). When the eyes are open or stimulated, alpha waves are blocked and beta waves dominate.

[0055] Sleep onset period (N1 stage, early stage of light sleep): The proportion of alpha waves gradually decreases, while the proportion of theta waves (4-7Hz) gradually increases. When the proportion of alpha waves is less than 50%, it marks the transition from wakefulness to sleep.

[0056] Light sleep stage (N2 stage): alpha waves basically disappear, and sleep spindle waves (12-14Hz) and K-complex waves appear.

[0057] Deep sleep (N3 stage): Alpha waves completely disappear, and high-amplitude delta waves (0.5-4Hz) dominate.

[0058] Rapid eye movement (REM) sleep: Alpha waves appear in small amounts, and the brain wave pattern is similar to that of wakefulness (mainly beta waves), but accompanied by rapid eye movements and dreams.

[0059] For example, if the time from lying down with eyes closed to the alpha wave percentage being <50% (entering the N1 sleep stage, i.e. the preset sleep monitoring period) lasts for more than 30 minutes, it is considered difficulty falling asleep; otherwise, there is no difficulty falling asleep.

[0060] In one possible implementation, the sleep quality assessment module is configured to, when beta waves are detected in EEG data, count the frequency of beta waves occurring within a preset sleep cycle, compare the frequency with a preset sleep stability beta wave frequency threshold, and generate an objective sleep quality assessment report based on the comparison results, including a conclusion on whether the target subject has sleep interruptions.

[0061] Among them, beta waves are high-frequency (frequency range of 13-30Hz) and low-amplitude (amplitude range of 5-20μV) brain waves generated by the human cerebral cortex, which are mainly related to the brain's waking activities, cognitive processing and alertness.

[0062] Wake state: Beta waves dominate (especially high beta waves), accompanied by alpha waves (when eyes are closed and relaxed), which is a normal physiological state.

[0063] Sleep onset period (N1 stage): The proportion of beta waves gradually decreases, while the proportion of alpha and theta waves increases. If beta waves persist, it indicates difficulty falling asleep (the brain is unable to enter a relaxed state).

[0064] Light sleep stage (N2 stage): Beta waves should completely disappear, with sleep spindle waves and K-complex waves as the main components; if a beta wave burst occurs, it indicates sleep interruption (local brain arousal).

[0065] Deep sleep (N3 stage): Beta waves completely disappear, and delta waves dominate; the presence of beta waves indicates that deep sleep has been interrupted, which is a serious sign of difficulty in maintaining sleep.

[0066] Rapid eye movement (REM) sleep: The brain wave pattern is similar to that of wakefulness (mainly beta waves), but this is a normal physiological phenomenon (accompanied by dreams) and needs to be distinguished from the beta wave bursts in non-REM sleep (the core difference is that beta waves in the REM stage are continuous rather than bursty).

[0067] For example, the number of beta wave bursts (13~30Hz) per hour during sleep (N1~REM) is as follows: beta waves should appear very rarely during normal sleep. A frequency > 5 times / hour is considered sleep interruption (beta wave bursts indicate local brain arousal, i.e. difficulty in maintaining sleep); conversely, there is no sleep interruption.

[0068] In one possible implementation, the sleep quality assessment module is configured to generate an objective sleep quality assessment report for the target subject based on the Pittsburgh Sleep Quality Index and any one of the following: the proportion of delta waves, the duration of alpha waves, or the frequency of beta waves.

[0069] In this embodiment of the application, the sleep quality assessment report includes changes in PSQI scale scores, EEG sleep structure analysis, disorder type determination, and preliminary assessment of intervention effects.

[0070] As shown in Table 1 below: Table 1

[0071] It should be understood that the methods for determining whether EEG data is normal or abnormal, and the methods for determining whether PSQI scale scores are normal or abnormal, have already been described in the preceding embodiments, and will not be repeated here. The threshold for determining whether PSQI scale scores are normal or abnormal can be set or modified according to the specific circumstances of the target subject.

[0072] In this embodiment, sleep quality of the target subjects is assessed using EEG data and the PSQI scale. Combined with a dual "subjective + objective" evaluation system, the type of sleep disorder can be directly identified, improving the accuracy of sleep disorder localization and providing a precise basis for personalized treatment plans. Furthermore, non-pharmacological interventions can be used throughout the treatment process for the corresponding sleep disorder type, with CBT-i as the core, combined with physical, psychological, and sensory therapies. This avoids drug side effects and dependence, while simultaneously changing sleep patterns from a "cognitive-behavioral" perspective, achieving a deep improvement from "symptom relief" to "cognitive restructuring." A structured process of "1 initial assessment + 5 interventions" is designed with a 1-6 month cycle, simultaneously conducting full data tracking and plan adjustments. This addresses the problems of non-cyclical and unsustainable effects of existing technical services, ensuring stable sleep improvement. It is not only suitable for elderly people in nursing homes but can also be extended to family members and middle-aged individuals with sleep disorders (such as insomnia caused by workplace anxiety). By adjusting the frequency of EEG monitoring and the depth of CBT-i intervention, it can meet the needs of different groups, thus broadening its applicability.

[0073] Based on the sleep health management system described in the foregoing embodiments, this application provides a sleep health management method.

[0074] See Figure 2 Sleep health management methods include the following steps: S1000: Acquire the target subject's electroencephalogram (EEG) data and a sleep quality index scale for the target subject.

[0075] S2000: Based on the Sleep Quality Index scale, a subjective sleep quality assessment report on the target subject is obtained; based on electroencephalogram (EEG) data, an objective sleep quality assessment report on the target subject is obtained.

[0076] S3000: Based on subjective and objective sleep assessment reports, a sleep quality assessment report for the target subject is obtained.

[0077] The sleep health management system in this application combines a "subjective + objective" dual evaluation system, and the objective evaluation uses electroencephalogram (EEG) data to improve the accuracy of locating the type of sleep disorder.

[0078] In one possible implementation, an objective sleep quality assessment report on the target subject is obtained based on electroencephalogram (EEG) data, including: When delta waves are detected in EEG data, the proportion of delta waves within a preset sleep cycle is extracted; this proportion is compared with a preset deep sleep delta wave proportion threshold, and an objective sleep quality assessment report is generated based on the comparison results, including a conclusion on whether the target subject has insufficient deep sleep; and / or, When alpha waves are detected in EEG data, the duration of alpha waves during a preset sleep monitoring period is calculated. This duration is compared to a preset alpha wave duration threshold for sleep onset, and an objective sleep quality assessment report is generated based on the comparison results, including a conclusion on whether the target subject has difficulty falling asleep; and / or, When beta waves are detected in EEG data, the frequency of beta waves occurring within a preset sleep cycle is statistically analyzed. The frequency is then compared with a preset stable sleep beta wave frequency threshold, and an objective sleep quality assessment report is generated based on the comparison results, which includes a conclusion on whether the target subject has sleep interruptions.

[0079] In one possible implementation, a subjective sleep quality assessment report for the target subject is obtained based on a sleep quality index scale, including: A subjective sleep quality assessment report is obtained based on seven factors: sleep quality, sleep onset time, sleep duration, sleep efficiency, sleep disorders, hypnotic medication, and daytime functioning.

[0080] In one possible implementation, an objective sleep quality assessment report for the target subject is obtained based on the Pittsburgh Sleep Quality Index and any one of the following: the proportion of delta waves, the duration of alpha waves, or the frequency of beta waves.

[0081] See Figure 3 This figure is a schematic diagram of a computer device provided in an embodiment of this application.

[0082] The computer device may include a memory 3100, a processor 3200, a communication interface 3300, a bus 3400, and a computer program stored in the memory and executable on the processor; The memory 3100 is configured to store EEG acquisition data, a preset EEG feature analysis model, and a computer program; The processor 3200 is configured to be electrically connected to the memory for calling and executing computer programs; The communication interface 3300 is configured to establish a data transmission link with an external EEG acquisition device and receive the user's frontal EEG signals acquired by the external EEG acquisition device. Bus 3400 is configured to be connected to memory 3100, processor 3200 and communication interface 3300 respectively to realize data interaction between the components; When the computer program is executed by the processor 3200, it performs the following EEG signal analysis steps: preprocessing the EEG signals received by the communication interface by filtering and noise reduction, and extracting feature parameters of alpha waves, beta waves, theta waves, and delta waves; inputting the extracted feature parameters into the EEG feature analysis model, and quantifying and outputting indicators of the user's emotional state, sleep quality, and mental fatigue through a preset algorithm; generating a visual analysis report containing the above indicators, and feeding it back to the terminal display device through the communication interface.

[0083] The memory 3100 can be random access memory (RAM), flash memory, read-only memory (ROM), EPROM, non-volatile read-only memory (Electronic Programmable ROM), register, hard disk, removable disk, etc.

[0084] The memory 3100 can store computer instructions. When the computer instructions stored in the memory 3100 are executed by the processor 3200, the processor 3200 can use them to control the sleep health management method. The memory 3100 can also store data, such as preset ranges, preset thresholds, and other information involved in the above embodiments.

[0085] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape) or a semiconductor medium (e.g., solid-state disk (SSD)).

[0086] This application also provides a readable storage medium for storing the methods provided in the above embodiments. Examples include random access memory (RAM), flash memory, read-only memory (ROM), EPROM, non-volatile read-only memory (EPROM), registers, hard disks, removable disks, or any other form of storage medium in the art.

[0087] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. Regarding the methods disclosed in the embodiments, since they correspond to the product embodiments disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the description of the product embodiments.

[0088] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A sleep health management system, characterized in that, include: Data acquisition module and sleep quality assessment module; The data acquisition module is configured to acquire the electroencephalogram (EEG) data of the target object, as well as a sleep quality index scale for the target object. The sleep quality assessment module is configured to obtain a subjective sleep quality assessment report for the target subject based on the sleep quality index scale; to obtain an objective sleep quality assessment report for the target subject based on the electroencephalogram (EEG) data; and to obtain a sleep quality assessment report for the target subject based on the subjective sleep assessment report and the objective sleep assessment report.

2. The system according to claim 1, characterized in that, The sleep quality assessment module is configured to, when delta waves are detected in the EEG data, extract the percentage data of delta waves in a preset sleep cycle; compare the percentage data with a preset deep sleep delta wave percentage threshold, and generate an objective sleep quality assessment report based on the comparison results, which includes a conclusion on whether the target subject has insufficient deep sleep.

3. The system according to claim 1, characterized in that, The sleep quality assessment module is configured to, when alpha waves are detected in the EEG data, count the duration of the alpha waves during a preset sleep monitoring period, compare the duration with a preset sleep alpha wave duration threshold, and generate an objective sleep quality assessment report based on the comparison results, which includes a conclusion on whether the target subject has difficulty falling asleep.

4. The system according to claim 1, characterized in that, The sleep quality assessment module is configured to, when beta waves are detected in the EEG data, count the frequency of the beta waves in a preset sleep cycle, compare the frequency of the beta waves with a preset sleep stability beta wave frequency threshold, and generate an objective sleep quality assessment report based on the comparison results, which includes a conclusion on whether the target subject has sleep interruptions.

5. The system according to any one of claims 2-4, characterized in that, The sleep quality assessment module is configured to generate an objective sleep quality assessment report for the target subject based on the Pittsburgh Sleep Quality Index and any one of the following: the proportion of delta waves, the duration of alpha waves, or the frequency of beta waves.

6. The system according to claim 1, characterized in that, The sleep quality assessment module is configured to generate a subjective sleep quality assessment report for the target subject based on seven factors: sleep quality, sleep onset time, sleep duration, sleep efficiency, sleep disorders, hypnotic drugs, and daytime function.

7. A sleep health management method, characterized in that, The method includes: Obtain the electroencephalogram (EEG) data of the target subject, as well as a sleep quality index scale for the target subject; A subjective sleep quality assessment report for the target subject is obtained based on the sleep quality index scale; an objective sleep quality assessment report for the target subject is obtained based on the electroencephalogram (EEG) data. Based on the subjective sleep assessment report and the objective sleep assessment report, a sleep quality assessment report for the target subject is obtained.

8. The method according to claim 7, characterized in that, The objective sleep quality assessment report for the target subject, obtained based on the electroencephalogram (EEG) data, includes: If delta waves are detected in the EEG data, the proportion of delta waves within a preset sleep cycle is extracted; the proportion data is compared with a preset deep sleep delta wave proportion threshold, and an objective sleep quality assessment report containing a conclusion on whether the target subject has insufficient deep sleep is generated based on the comparison results; and / or, If alpha waves are detected in the EEG data, the duration of the alpha waves within a preset sleep monitoring period is calculated, and the duration is compared with a preset sleep alpha wave duration threshold. Based on the comparison results, an objective sleep quality assessment report is generated, including a conclusion on whether the target subject has difficulty falling asleep; and / or, When beta waves are detected in the EEG data, the frequency of the beta waves in a preset sleep cycle is counted, the frequency of the beta waves is compared with a preset sleep stability beta wave frequency threshold, and an objective sleep quality assessment report containing a conclusion on whether the target subject has sleep interruption is generated based on the comparison results.

9. The method according to claim 7, characterized in that, The process of obtaining a subjective sleep quality assessment report for the target subject based on the sleep quality index scale includes: A subjective sleep quality assessment report is obtained based on seven factors: sleep quality, sleep onset time, sleep duration, sleep efficiency, sleep disorders, use of hypnotic drugs, and daytime functioning.

10. A computer device, characterized in that, include: Memory, processor, communication interface, bus, and computer program stored in the memory and executable on the processor; The memory is configured to store EEG acquisition data, a preset EEG feature analysis model, and a computer program. The processor is configured to be electrically connected to the memory for calling and executing the computer program; The communication interface is configured to establish a data transmission link with an external EEG acquisition device and receive the user's frontal EEG signals acquired by the external EEG acquisition device. The bus is configured to be connected to the memory, the processor and the communication interface respectively, for realizing data interaction between the components; When the computer program is executed by the processor, it performs the following EEG signal analysis steps: preprocessing the EEG signals received by the communication interface by filtering and noise reduction, and extracting feature parameters of alpha waves, beta waves, theta waves, and delta waves; inputting the extracted feature parameters into the EEG feature analysis model, and quantifying and outputting indicators of the user's emotional state, sleep quality, and mental fatigue through a preset algorithm; generating a visual analysis report containing the above indicators, and feeding it back to the terminal display device through the communication interface.

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