WO2021180028A1 - 基于高频脑电睡眠质量评价方法、装置、设备和存储介质 - Google Patents

基于高频脑电睡眠质量评价方法、装置、设备和存储介质 Download PDF

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WO2021180028A1
WO2021180028A1 PCT/CN2021/079528 CN2021079528W WO2021180028A1 WO 2021180028 A1 WO2021180028 A1 WO 2021180028A1 CN 2021079528 W CN2021079528 W CN 2021079528W WO 2021180028 A1 WO2021180028 A1 WO 2021180028A1
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sleep
frequency
eeg
different
sleep quality
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PCT/CN2021/079528
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French (fr)
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姜建
张洪钧
蒲慕明
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中国科学院脑科学与智能技术卓越创新中心
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4806—Sleep evaluation
    • A61B5/4815—Sleep quality
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4806—Sleep evaluation
    • A61B5/4812—Detecting sleep stages or cycles
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2576/00—Medical imaging apparatus involving image processing or analysis
    • A61B2576/02—Medical imaging apparatus involving image processing or analysis specially adapted for a particular organ or body part
    • A61B2576/026—Medical imaging apparatus involving image processing or analysis specially adapted for a particular organ or body part for the brain

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  • This application relates to the technical field of sleep quality evaluation, and in particular to a method, device, equipment and storage medium for evaluating sleep quality based on high-frequency brain electricity.
  • EEG plays the most important role in sleep staging and evaluation.
  • the current sleep evaluation method based on EEG mainly uses low-frequency signals ( ⁇ 35Hz).
  • low-frequency signals ⁇ 35Hz
  • High-frequency EEG is the objective performance of performing cognitive functions and conscious activities of the brain, so it is more consistent with the subjective feelings of a person's waking state and sleeping state [2-5].
  • the past analysis methods cannot use high-frequency EEG signals for sleep quality evaluation, or only use part of the high-frequency frequency band to perform statistics on large-scale groups.
  • the purpose of this application is to provide a method, device, equipment and storage medium for evaluating sleep quality based on high-frequency EEG to solve the problems in the prior art.
  • this application provides a method for evaluating sleep quality based on high-frequency EEG.
  • the method includes: obtaining EEG signals generated during sleep, preprocessing them, and staging them into different sleep stages ; According to the frequency domain filtering method, separate the EEG signals of different sleep stages that contain high-frequency EEG signals of different frequency bands, and calculate the sleep quality parameters corresponding to the different sleep stages according to the high-frequency energy relative entropy algorithm; The quality reference data is used to evaluate the sleep quality parameters.
  • the frequency domain filtering method includes: dividing the EEG signal of each sleep stage into a plurality of time periods using a time window of a preset length; The frequency spectrum of the time period is removed, and the time period with abnormal frequency spectrum is removed; the different frequency components of each time period are separated according to the Butterworth filter, so as to extract and obtain high-frequency EEG signals containing various high-frequency frequency bands.
  • the high-frequency EEG signals are scalp or intracranial EEG signals with a frequency range between 10 Hz and 50 Hz, and between 50 Hz and 100 Hz.
  • the method for the relative entropy of high-frequency energy includes: calculating the average energy value within each time window in the high-frequency EEG signals of the different sleep stages; and according to the average The energy value calculates the relative entropy of the high-frequency EEG signals corresponding to the different sleep stages, and uses them as the sleep quality parameter.
  • the preprocessing method includes: filtering the EEG signal to remove characteristic frequency band interference; and/or, removing noise signals in the EEG signal through identification.
  • the method for staging the pre-processed EEG signals into different sleep stages includes: obtaining the energy and frequency characteristics of low-frequency EEG signals in the pre-processed EEG signals , And according to the American sleep staging standard or European sleep staging standard for staging.
  • the sleep quality reference data includes: sleep quality data based on healthy people or historical sleep quality of subjects.
  • the present application provides an electronic device, the device includes: an acquisition module for acquiring brain electrical signals generated during sleep, pre-processing and staging them into different sleep stages; processing; The module is used to separate the EEG signals of different sleep stages including high frequency EEG signals of different frequency bands according to the frequency domain filtering method, and calculate the sleep quality parameters corresponding to the different sleep stages according to the high frequency energy relative entropy algorithm ; According to the sleep quality reference data, the sleep quality parameters are evaluated.
  • this application provides an electronic device that includes: a memory, a processor, and a communicator; the memory is used to store computer instructions; the processor runs the computer instructions to achieve the above The method; the communicator is used to communicate with external devices.
  • this application provides a non-transitory computer-readable storage medium that stores computer instructions that execute the above-mentioned method when the computer instructions are executed
  • a method, device, equipment and storage medium for evaluating sleep quality based on high-frequency EEG of the present application are used to obtain EEG signals generated during sleep, preprocess them and stage them into different sleep stages;
  • the EEG signals of different sleep stages are separated from the high-frequency EEG signals of different frequency bands, and the sleep quality parameters corresponding to the different sleep stages are calculated according to the high-frequency energy relative entropy algorithm; according to the sleep quality Refer to the data to evaluate the sleep quality parameters.
  • the algorithm standard in the method for evaluating sleep quality based on high-frequency EEG described in this application is clear and is suitable for health big data applications.
  • the analysis result is closer to the subject's own sleep experience, and considerable social and economic benefits are expected.
  • FIG. 1 shows a schematic flowchart of a method for evaluating sleep quality based on high-frequency EEG in an embodiment of the present application.
  • Fig. 2 shows a broken line diagram of the sleep quality evaluation of an individual complaining of good sleep and an individual complaining of poor sleep in an embodiment of the present application.
  • FIG. 3 shows a schematic diagram of modules of an electronic device in an embodiment of the present application.
  • FIG. 4 shows a schematic structural diagram of an electronic device in an embodiment of the application.
  • the method described in this application is suitable for the evaluation of patients with sleep disorders and emotional disorders with sleep problems.
  • By collecting the EEG signals during the user's sleep using traditional or existing methods to stage the sleep EEG signals, extract the high-frequency signals of each sleep stage, and generate the sleep quality of each subject in real time through the high-frequency signals Parameters to evaluate the subjects’ sleep quality.
  • FIG. 1 shows a schematic flowchart of a method for evaluating sleep quality based on high-frequency EEG in an embodiment of the present application. As shown in the figure, the method includes:
  • Step S101 Obtain the EEG signal generated during sleep, preprocess it and stage it into different sleep stages.
  • the EEG signal may be the scalp EEG of the subject or the user during sleep or free movement, for example, collected by one or more electrodes.
  • the EEG signal may be collected by the EEG signal collector on the sleep process of the user under test.
  • the EEG signal collector may be a common device that can collect EEG signals including EMI filters, amplifiers, samplers and other devices.
  • the method described in this application may further include obtaining other indicator signals that can characterize the sleep process, such as body motion signals, respiration signals, ECG signals, etc., to assist in the evaluation. Specifically, it can be used for cross-cutting analysis and applications, such as the prevention of sudden death in sleep, or the analysis of the effects of hyperactivity or sleepwalking on sleep quality during sleep.
  • the preprocessing method includes: filtering the EEG signal to remove characteristic frequency band interference; and/or, removing noise signals in the EEG signal through recognition.
  • the acquired EEG signal is rough, and it is not reliable and accurate to analyze it directly, so it does not need to be processed to a certain extent.
  • the noise may be noise caused by eye movement, body movement, electromagnetic interference and the like.
  • This application is not limited to the filtering processing and/or noise removal processing mentioned above, for example, it may also include signal amplification processing.
  • the method of staging the pre-processed EEG signals into different sleep stages includes:
  • non-rapid eye movement sleep also known as normal phase sleep, slow wave sleep, synchronized sleep, quiet sleep, NREM sleep
  • rapid eye movement sleep also known as Paradoxical sleep, fast wave sleep, desynchronized sleep, active sleep, REM sleep, also known as the Rem phase phenomenon
  • NREM sleep is a dynamic process, and the non-rapid eye movement (NREM) period and the rapid eye movement (REM) period alternate. NREM sleep time accounts for about 75% to 80%, and REM sleep time accounts for about 20% to 25%.
  • NREM1 sleep-non-rapid eye movement 1 stage
  • NREM2 N2 stage sleep-non-rapid eye movement 2
  • NREM3 sleep-non-rapid eye movement stage 3
  • the changes in the various phases corresponding to the general population are usually: people start to fall asleep, first in the N1, N2, and N3 phases, and then transition to the REM phase through N2. This is the first cycle; the second cycle is N2, N3, N2, REM; and then N2, N3, N2, REM; and so on, there are about 5 cycles in one night, and each cycle is about 90 minutes.
  • N3 accounted for a high proportion in the first half of the night, N3 less and less in the second half of the night, and more and more REM.
  • the examples are illustrated here and the standard or corresponding to each person, but the general changes in each period are similar, and this is for reference only.
  • the division of each period is mainly based on the EEG waveform.
  • the main waveforms include the following:
  • Alpha rhythm wave W-phase closed eyes state.
  • Alpha wave frequency 8 ⁇ 13Hz, mainly seen in the state of quiet and awake eyes closed and REM phase, NI phase ⁇ 50%. It disappears when the eyes are opened, and the REM period is 1 to 2 Hz slower than the awake period, and there is no amplitude and morphological standard, and the appearance is often gradually increasing and decreasing. The frequency and amplitude of the elderly are reduced. It is also seen in the state of lethargy. Some patients with severe OSAHS or severe left ventricular insufficiency may have low-frequency alpha waves for most of the night in PSG. Mainly in the occipital area.
  • ⁇ wave Eye open state in W phase.
  • ⁇ wave It is mainly seen in the state of awake period with open eyes, low potential wave with frequency >13Hz, without amplitude and morphological standards. Sometimes it also occurs in the N2 sleep period, and more often after taking sleeping pills.
  • Theta wave late stage N1.
  • the frequency is 4 ⁇ 7Hz, there is generally no amplitude and morphological standard, but the amplitude is usually >50uV, sometimes (especially in children and adolescents) it is a short array of high-voltage waves, which need to be distinguished from epileptic waves.
  • Some researchers call them benign Transitional waves of epileptiform sleep (BETS). The central part is obvious.
  • the appearance is regular, the frequency is 11-16Hz (the most common 12-14Hz) is a clearly distinguishable waveform appearing in series, the duration is ⁇ 0.5 seconds, and the amplitude ⁇ > is mainly in the N2 and N3 sleep period, which is the characteristic brain wave of the N2 period , Usually the largest amplitude recorded in the central lead. Spindle waves increased significantly in patients taking stabilizers.
  • K complex wave N2 period characteristic brain wave, no frequency standard, stand out from the background. It is a clearly recognizable steep negative wave (upward) followed by a positive wave (downward), which is highlighted in the background EEG, with a duration of ⁇ 0.5 seconds.
  • the K complex wave is usually the most obvious in the frontal brain electrical lead recording, and it appears in the N2 and N3 phases. It can also be used as the brain's response to the outside (sound) or internal stimuli (apnea) during sleep.
  • the alpha wave appears within 1 second after the K-complex is over, which is called arousal-related K-complex. The awakening related to the K-complex, the starting point and the cut-off point of the K-complex cannot be greater than 1s.
  • Low-frequency delta slow wave N3 period or N4 period.
  • Delta wave (slow wave sleep) low frequency (range 0.5-2Hz), high wave amplitude ( ⁇ 75uV), which can be monitored in the entire frontal area.
  • N2 stage sleep is less than 20%
  • N3 stage sleep delta wave accounts for 20%-50%
  • N4 stage sleep delta wave accounts for more than 50%.
  • Sawtooth wave REM period.
  • Sawtooth wave A sequence of steep waves or triangular waveforms, similar to a sawtooth shape, with a frequency of 2 to 6 Hz. It appears in bursts, essentially theta waves, and ⁇ 50 ⁇ V often appears before the paroxysmal rapid eye movement waves.
  • Low voltage mixed frequency wave Rhythmic wave not higher than 10 ⁇ V And a single wave not higher than 20 ⁇ V.
  • Step S102 Separate the EEG signals of different sleep stages including high frequency EEG signals of different frequency bands according to the frequency domain filtering method, and calculate the sleep quality parameters corresponding to the different sleep stages according to the high frequency energy relative entropy algorithm.
  • this method After analyzing the EEG signals generated during sleep according to a common staging method, this method will extract high frequencies from the EEG signals.
  • the high-frequency EEG signals are scalp or intracranial EEG signals with a frequency range between 10 Hz and 50 Hz, and between 50 Hz and 100 Hz.
  • the frequency domain filtering method includes:
  • A. Use a time window of a preset length to divide the EEG signal of each sleep stage into multiple time periods.
  • the time window with a predetermined length is preferably a time window with a length of 10-30s.
  • the calculation formula is:
  • n is the sampling time point
  • N is the total number of sampling points
  • x n is the EEG sampling value at the time point n.
  • ⁇ and e are the pi and natural constant respectively
  • i is the imaginary number
  • X k is the Fourier coefficient under the wave vector k.
  • Spectrum is the abbreviation of frequency spectral density, and is the distribution curve of frequency.
  • Complex oscillations are decomposed into resonant oscillations with different amplitudes and frequencies.
  • the graphs in which the amplitudes of these resonant oscillations are arranged according to frequency are called frequency spectra.
  • Spectrum is widely used in acoustics, optics, and radio technology.
  • the frequency spectrum introduces the study of signals from the time domain to the frequency domain, thereby bringing a more intuitive understanding.
  • the spectrum decomposed into complex mechanical vibration is called the mechanical vibration spectrum
  • the spectrum decomposed into acoustic vibration is called the acoustic spectrum
  • the spectrum decomposed into light vibration is called the spectrum
  • the spectrum decomposed into electromagnetic vibration is called the electromagnetic spectrum.
  • the spectrum is often included in the range of the electromagnetic spectrum. Analyzing the frequency spectrum of various vibrations can understand many basic properties of the complex vibration. Therefore, frequency spectrum analysis has become a basic method for analyzing various complex
  • the different frequencies/frequency bands of the high frequencies are divided instead of unified processing.
  • the Butterworth filter is a kind of electronic filter.
  • the characteristic of the Butterworth filter is that the frequency response curve of the passband is the smoothest.
  • the specific calculation formula includes:
  • H represents the transfer function
  • z refers to the z-domain representation of the signal
  • a and b respectively represent the recursive coefficients
  • n represents the order of the filter.
  • the method for relative entropy of high-frequency energy includes:
  • the time window corresponds to the above-mentioned preferred time window of 10-30s.
  • the specific calculation formula includes:
  • i is the characteristic parameter of the EEG signal
  • Q and P are the probability density distribution of the EEG signal energy in the frequency band i Hz.
  • D represents the relative entropy of the two probability density P and Q distributions.
  • Step S103 Evaluate the sleep quality parameters according to the sleep quality reference data.
  • the sleep quality reference data includes: sleep quality data based on healthy people or historical sleep quality of subjects.
  • the subjects sleep quality is evaluated and intervention suggestions are given, or neurophysiological information is provided as feedback signals for sleep regulation.
  • the purpose of this application is to propose a new sleep quality evaluation method, which can identify people's sleep quality by extracting the entropy characteristics of the high-frequency components of sleep EEG, and then provide a more objective evaluation method for the intervention and treatment of sleep disorders.
  • the sleep quality evaluation method based on high-frequency EEG described in this application is closer to the subject's own sleep experience, and considerable social and economic benefits can be obtained.
  • Figure 2 it is shown as a broken line diagram of the sleep quality evaluation of an individual complaining of good sleep and an individual complaining of poor sleep. Among them, the abscissa is the EEG frequency, and the ordinate is the quality of sleep.
  • the method described in this application is suitable for the evaluation of sleep disorder patients and affective disorder patients with sleep problems.
  • the method described in this application is used in a sleep disorder mouse model (10-50Hz, 50-100Hz), two sleep disorder monkeys
  • the model (10-50Hz, 50-100Hz), 100 patients with insomnia (50-100Hz), and 50 patients with depressive symptoms (10-50Hz) were tested on the model (10-50Hz, 50-100Hz), which confirmed the effectiveness of the method, and its sensitivity and stability It is better than traditional sleep evaluation methods.
  • the device 300 includes:
  • the obtaining module 301 is used to obtain the EEG signals generated during sleep, pre-process them and stage them into different sleep stages;
  • the processing module 302 is configured to separate the EEG signals of different sleep stages containing high-frequency EEG signals of different frequency bands according to the frequency domain filtering method, and calculate the sleep corresponding to the different sleep stages according to the high-frequency energy relative entropy algorithm Quality parameters; the sleep quality parameters are evaluated according to the sleep quality reference data.
  • the division of the various modules of the above device is only a division of logical functions, and may be fully or partially integrated into a physical entity in actual implementation, or may be physically separated. And these units can all be implemented in the form of software called by processing elements; they can also be all implemented in the form of hardware; some modules can be implemented in the form of calling software by processing elements, and some of the modules can be implemented in the form of hardware.
  • the acquisition module 301 may be a separately established processing element, or it may be integrated in a chip of the above-mentioned device for implementation.
  • it may also be stored in the memory of the above-mentioned device in the form of program code and processed by one of the above-mentioned devices.
  • the component calls and executes the functions of the above acquisition module 301.
  • the implementation of other modules is similar.
  • all or part of these modules can be integrated together or implemented independently.
  • the processing element described here may be an integrated circuit with signal processing capability.
  • each step of the above method or each of the above modules can be completed by an integrated logic circuit of hardware in the processor element or instructions in the form of software.
  • the above modules may be one or more integrated circuits configured to implement the above methods, for example: one or more application specific integrated circuits (ASICs for short), or one or more microprocessors ( Digital signal processor, DSP for short), or, one or more Field Programmable Gate Array (FPGA for short), etc.
  • ASICs application specific integrated circuits
  • DSP Digital signal processor
  • FPGA Field Programmable Gate Array
  • the processing element may be a general-purpose processor, such as a central processing unit (CPU for short) or other processors that can call program codes.
  • these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC for short).
  • the electronic device 400 includes: a memory 401, a processor 402, and a communicator 403; the memory 401 is used to store computer instructions; the processor 402 runs the computer instructions to implement the method described in FIG. 2 .
  • the communicator 403 is used to communicate with external devices.
  • the external device may be an EEG signal collector to collect EEG signals of the user under test.
  • the electronic device 400 described in the present application may be presented as a terminal such as a smart bracelet or a smart phone for evaluating sleep quality.
  • the electronic device 400 described in the present application can also be integrated with an EEG signal collector (collection in the form of electrodes attached to the scalp) to form a sleep evaluation system.
  • EEG signal collector selection in the form of electrodes attached to the scalp
  • the number of the memories 401 in the electronic device 400 may all be one or more, the number of the processors 402 may all be one or more, and the number of the communicators 403 are all. There can be one or more, and Figure 4 takes one as an example.
  • the processor 402 in the electronic device 400 will load one or more instructions corresponding to the process of the application program into the memory 401 according to the steps described in FIG.
  • the device 402 runs the application program stored in the memory 402, so as to implement the method described in FIG. 2.
  • the memory 401 may include a random access memory (Random Access Memory, RAM for short), and may also include a non-volatile memory (non-volatile memory), for example, at least one disk memory.
  • the memory 401 stores an operating system and operating instructions, executable modules or data structures, or a subset of them, or an extended set of them.
  • the operating instructions may include various operating instructions for implementing various operations.
  • the operating system may include various system programs for implementing various basic services and processing hardware-based tasks.
  • the processor 402 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP), etc.; it may also be a digital signal processor (Digital Signal Processing, DSP for short), etc. ), Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.
  • CPU Central Processing Unit
  • NP Network Processor
  • DSP Digital Signal Processing
  • FPGA Field-Programmable Gate Array
  • the communicator 403 is used to realize the communication connection between the database access device and other devices (for example, a client, a read-write library, and a read-only library).
  • the communicator 403 may include one or more groups of modules with different communication modes, for example, a CAN communication module connected to a CAN bus.
  • the communication connection may be one or more wired/wireless communication methods and combinations thereof. Communication methods include: Internet, CAN, intranet, wide area network (WAN), local area network (LAN), wireless network, digital subscriber line (DSL) network, frame relay network, asynchronous transfer mode (ATM) network, virtual private network (VPN) ) And/or any one or more of any other suitable communication networks. For example: any one and multiple combinations of WIFI, Bluetooth, NFC, GPRS, GSM, and Ethernet.
  • the various components of the electronic device 400 are coupled together through a bus system, where the bus system may include a power bus, a control bus, and a status signal bus in addition to a data bus.
  • bus system may include a power bus, a control bus, and a status signal bus in addition to a data bus.
  • various buses are referred to as bus systems in FIG. 4.
  • the present application provides a non-transitory computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the method described in FIG. 1 is implemented.
  • the implementation of the above-mentioned system and the functions of each unit can be implemented by hardware related to a computer program.
  • the aforementioned computer program can be stored in a computer-readable storage medium.
  • the program is executed, the embodiment including the functions of the aforementioned system and each unit is executed; and the aforementioned storage medium includes: ROM, RAM, magnetic disk, or optical disc, and other media that can store program codes.
  • this application provides a method, device, equipment, and storage medium for evaluating sleep quality based on high-frequency EEG.
  • EEG signals generated during sleep they are preprocessed and staged into different sleep stages.
  • the frequency domain filtering method separate the EEG signals of different sleep stages that contain high-frequency EEG signals of different frequency bands, and calculate the sleep quality parameters corresponding to the different sleep stages according to the high-frequency energy relative entropy algorithm;
  • the quality reference data is used to evaluate the sleep quality parameters.

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Abstract

一种基于高频脑电睡眠质量评价方法、装置、设备和存储介质。通过获取睡眠过程中产生的脑电信号，对其进行预处理并分期为不同睡眠阶段（S101）；依据频域滤波方法分离出不同睡眠阶段的脑电信号中包含不同频段的高频脑电信号，并依据高频能量相对熵算法计算对应不同睡眠阶段的睡眠质量参数（S102）；依据睡眠质量参考数据，对睡眠质量参数进行评价（S103）。基于高频脑电睡眠质量评价方法中的算法标准明确，适用于健康大数据应用，其分析结果更接近被试本人的睡眠感受。

Description

基于高频脑电睡眠质量评价方法、装置、设备和存储介质 技术领域
本申请涉及睡眠质量评价技术领域，特别是涉及一种基于高频脑电睡眠质量评价方法、装置、设备和存储介质。
背景技术
睡眠占据人类整个生命周期的三分之一，越来越多的研究发现睡眠在人类健康和高级认知活动中扮演着关键的作用。良好的睡眠对于免疫系统的正常运行、脑中代谢废物的清除、记忆的形成与巩固、情绪调节是必不可少的。睡眠紊乱是仅次于疼痛的第二大就诊原因。几乎所有的精神类和神经类疾病，都伴随着睡眠问题，例如焦虑、抑郁、惊恐发作、精神分裂症、帕金森病、老年痴呆症、自闭症等。对睡眠质量进行实时的监测与评价，对改善人类生活质量、保障身心健康具有重大意义。
目前国内外研究者通过多导睡眠脑图设备，记录被试睡眠脑电及心电、呼吸等各项生理指标，依据权威标准对睡眠进行分期，从而对被试的睡眠进行诊断和分类。其中，脑电在睡眠分期和评价中扮演最重要的角色。
当前基于脑电的睡眠评价方法，主要使用的是低频信号(<35Hz)。但是却存在一类主观失眠患者，其低频脑电睡眠分期与正常人并无显著差异，但其主观的睡眠体验却极差[1]，并对其日常生活和健康产生不良的影响，因此如何提升目前睡眠分析的普适性亟需方法上的突破。高频脑电是执行认知功能、脑进行意识活动的客观表现，因而与人的清醒态与睡眠态的主观感受更一致[2-5]。但因为数据噪音与分析方法的问题，过去的分析方法无法使用高频脑电信号进行睡眠质量评价，或者仅采用部分高频频段在大规模的群体上进行统计。
因此，需要一种从高频的脑电信号角度对个人的睡眠进行客观评价的方案。
参考文献：
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2.J.Fernandez-Mendoza et al.,Insomnia is Associated with Cortical Hyperarousal as Early as Adolescence.Sleep39,1029-1036(2016).
3.A.D.Krystal,J.D.Edinger,W.K.Wohlgemuth,G.R.Marsh,NREM sleep EEG frequency spectral correlates of sleep complaints in primary insomnia subtypes.Sleep25,630-640(2002).
4.M.L.Perlis,M.T.Smith,P.J.Andrews,H.Orff,D.E.Giles,Beta/Gamma EEG activity in  patients with primary and secondary insomnia and good sleeper controls.Sleep24,110-117(2001).
5.D.Riemann et al.,The neurobiology,investigation,and treatment of chronic insomnia.The Lancet.Neurology14,547-558(2015).
发明内容
鉴于以上所述现有技术的缺点，本申请的目的在于提供一种基于高频脑电睡眠质量评价方法、装置、设备和存储介质，以解决现有技术中的问题。
为实现上述目的及其他相关目的，本申请提供一种基于高频脑电睡眠质量评价方法，所述方法包括：获取睡眠过程中产生的脑电信号，对其进行预处理并分期为不同睡眠阶段；依据频域滤波方法分离出不同所述睡眠阶段的脑电信号中包含不同频段的高频脑电信号，并依据高频能量相对熵算法计算对应不同所述睡眠阶段的睡眠质量参数；依据睡眠质量参考数据，对所述睡眠质量参数进行评价。
于本申请的一实施例中，所述频域滤波方法包括：采用预设长度的时间窗口将各所述睡眠阶段的脑电信号分割为多个时间段；利用快速傅里叶变换计算每个所述时间段的频谱，并去除频谱异常的所述时间段；依据巴特沃斯滤波器分离各所述时间段不同的频率成分，以提取得到包含各个高频频段的高频脑电信号。
于本申请的一实施例中，所述高频脑电信号为频率范围介于10Hz和50Hz之间、以及介于50Hz和100Hz之间的头皮或颅内的脑电信号。
于本申请的一实施例中，所述高频能量相对熵的方法包括：计算不同所述睡眠阶段的所述高频脑电信号中各所述时间窗口内的平均能量值；依据所述平均能量值计算对应不同所述睡眠阶段的所述高频脑电信号的相对熵，据以作为所述睡眠质量参数。
于本申请的一实施例中，所述预处理的方法包括：对所述脑电信号进行滤波处理以去除特点频段干扰；和/或，通过识别以去除所述脑电信号中的噪音信号。
于本申请的一实施例中，所述对预处理后的所述脑电信号分期为不同睡眠阶段的方法包括：获取预处理后的所述脑电信号中低频脑电信号的能量和频率特征，并依据美国睡眠分期标准或欧洲睡眠分期标准进行分期。
于本申请的一实施例中，所述睡眠质量参考数据包括：基于健康人群的睡眠质量数据或被试对象的历史睡眠质量。
为实现上述目的及其他相关目的，本申请提供一种电子装置，所述装置包括：获取模块，用于获取睡眠过程中产生的脑电信号，对其进行预处理并分期为不同睡眠阶段；处理模块，用于依据频域滤波方法分离出不同所述睡眠阶段的脑电信号中包含不同频段的高频脑电信号， 并依据高频能量相对熵算法计算对应不同所述睡眠阶段的睡眠质量参数；依据睡眠质量参考数据，对所述睡眠质量参数进行评价。
为实现上述目的及其他相关目的，本申请提供一种电子设备，所述设备包括：存储器、处理器、及通信器；所述存储器用于存储计算机指令；所述处理器运行计算机指令实现如上所述的方法；所述通信器用于与外部设备通信。
为实现上述目的及其他相关目的，本申请提供一种非暂时的计算机可读存储介质，存储有计算机指令，所述计算机指令被运行时执行如上所述的方法
综上所述，本申请的一种基于高频脑电睡眠质量评价方法、装置、设备和存储介质，通过获取睡眠过程中产生的脑电信号，对其进行预处理并分期为不同睡眠阶段；依据频域滤波方法分离出不同所述睡眠阶段的脑电信号中包含不同频段的高频脑电信号，并依据高频能量相对熵算法计算对应不同所述睡眠阶段的睡眠质量参数；依据睡眠质量参考数据，对所述睡眠质量参数进行评价。
具有以下有益效果：
本申请所述的基于高频脑电睡眠质量评价方法中的算法标准明确，适用于健康大数据应用，其分析结果更接近被试本人的睡眠感受，可望获得可观的社会效益和经济效益。
附图说明
图1显示为本申请于一实施例中的基于高频脑电睡眠质量评价方法的流程示意图。
图2显示为本申请于一实施例中一例主诉睡眠好与一例主诉睡眠差的个体的睡眠质量评价的折线示意图。
图3显示为本申请于一实施例中的电子装置的模块示意图。
图4显示为本申请于一实施例中的电子设备的结构示意图。
具体实施方式
以下通过特定的具体实例说明本发明的实施方式，本领域技术人员可由本说明书所揭露的内容轻易地了解本发明的其他优点与功效。本发明还可以通过另外不同的具体实施方式加以实施或应用，本说明书中的各项细节也可以基于不同观点与应用，在没有背离本发明的精神下进行各种修饰或改变。需说明的是，在不冲突的情况下，以下实施例及实施例中的特征可以相互组合。
需要说明的是，以下实施例中所提供的图示仅以示意方式说明本发明的基本构想，遂图式中仅显示与本发明中有关的组件而非按照实际实施时的组件数目、形状及尺寸绘制，其实 际实施时各组件的型态、数量及比例可为一种随意的改变，且其组件布局型态也可能更为复杂。
本申请所述的方法适用于睡眠障碍患者及存在睡眠问题的情感障碍患者评估。通过对用户睡眠过程中的脑电信号进行采集，采用传统或现有方法对睡眠脑电信号进行分期之后，提取各睡眠阶段的高频信号，通过高频信号实时生成每个被试的睡眠质量参数，从而对被试的睡眠质量进行评价。
如图1所示，展示为本申请于一实施例中的基于高频脑电睡眠质量评价方法的流程示意图。如图所示，所述方法包括：
步骤S101：获取睡眠过程中产生的脑电信号，对其进行预处理并分期为不同睡眠阶段。
于本实施例中，所述脑电信号可以是来自被试者或用户在睡眠或自由活动情况下的头皮脑电，例如，通过一个或多个电极采集。
于本实施例中，所述脑电信号可以是通过脑电信号采集器对被试用户的睡眠过程进行采集的。举例来说，所述脑电信号采集器可以是包含EMI滤波器，放大器，采样器等器件的常见可采集脑电信号的设备。
在一些实施例中，在本申请所述的方法中，还可以包含获取其他可表征睡眠过程的指标信号，如体动信号，呼吸信号，心电信号等，用来辅助评价。具体来说，可以用于交叉性的分析与应用，如预防睡梦中猝死，或分析睡眠中多动或梦游等情况下对睡眠质量的影响等。
于本申请一实施例中，所述预处理的方法包括：对所述脑电信号进行滤波处理以去除特点频段干扰；和/或，通过识别以去除所述脑电信号中的噪音信号。
于本实施例中，所获取的脑电信号是粗糙的，直接对其进行分析并不可靠和准确，因此还不需要对其进行一定的处理。
其中，所述噪音可以是由于眼动、身体运动、电磁干扰等导致的噪音。
于本申请中并不局限上述所提到的滤波处理和/或去噪音处理，例如还可以包含信号放大处理。
于本申请一实施例中，所述对预处理后的所述脑电信号分期为不同睡眠阶段的方法包括：
获取预处理后的所述脑电信号中低频脑电信号的能量和频率特征，并依据美国睡眠分期标准或欧洲睡眠分期标准进行分期。
通常，在睡眠过程中，脑电信号发生各种不同变化，这些变化随着睡眠的深度而不同。根据脑电图的不同特征，又将睡眠分为两种状态：非眼球快速运动睡眠(又称正相睡眠、慢波睡眠、同步睡眠、安静睡眠、NREM睡眠)和眼球快速运动睡眠(又称异相睡眠、快波睡 眠、去同步化睡眠、活跃睡眠、REM睡眠，还称雷姆期现象)，二者以是否有眼球阵发性快速运动及不同的脑电波特征相区别。
睡眠是一动态过程，非快速眼动期(NREM)期与快速眼动期(REM)期交替出现。NREM期睡眠时间约占75％－80％，REM期睡眠时间约占20％－25％。
例如，依据美国睡眠医学学会的分期标准，睡眠主要分为以下5期(成年人)：
W期——清醒期(Wakefulness)；
N1期睡眠——非快速眼动1期(NREM1)；
N2期睡眠——非快速眼动2期(NREM2)；
N3期睡眠——非快速眼动3期(NREM3)；
R期睡眠——快速眼动期(REM)。
举例来说，对应一般人群的各期变化通常为：人从入睡开始，先是N1、N2、N3期，然后经N2过渡到REM期。这是第一个循环；第二个循环N2、N3、N2、REM；然后再来N2、N3、N2、REM；如此往复，一夜共约5个循环，每个循环约90分钟左右。前半夜N3占比高，后半夜N3越来越少，REM越来越多。这里举例说明的并且标准或对应每个人，但大致的各期变化情况类似，这里仅供参考。
各期的划分主要依据脑电波形来判断，主要的波形包括如下：
α节律波：W期闭眼状态。
α波：频率8～13Hz，主要见于安静清醒闭眼状态下和REM期，NI期<50％。睁眼时消失，REM期较清醒期频率慢1～2Hz无振幅和形态标准，往往表现渐增渐减状。老年人频率和振幅降低。亦见于嗜睡状态，某些重症OSAHS或重症左心功能不全患者可能整夜PSG大部分时间表现为低频α波。主要在枕区。
β波：W期睁眼状态。
β波：主要见于清醒期睁眼状态，频率>13Hz的低电位波，无振幅和形态标准。有时也出现在N2睡眠期，服用安眠药后出现较多。
顶尖波：N1期。
外形尖锐，与背景明显区别，主要见于N1睡眠期后期，常与θ波毗邻。无振幅和形态标准，时间一般<0.5秒，中央部。
θ波：N1期后期。
主要见于N1期后期，频率4～7Hz，一般无振幅及形态标准，但波幅通常>50uV，有时(尤其在幼儿及青少年)为短阵高电压波，需与癫痫波鉴别，有学者称为良性癫痫样睡眠移行波 (BETS)。中央部位明显。
纺锤波(梭形波)：N2期。
外形规则，频率为11～16Hz(最常见12～14Hz)成串出现的明显可辨的波形，持续时间≥0.5秒，波幅<>主要在N2,N3睡眠期，为N2期的特征性脑电波，通常以中央区导联记录的波幅最大。服用安定类药物患者纺锤波出现明显增多。
K复合波：N2期。
K复合波：N2期特征性脑电波，无频率标准，从背景脱颖而出。为一个清晰可辨的陡峭负向波(向上)之后紧接着一个正向波(向下)，凸显在背景EEG中，持续时间≥0.5秒。K复合波通常在额部脑电导联记录最明显，N2、N3期出现。还可作为睡眠期间脑对外界(声音)或内部刺激(呼吸暂停)的反应出现K复合波结束后1秒内出现α波，此时称为觉醒相关K复合波。与K复合波相关的觉醒，其发生的始点与K复合波截止点间不能大于1s。
低频δ慢波：N3期或N4期。
δ波(慢波睡眠)：低频率(范围0.5～2Hz)，高波幅(≥75uV)，在整个额区可监测到。N2期睡眠<20％，N3期睡眠δ波占20％－50％，N4期δ波占50％以上。
锯齿波：REM期。
锯齿波：序列陡峭波浪或三角状波形，类似锯齿状，频率为2～6Hz，阵发出现，本质上为θ波，<50μV常出现在阵发性快速眼动波之前。
低电压混合频率波：不高于10μV的节律性波
Figure PCTCN2021079528-appb-000001
以及不高于20μV的单个波。
通过分析低频脑电如上述各波形，对睡眠进行分期。
步骤S102：依据频域滤波方法分离出不同所述睡眠阶段的脑电信号中包含不同频段的高频脑电信号，并依据高频能量相对熵算法计算对应不同所述睡眠阶段的睡眠质量参数。
在依据常见的分期方法对所述睡眠过程中产生的脑电信号分析后，本方法将从所述脑电信号中提取高频。
于本申请一实施例中，所述高频脑电信号为频率范围介于10Hz和50Hz之间、以及介于50Hz和100Hz之间的头皮或颅内的脑电信号。
具体方法如下：
于本申请一实施例中，所述频域滤波方法包括：
A、采用预设长度的时间窗口将各所述睡眠阶段的脑电信号分割为多个时间段。
于本实施例中，所述预设长度的时间窗口优选为长度为10-30s的时间窗口。
B、利用快速傅里叶变换计算每个所述时间段的频谱，并去除频谱异常的所述时间段。
于本实施例中，计算公式为：
Figure PCTCN2021079528-appb-000002
其中，n是采样时间点，N是总的采样点数目，x n是时间点n的脑电采样值。π、e分别是圆周率和自然常数，i是虚数，X k是波向量k下的傅里叶系数。
频谱是频率谱密度的简称，是频率的分布曲线。复杂振荡分解为振幅不同和频率不同的谐振荡，这些谐振荡的幅值按频率排列的图形叫做频谱。频谱广泛应用于声学、光学和无线电技术等方面。频谱将对信号的研究从时域引入到频域，从而带来更直观的认识。把复杂的机械振动分解成的频谱称为机械振动谱，把声振动分解成的频谱称为声谱，把光振动分解成的频谱称为光谱，把电磁振动分解成的频谱称为电磁波谱，一般常把光谱包括在电磁波谱的范围之内。分析各种振动的频谱就能了解该复杂振动的许多基本性质，因此频谱分析已经成为分析各种复杂振动的一项基本方法。
C、依据巴特沃斯滤波器分离各所述时间段不同的频率成分，以提取得到包含各个高频频段的高频脑电信号。
于本实施例中，由于介于高频的频率有一定范围，故对高频的不同频率/频段进行划分，而不是统一处理。
所述巴特沃斯滤波器(Butterworth filter)是电子滤波器的一种。巴特沃斯滤波器的特点是通频带的频率响应曲线最平滑。
具体计算公式包括：
Figure PCTCN2021079528-appb-000003
其中，H代表传递函数，z是指信号的z域表示,a、b分别表示递归系数，n表示滤波器的阶数。
于本申请一实施例中，所述高频能量相对熵的方法包括：
A、计算不同所述睡眠阶段的所述高频脑电信号中各所述时间窗口内的平均能量值。
于本实施例中，所述时间窗口对应上述优选的10-30s时间窗口。
B、依据所述平均能量值计算对应不同所述睡眠阶段的所述高频脑电信号的相对熵，据以作为所述睡眠质量参数。
具体计算公式包括：
Figure PCTCN2021079528-appb-000004
其中，i是脑电信号的特征参数，Q、P是频段i Hz下脑电信号能量的概率密度分布。D表示两个概率密度P、Q分布的相对熵。
步骤S103：依据睡眠质量参考数据，对所述睡眠质量参数进行评价。
于本申请一实施例中，所述睡眠质量参考数据包括：基于健康人群的睡眠质量数据或被试对象的历史睡眠质量。
于本实施例中，基于健康人群的睡眠参数和被试本人的历史参数，对被试的睡眠质量进行评价，并给出干预建议，或者为睡眠调节提供神经生理信息作为反馈信号。
本申请旨在提出一种新的睡眠质量评价方法，通过提取睡眠脑电的高频成分的熵特征来识别人的睡眠质量，继而为睡眠紊乱的干预治疗提供一个更加客观的评价方法。本申请所述的基于高频脑电睡眠质量评价方法更接近被试本人的睡眠感受，可获得可观的社会效益和经济效益。如图2所示，展示为一例主诉睡眠好与一例主诉睡眠差的个体的睡眠质量评价的折线示意图。其中，横坐标为脑电频率，纵坐标为睡眠质量。
本申请所述的方法适用于睡眠障碍患者及存在睡眠问题的情感障碍患者评估，本申请所述的方法通过在一种睡眠障碍鼠模型(10-50Hz，50-100Hz)，两种睡眠障碍猴模型(10-50Hz，50-100Hz)、100例失眠患者(50-100Hz)，50例具有抑郁症状的患者(10-50Hz)上进行检测，证实了该方法的有效性，且灵敏性、稳定性优于传统的睡眠评测方法。
如图3所示，展示为本申请于一实施例中的电子装置的模块示意图。如图所示，所述装置300包括：
获取模块301，用于获取睡眠过程中产生的脑电信号，对其进行预处理并分期为不同睡眠阶段；
处理模块302，用于依据频域滤波方法分离出不同所述睡眠阶段的脑电信号中包含不同频段的高频脑电信号，并依据高频能量相对熵算法计算对应不同所述睡眠阶段的睡眠质量参数；依据睡眠质量参考数据，对所述睡眠质量参数进行评价。
需要说明的是，上述装置各模块/单元之间的信息交互、执行过程等内容，由于与本申请所述方法实施例基于同一构思，其带来的技术效果与本申请方法实施例相同，具体内容可参见本申请前述所示的方法实施例中的叙述，此处不再赘述。
还需要说明的是，应理解以上装置的各个模块的划分仅仅是一种逻辑功能的划分，实际实现时可以全部或部分集成到一个物理实体上，也可以物理上分开。且这些单元可以全部以软件通过处理元件调用的形式实现；也可以全部以硬件的形式实现；还可以部分模块通过处理元件调用软件的形式实现，部分模块通过硬件的形式实现。例如，获取模块301可以为单 独设立的处理元件，也可以集成在上述装置的某一个芯片中实现，此外，也可以以程序代码的形式存储于上述装置的存储器中，由上述装置的某一个处理元件调用并执行以上获取模块301的功能。其它模块的实现与之类似。此外这些模块全部或部分可以集成在一起，也可以独立实现。这里所述的处理元件可以是一种集成电路，具有信号的处理能力。在实现过程中，上述方法的各步骤或以上各个模块可以通过处理器元件中的硬件的集成逻辑电路或者软件形式的指令完成。
例如，以上这些模块可以是被配置成实施以上方法的一个或多个集成电路，例如：一个或多个特定集成电路(Application Specific Integrated Circuit，简称ASIC)，或，一个或多个微处理器(digital signal processor，简称DSP)，或，一个或者多个现场可编程门阵列(Field Programmable Gate Array，简称FPGA)等。再如，当以上某个模块通过处理元件调度程序代码的形式实现时，该处理元件可以是通用处理器，例如中央处理器(Central Processing Unit，简称CPU)或其它可以调用程序代码的处理器。再如，这些模块可以集成在一起，以片上系统(system-on-a-chip，简称SOC)的形式实现。
如图4所示，展示为本申请于一实施例中的电子设备结构示意图。如图所示，所述电子设备400包括：存储器401、处理器402、及通信器403；所述存储器401用于存储计算机指令；所述处理器402运行计算机指令实现如图2所述的方法。所述通信器403用于与外部设备通信。
于本实施例中，所述外部设备可以是脑电信号采集器，以采集被试用户的脑电信号。
于本申请一实施例中，本申请所述电子设备400可以呈现为智能手环，智能手机等终端，以用于评价睡眠质量。
于本申请另一实施例中，本申请所述电子设备400还可以与脑电信号采集器(贴于头皮的电极等形式的采集)一体化为睡眠评价系统。
在一些实施例中，所述电子设备400中的所述存储器401的数量均可以是一或多个，所述处理器402的数量均可以是一或多个，所述通信器403的数量均可以是一或多个，而图4中均以一个为例。
于本申请一实施例中，所述电子设备400中的处理器402会按照如图2所述的步骤，将一个或多个以应用程序的进程对应的指令加载到存储器401中，并由处理器402来运行存储在存储器402中的应用程序，从而实现如图2所述的方法。
所述存储器401可以包括随机存取存储器(Random Access Memory，简称RAM)，也可以包括非易失性存储器(non-volatile memory)，例如至少一个磁盘存储器。所述存储器401存储 有操作系统和操作指令、可执行模块或者数据结构，或者它们的子集，或者它们的扩展集，其中，操作指令可包括各种操作指令，用于实现各种操作。操作系统可包括各种系统程序，用于实现各种基础业务以及处理基于硬件的任务。
所述处理器402可以是通用处理器，包括中央处理器(Central Processing Unit，简称CPU)、网络处理器(Network Processor，简称NP)等；还可以是数字信号处理器(Digital Signal Processing，简称DSP)、专用集成电路(Application Specific Integrated Circuit，简称ASIC)、现场可编程门阵列(Field－Programmable Gate Array，简称FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。
所述通信器403用于实现数据库访问装置与其他设备(例如客户端、读写库和只读库)之间的通信连接。所述通信器403可包含一组或多组不同通信方式的模块，例如，与CAN总线通信连接的CAN通信模块。所述通信连接可以是一个或多个有线/无线通讯方式及其组合。通信方式包括：互联网、CAN、内联网、广域网(WAN)、局域网(LAN)、无线网络、数字用户线(DSL)网络、帧中继网络、异步传输模式(ATM)网络、虚拟专用网络(VPN)和/或任何其它合适的通信网络中的任何一个或多个。例如：WIFI、蓝牙、NFC、GPRS、GSM、及以太网中任意一种及多种组合。
在一些具体的应用中，所述电子设备400的各个组件通过总线系统耦合在一起，其中总线系统除包括数据总线之外，还可以包括电源总线、控制总线和状态信号总线等。但是为了清除说明起见，在图4中将各种总线都成为总线系统。
于本申请的一实施例中，本申请提供一种非暂时的计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时实现如图1所述的方法。
所述计算机可读存储介质，本领域普通技术人员可以理解：实现上述系统及各单元功能的实施例可以通过计算机程序相关的硬件来完成。前述的计算机程序可以存储于一计算机可读存储介质中。该程序在执行时，执行包括上述系统及各单元功能的实施例；而前述的存储介质包括：ROM、RAM、磁碟或者光盘等各种可以存储程序代码的介质。
综上所述，本申请提供的一种基于高频脑电睡眠质量评价方法、装置、设备和存储介质，通过获取睡眠过程中产生的脑电信号，对其进行预处理并分期为不同睡眠阶段；依据频域滤波方法分离出不同所述睡眠阶段的脑电信号中包含不同频段的高频脑电信号，并依据高频能量相对熵算法计算对应不同所述睡眠阶段的睡眠质量参数；依据睡眠质量参考数据，对所述睡眠质量参数进行评价。
本申请有效克服了现有技术中的种种缺点而具高度产业利用价值。
上述实施例仅例示性说明本申请的原理及其功效，而非用于限制本发明。任何熟悉此技术的人士皆可在不违背本申请的精神及范畴下，对上述实施例进行修饰或改变。因此，举凡所属技术领域中包含通常知识者在未脱离本发明所揭示的精神与技术思想下所完成的一切等效修饰或改变，仍应由本申请的权利要求所涵盖。

Claims (10)

  1. 一种基于高频脑电睡眠质量评价方法，其特征在于，所述方法包括：
    获取睡眠过程中产生的脑电信号，对其进行预处理并分期为不同睡眠阶段；
    依据频域滤波方法分离出不同所述睡眠阶段的脑电信号中包含不同频段的高频脑电信号，并依据高频能量相对熵算法计算对应不同所述睡眠阶段的睡眠质量参数；
    依据睡眠质量参考数据，对所述睡眠质量参数进行评价。
  2. 根据权利要求1所述的方法，其特征在于，所述频域滤波方法包括：
    采用预设长度的时间窗口将各所述睡眠阶段的脑电信号分割为多个时间段；
    利用快速傅里叶变换计算每个所述时间段的频谱，并去除频谱异常的所述时间段；
    依据巴特沃斯滤波器分离各所述时间段不同的频率成分，以提取得到包含各个高频频段的高频脑电信号。
  3. 根据权利要求2所述的方法，其特征在于，所述高频脑电信号为频率范围介于10Hz和50Hz之间、以及介于50Hz和100Hz之间的头皮或颅内的脑电信号。
  4. 根据权利要求2所述的方法，其特征在于，所述高频能量相对熵的方法包括：
    计算不同所述睡眠阶段的所述高频脑电信号中各所述时间窗口内的平均能量值；
    依据所述平均能量值计算对应不同所述睡眠阶段的所述高频脑电信号的相对熵，据以作为所述睡眠质量参数。
  5. 根据权利要求1所述的方法，其特征在于，所述预处理的方法包括：
    对所述脑电信号进行滤波处理以去除特点频段干扰；和/或，通过识别以去除所述脑电信号中的噪音信号。
  6. 根据权利要求1所述的方法，其特征在于，所述对预处理后的所述脑电信号分期为不同睡眠阶段的方法包括：
    获取预处理后的所述脑电信号中低频脑电信号的能量和频率特征，并依据美国睡眠分期标准或欧洲睡眠分期标准进行分期。
  7. 根据权利要求1所述的方法，其特征在于，所述睡眠质量参考数据包括：基于健康人群的睡眠质量数据或被试对象的历史睡眠质量。
  8. 一种电子装置，其特征在于，所述装置包括：
    获取模块，用于获取睡眠过程中产生的脑电信号，对其进行预处理并分期为不同睡眠阶段；
    处理模块，用于依据频域滤波方法分离出不同所述睡眠阶段的脑电信号中包含不同频段的高频脑电信号，并依据高频能量相对熵算法计算对应不同所述睡眠阶段的睡眠质量 参数；
    依据睡眠质量参考数据，对所述睡眠质量参数进行评价。
  9. 一种电子设备，其特征在于，所述设备包括：存储器、处理器、及通信器；所述存储器用于存储计算机指令；所述处理器运行计算机指令实现如权利要求1至7中任意一项所述的方法；所述通信器用于与外部设备通信。
  10. 一种非暂时的计算机可读存储介质，其特征在于，存储有计算机指令，所述计算机指令被运行时执行如权利要求1至7中任一项所述的方法。
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