Sleep electroencephalogram analysis method and device

Abstract

The invention relates to the technical field of sleep electroencephalogram analysis, in particular to a sleep electroencephalogram analysis method and device, which more comprehensively assesses the sleep state and improves the reliability and credibility of the result through double verification of frequency domain analysis and image analysis. The method comprises the following steps: acquiring sleep electroencephalogram time domain information of a to-be-analyzed user; the sleep electroencephalogram time domain information at least comprises one physiological signal including an electrooculogram, an electroencephalogram, an electrocardiogram and a mandibular electromyogram; inputting the sleep electroencephalogram time domain information into an electroencephalogram time-frequency domain conversion model to obtain a sleep electroencephalogram frequency domain feature set; performing classified statistics on the sleep electroencephalogram frequency domain feature set by using a preset sleep activity feature reference to obtain a sleep state staging frequency domain analysis result of the to-be-analyzed user; the sleep states comprise sleep states in different time periods; and inputting the sleep electroencephalogram time domain information of the user to be analyzed into the electroencephalogram activity image recognition model.

Description

Technical Field

[0001] The present invention relates to the technical field of sleep electroencephalogram analysis, and in particular to a sleep electroencephalogram analysis method and device. Background Art

[0002] EEG is a weak electrical signal spontaneously generated by the human body. It is a direct reflection of the activity of brain neurons and is of great significance for understanding brain function, diagnosing neurological diseases and evaluating sleep quality. These weak electrical signals are amplified by EEG amplifiers and converted into digital information through A / D conversion technology. We can observe the electroencephalogram (EEG) on the screen, which provides doctors and researchers with an intuitive analysis tool. In current medical practice, most hospitals are already equipped with digital EEG examination equipment and use sleep EEG as one of the routine examination items.

[0003] Most existing sleep EEG analysis methods rely on simple EEG fluctuation recognition, focusing mainly on the waveform shape and amplitude changes, while ignoring deeper information such as EEG frequency domain components and graphic trends. Although existing methods can provide some basic sleep information, such as the approximate division of sleep stages, they have certain limitations in accuracy and depth.

[0004] Therefore, there is an urgent need for a sleep EEG analysis method to solve the above problems. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a sleep EEG analysis method and device which can more comprehensively evaluate the sleep state and improve the reliability and credibility of the results through dual verification of frequency domain analysis and image analysis.

[0006] In a first aspect, the present invention provides a sleep EEG analysis method, the method comprising:

[0007] Obtain the sleep EEG time domain information of the user to be analyzed;

[0008] Inputting the sleep EEG time domain information into an EEG time-frequency domain conversion model to obtain a sleep EEG frequency domain feature set;

[0009] Using the pre-set sleep activity feature benchmark, the sleep EEG frequency domain feature set is classified and counted to obtain the sleep state stage frequency domain analysis results of the user to be analyzed;

[0010] Input the sleep EEG time domain information of the user to be analyzed into the EEG activity image recognition model to obtain the sleep state stage image analysis result of the user to be analyzed;

[0011] Comparing the sleep state staging frequency domain analysis result with the sleep state staging image analysis result, extracting different time periods of the analysis results, and sending them to manual analysis;

[0012] The manual analysis results and the undisputed analysis results are aggregated to obtain the sleep state staging results of the user to be analyzed.

[0013] On the other hand, the present application also provides a sleep EEG analysis device, the device comprising:

[0014] A sleep EEG time domain information acquisition module is used to obtain the sleep EEG time domain information of the user to be analyzed; the sleep EEG time domain information includes at least one physiological signal including electrooculogram, electroencephalogram, electrocardiogram and mandibular electromyogram;

[0015] The EEG time-frequency domain conversion module is used to input the collected sleep EEG time-domain information into the EEG time-frequency domain conversion model to obtain a sleep EEG frequency domain feature set;

[0016] The sleep state frequency domain analysis module is used to classify and count the sleep EEG frequency domain feature set using a preset sleep activity feature benchmark to obtain the sleep state stage frequency domain analysis result of the user to be analyzed;

[0017] The EEG activity image recognition module is used to input the sleep EEG time domain information of the user to be analyzed into the EEG activity image recognition model to obtain the sleep state stage image analysis results;

[0018] The analysis result comparison and manual review module is used to compare the sleep state staging frequency domain analysis results with the sleep state staging image analysis results, extract different time periods of the analysis results, and send these time periods to the manual analysis link;

[0019] The result summary module is used to summarize the manual analysis results and the undisputed analysis results to obtain the final sleep state staging results of the user to be analyzed.

[0020] In a third aspect, the present application provides an electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and the computer program, when executed by the processor, implements the steps of any one of the above methods.

[0021] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps in any one of the above-mentioned methods when executed by a processor.

[0022] Compared with the prior art, the present invention has the following advantages: by converting the time domain signal into the frequency domain feature, the changes of different frequency components can be observed more clearly, so as to identify different sleep stages, and can capture the subtle changes of EEG activity, thus improving the accuracy of analysis; by using the EEG activity image recognition model, the sleep state can be analyzed by image, providing more intuitive and detailed sleep state information, further improving the accuracy of analysis; by frequency domain analysis, more in-depth information about EEG activity can be extracted, such as the changing trends of fast frequency components and slow frequency components; by image recognition technology, complex patterns and structures in the EEG can be captured, and these patterns and structures can be used to identify the sleep stages. The structure is often associated with a specific physiological state or pathological condition; through frequency domain analysis and image recognition technology, most of the analysis work can be automated, reducing the workload of manual visual analysis and improving analysis efficiency; for time periods with large differences in automatic analysis results, corrections can be made through manual verification to ensure the accuracy of the final results; through pre-set sleep activity feature benchmarks and classification statistical methods, the standardization and consistency of the analysis process can be ensured, reducing the inconsistency of results caused by differences in analysts' technical levels and sense of responsibility; through dual verification of frequency domain analysis and image analysis, the sleep state can be evaluated more comprehensively, improving the reliability and credibility of the results. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 is a flow chart of a sleep EEG analysis method provided by one embodiment of the present invention;

[0025] Figure 2 is a hardware architecture diagram of an electronic device provided by an embodiment of the present invention;

[0026] Figure 3 It is a structural diagram of a sleep EEG analysis device provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0028] Please refer to Figure 1 The embodiment of the present invention provides a sleep EEG analysis method, the method comprising:

[0029] Step S1, obtaining the sleep EEG time domain information of the user to be analyzed; the sleep EEG time domain information includes at least one physiological signal including electrooculogram, electroencephalogram, electrocardiogram and mandibular electromyogram;

[0030] Step S2, inputting the sleep EEG time domain information into an EEG time-frequency domain conversion model to obtain a sleep EEG frequency domain feature set;

[0031] Step S3, using a preset sleep activity feature benchmark to classify and count the sleep EEG frequency domain feature set, to obtain a frequency domain analysis result of the sleep state of the user to be analyzed; the result includes the sleep state in different time periods;

[0032] Step S4, inputting the sleep EEG time domain information of the user to be analyzed into the EEG activity image recognition model to obtain the sleep state stage image analysis results of the user to be analyzed; the sleep state in different time periods is included;

[0033] Step S5, comparing the sleep state staging frequency domain analysis result with the sleep state staging image analysis result, extracting different time periods of the analysis results, and sending them to manual analysis;

[0034] Step S6: Summarize the manual analysis results and the undisputed analysis results to obtain the sleep state staging results of the user to be analyzed.

[0035] In this embodiment, by converting the time domain signal into frequency domain features, the changes of different frequency components can be observed more clearly, so as to identify different sleep stages, capture subtle changes in EEG activity, and improve the accuracy of analysis; by using the EEG activity image recognition model, the sleep state is analyzed in an image form, providing more intuitive and detailed sleep state information, and further improving the accuracy of analysis; by frequency domain analysis, more in-depth information about EEG activity can be extracted, such as the change trend of fast frequency components and slow frequency components; by image recognition technology, complex patterns and structures in the EEG can be captured, which are often associated with specific physiological states or pathological conditions; by frequency domain analysis and image recognition technology, most of the analysis work can be automated, reducing the workload of manual visual analysis and improving analysis efficiency; for time periods with large differences in automatic analysis results, manual verification is used to correct them to ensure the accuracy of the final results; by pre-set sleep activity feature benchmarks and classification statistical methods, the standardization and consistency of the analysis process can be ensured, and the inconsistency of results caused by differences in the technical level and sense of responsibility of analysts can be reduced; by dual verification of frequency domain analysis and image analysis, the sleep state can be evaluated more comprehensively, and the reliability and credibility of the results can be improved.

[0036] Described below Figure 1 How the various steps are performed.

[0037] For step S1:

[0038] In step S1, the sleep EEG time domain information of the user to be analyzed is obtained; the sleep EEG time domain information refers to the characteristics of the signal changing over time. For sleep EEG analysis, the sleep EEG time domain information includes but is not limited to physiological signals such as electrooculogram (EOG), electroencephalogram (EEG), electrocardiogram (ECG) and mandibular electromyogram (EMG); the specific signals are as follows:

[0039] Electrooculogram (EOG): EOG records the changes in electrical potential caused by eye movement; eye movement is closely related to sleep stages, especially rapid eye movement (REM) sleep; EOG can be used to identify REM and non-REM sleep stages;

[0040] Electroencephalogram (EEG): EEG is the most important sleep EEG signal, reflecting the electrical activity of the cerebral cortex; EEG signals show different characteristic waveforms in different sleep stages, such as delta waves (deep sleep), theta waves (light sleep) and alpha waves (awake state); EEG signal acquisition usually requires the use of multi-conductor electrodes to ensure coverage of different areas of the brain;

[0041] Electrocardiogram (ECG): ECG records the electrical activity of the heart, which is manifested as a series of heartbeat waveforms. Heart activity is related to sleep quality, and abnormal heart rate variability may indicate sleep disorders. ECG signals help eliminate sleep disturbances caused by heart problems.

[0042] Jaw electromyography (EMG): EMG records the electrical activity of the jaw muscles, reflecting muscle tension and movement. During sleep, EMG signals can help distinguish different sleep stages, especially during REM sleep, when muscle tension is significantly reduced. Abnormal EMG activity may be associated with sleep apnea or other sleep-related disorders.

[0043] The specific data collection operations are as follows:

[0044] Step S11, the acquisition equipment includes an EEG amplifier, an A / D converter and a multi-lead recorder; the EEG amplifier is used to amplify weak EEG signals to reach an amplitude suitable for A / D conversion; the A / D converter is used to convert the amplified analog signal into a digital signal for computer processing; the multi-lead recorder is used to synchronously record multiple physiological signals, such as EOG, EEG, ECG and EMG;

[0045] Step S12, electroencephalogram (EEG) electrodes are placed at specific locations on the scalp according to the international 10-20 system standard to record electrical activity in different areas of the brain; electrooculogram (EOG) electrodes are placed above and below the eyes to record eye movements; electrocardiogram (ECG) electrodes are placed on the chest or limbs to record cardiac electrical activity; mandibular electromyogram (EMG) electrodes are placed on the mandibular muscles to record muscle activity;

[0046] Step S13, ensuring that all electrodes start recording at the same time point to ensure data synchronization; selecting a suitable sampling frequency, the sampling frequency of the electroencephalogram is set to 256 Hz or higher to capture high-frequency components; storing the collected physiological signals in a computer for subsequent processing and analysis;

[0047] Step S14: filter the collected signal to remove noise and interference. Common filtering methods include bandpass filtering, low-pass filtering and high-pass filtering; perform baseline correction on the signal to eliminate baseline drift; identify and eliminate artifacts caused by external interference or poor electrode contact.

[0048] For step S2:

[0049] Step S2 is to convert the sleep EEG time domain information obtained in step S1 into a sleep EEG frequency domain feature set; through the EEG time-frequency domain conversion model, the time domain signal is converted into a frequency domain signal, thereby extracting EEG signal features of different frequency components for identifying different sleep stages and abnormal events; the specific implementation is as follows:

[0050] Step S21, the sleep EEG time domain information obtained from step S1, including electrooculogram (EOG), electroencephalogram (EEG), electrocardiogram (ECG) and mandibular electromyogram (EMG); ensure that the input data is a digital signal and is time series data, and each time point corresponds to a sampling value;

[0051] Step S22, using fast Fourier transform (FFT) as the core architecture of the EEG time-frequency domain conversion model, the input of the EEG time-frequency domain conversion model is the sleep EEG time domain information, and the output is the sleep EEG frequency domain feature set; the specific conversion is as follows:

[0052] Step S221, segmenting the time domain signal according to a certain time window, for example, 30 seconds can be selected as a time window;

[0053] Step S222: Apply fast Fourier transform to the data in each time window. The conversion process is as follows:

[0054] “import numpy as np

[0055] from scipy.fft import fft

[0056] #Assume that data is a time series data, fs is the sampling frequency

[0057] fs=256#sampling frequency

[0058] window_length = 30*fs # 30 seconds of data

[0059] num_windows=len(data) / / window_length

[0060] freq_features = []

[0061] for i in range(num_windows):

[0062] window_data=data[i*window_length:(i+1)*window_length]

[0063] freq_spectrum=fft(window_data)

[0064] freq_features.append(np.abs(freq_spectrum))”

[0065] Step S223: Divide the spectrum into different frequency ranges as needed, for example, define frequencies above 12 Hz as fast frequency components, and frequencies below 8 Hz as slow frequency components;

[0066] Step S23, extracting preset frequency components of interest from the spectrum, for example, extracting fast frequency components above 12 Hz and slow frequency components below 8 Hz; performing statistical analysis on the extracted frequency components to extract useful feature values, such as maximum value, average value, variance, etc.;

[0067] Step S24, saving the extracted frequency features as a sleep EEG frequency domain feature set, each sleep EEG frequency domain feature set contains multiple eigenvalues ​​corresponding to different time windows; ensuring that the format of the sleep EEG frequency domain feature set is suitable for subsequent classification statistics and analysis, usually a two-dimensional array, each row corresponds to a time window, and each column corresponds to a eigenvalue.

[0068] Step S2 converts the sleep EEG time domain information into a frequency domain feature set, extracts the features of different frequency components, and provides rich information for subsequent sleep state staging. It can not only capture subtle changes in EEG activity, but also improve the accuracy and depth of analysis, providing strong support for clinical diagnosis and research.

[0069] For step S3:

[0070] Step S3 uses the preset sleep activity feature benchmark to classify and count the sleep EEG frequency domain feature set obtained in step S2, so as to obtain the sleep state stage frequency domain analysis result of the user to be analyzed; the sleep state stage frequency domain analysis result includes the sleep state in different time periods, such as the awake period, light sleep period, deep sleep period and REM period; wherein the method for setting the sleep activity feature benchmark includes:

[0071] Step S31, obtaining basic personal information of the user to be analyzed and basic configuration information of the monitoring device; based on the impact on sleep patterns and EEG, collect basic personal information including age, gender, weight, height, health status, medication use, etc.; basic configuration information of the monitoring device involves the model, sampling rate, filter settings, etc. of the EEG monitoring device used; device configuration directly affects the quality of the data and the accuracy of the analysis results;

[0072] Step S32, extracting sleep-affecting features from the personal basic information to obtain a sleep-affecting feature information set; analyzing the user's personal basic information to extract features that may affect sleep, including age-related changes in sleep needs, different sleep patterns caused by gender differences, and the direct impact of health conditions on sleep quality; through feature extraction, obtaining a set of multiple sleep-affecting features, i.e., a sleep-affecting feature information set;

[0073] Step S33: input the sleep impact feature information set into a sleep impact comprehensive analysis model to obtain a sleep impact comprehensive index; the sleep impact comprehensive analysis model uses a machine learning or deep learning algorithm, can process multi-source data and output a sleep impact comprehensive index, which reflects the overall impact of the user's personal characteristics on his or her sleep pattern;

[0074] Step S34: extract the data acquisition quality impact feature of the basic configuration information of the monitoring device to obtain a heterogeneous vector of the impact feature of the monitoring device; the heterogeneous vector of the impact feature of the monitoring device includes the following contents:

[0075] Equipment model characteristics: Different equipment models may have different performance and accuracy;

[0076] Electrode position characteristics: the influence of the position and number of electrodes placed on signal quality;

[0077] Sampling frequency characteristics: the impact of sampling frequency on signal resolution;

[0078] Characteristics of data transmission methods: Differences in stability between wired and wireless transmission;

[0079] Calibration information characteristics: accuracy and timeliness of equipment calibration;

[0080] Step S35: performing error correlation analysis according to the heterogeneous vector of the monitoring equipment influencing features to obtain the equipment influencing error factor; quantifying the influence of the monitoring equipment on the data acquisition quality through the error correlation analysis to obtain the equipment influencing error factor; specifically, analyzing the influence of different features on the data acquisition error by establishing an error model; using statistical methods (such as regression analysis) to calculate the error factor of each feature for correcting the equipment error in data acquisition;

[0081] Step S36: correcting the sleep impact comprehensive index based on the device impact error factor to obtain a sleep impact correction index; adjusting the sleep impact comprehensive index using the device impact error factor to compensate for the impact of the device error on the sleep analysis result; the corrected index can more accurately reflect the user's sleep characteristics and reduce the interference of the device error on the analysis result;

[0082] Step S37, using the sleep impact correction index as an index condition, traversing the pre-built sleep activity benchmark database to extract the sleep activity feature benchmark corresponding to the sleep impact correction index; the sleep activity feature benchmark includes the EEG frequency domain benchmark corresponding to sleep activities at different stages; the sleep activity benchmark database contains a large number of sleep activity feature benchmarks of different groups of people under different conditions (i.e., sleep impact correction index). By matching the sleep impact correction index, the sleep activity feature benchmark closest to the user's current condition can be found, providing an accurate reference for the subsequent sleep state staging frequency domain analysis.

[0083] In this step, by collecting the basic personal information of the user to be analyzed (such as age, gender, health status, etc.), the impact of individual differences on sleep patterns can be taken into account to achieve personalized sleep status analysis; Steps S34 to S36 describe in detail how to analyze the basic configuration information of the monitoring equipment, extract the features that affect the quality of data collection, and obtain the equipment influence error factor through error correlation analysis; ensure that even when using devices of different models and configurations, relatively accurate data can be obtained to improve the accuracy of the analysis; by inputting the basic personal information and monitoring equipment information into the comprehensive analysis model of sleep impact, a comprehensive index can be output to reflect the impact of the user's personal characteristics on his or her sleep. The overall impact of sleep patterns, this comprehensive evaluation method can more comprehensively reflect the user's sleep status than a single indicator; use the pre-built sleep activity benchmark database to traverse and extract the sleep activity feature benchmark corresponding to the sleep impact correction index; the database-based matching method can quickly find the sleep activity feature benchmark that is closest to the user's current condition, providing an accurate reference for subsequent analysis; in summary, by comprehensively considering personal characteristics, equipment errors and sleep activity feature benchmarks, it can reduce the uncertainty and error in the analysis process and improve the reliability and accuracy of the analysis; it can adapt to the sleep analysis needs of different users, different equipment and under different conditions, and has high flexibility and versatility.

[0084] For step S4:

[0085] Step S4 is to input the sleep EEG time domain information obtained in step S1 into the EEG activity image recognition model, analyze the EEG activity through image recognition technology, and obtain the sleep state stage image analysis result of the user to be analyzed; the sleep state stage image analysis result includes the sleep state in different time periods, such as the awake period, light sleep period, deep sleep period and REM period; the specific implementation is as follows:

[0086] Step S41, using the sleep EEG time domain information of the user to be analyzed as input data; the sleep EEG time domain information includes one or more physiological signals such as electroencephalogram (EEG), electrooculogram (EOG), electrocardiogram (ECG) and mandibular electromyogram (EMG); the sleep EEG time domain signal will show specific patterns and changes during sleep;

[0087] Step S42, inputting the sleep EEG time domain information into a pre-trained EEG activity image recognition model; the EEG activity image recognition model is constructed based on deep learning or machine learning technology, and can automatically extract and recognize key features in sleep EEG signals; the key features include waveform shape, amplitude change, and dynamic features in time series, etc.;

[0088] Step S43: within the EEG activity image recognition model, the extracted pipe features are further processed and converted into image form; including but not limited to spectrum graphs, waveform graphs, trend graphs, etc., which can intuitively display the changes of sleep EEG signals in different time periods;

[0089] Step S44, based on the above-mentioned converted images, the EEG activity image recognition model will further perform a stage analysis of the sleep state; according to the preset sleep state classification standards (such as the light sleep stage, deep sleep stage of non-rapid eye movement (NREM) and rapid eye movement (REM) sleep stage, etc.), the sleep state in each time period is classified, and the corresponding sleep state stage image analysis results are generated; the sleep state stage image analysis results include the sleep state labels in different time periods and the corresponding confidence or probability values.

[0090] In this step, the EEG activity image recognition model is used to conduct an in-depth analysis of the sleep EEG time domain information, which can automatically extract key features and convert them into intuitive image forms, thereby improving the accuracy and reliability of sleep state staging. Through the preset sleep state classification standards, the model can classify sleep states in different time periods and generate image analysis results containing confidence or probability values, which not only reduces the reliance on manual analysis, but also improves analysis efficiency and accuracy, providing doctors and researchers with more detailed and intuitive sleep state staging information.

[0091] Specifically, the EEG activity image recognition model is a model built based on deep learning or machine learning technology, which is used to analyze the time domain information of sleep EEG. The EEG activity is analyzed through image recognition technology to obtain the sleep state stage image analysis results of the user to be analyzed; the model can automatically extract and identify the key features in the sleep EEG signal, and convert it into image form, and further perform sleep state stage analysis; the model architecture is as follows:

[0092] Data input layer: sleep EEG time domain information, including electroencephalogram (EEG), electrooculogram (EOG), electrocardiogram (ECG), mandibular electromyogram (EMG) and other physiological signals; normalize, reduce noise and resize the input data to ensure that the data format meets the model requirements;

[0093] Feature extraction layer: extracts key features of time domain signals, such as waveform shape, amplitude changes, and dynamic features in time series; converts the extracted features into image forms, such as waveform graphs and trend graphs, which can intuitively display the changes in sleep EEG signals;

[0094] Deep learning model: Use convolutional neural network model to automatically extract high-level features from images through multiple layers of convolution, pooling and activation functions; use pre-trained convolutional neural network models (such as ResNet, VGG, etc.) and fine-tune them to adapt to specific EEG image recognition tasks; map the extracted features to classification labels and output the sleep state classification results in each time period;

[0095] Classification layer: According to the preset sleep state classification standards (such as light sleep, deep sleep and rapid eye movement REM of non-rapid eye movement period), the sleep state in each time period is classified; the sleep state stage image analysis results are generated, including the sleep state labels in different time periods and the corresponding confidence or probability values.

[0096] The EEG activity image recognition model combines deep learning technology and image recognition technology to automatically extract and identify key features in sleep EEG signals, generate intuitive images, and perform stage analysis of sleep states. This model not only improves the accuracy and depth of analysis, but also provides strong support for clinical diagnosis and research.

[0097] For step S5:

[0098] Step S5 compares the sleep state staging result based on frequency domain analysis (obtained in step S3) with the sleep state staging result based on image comprehensive processing (obtained in step S4) to identify time periods with differences between the two, and further perform manual analysis on these time periods; the purpose is to improve the accuracy and reliability of the analysis and ensure that the sleep state staging result finally obtained is more accurate; the specific operations are as follows:

[0099] Step S51, comparing the frequency domain analysis result obtained in step S3 with the image analysis result obtained in step S4, and comparing the sleep states (such as awake, light sleep, deep sleep, REM sleep, etc.) of each time period in the two results one by one;

[0100] Step S52: Each time period has a clear start time and end time, as well as a corresponding sleep state label and confidence level; align the time periods of the frequency domain analysis result and the image analysis result to ensure that each time period can find a corresponding record in both results; compare the sleep state labels (such as awake, light sleep, deep sleep, REM sleep, etc.) in each time period to check whether the two are consistent; during the comparison process, if it is found that the sleep state labels of a certain time period in the two analysis results are inconsistent, mark the time period as a "difference time period";

[0101] Step S53: The identified differential time periods need to be sent to professionals for manual analysis; the manual analysis includes a detailed review of the original sleep EEG time domain information and a comprehensive consideration of related physiological signals (such as electrooculogram, electrocardiogram, mandibular electromyogram, etc.); professionals will make accurate judgments on the sleep status within the differential time periods based on their own professional knowledge and experience, and give the final analysis results.

[0102] Step S5 effectively improves the accuracy and reliability of sleep EEG analysis by introducing a manual review mechanism, ensures the accuracy of automated analysis results, and reduces misjudgments due to model limitations or errors; at the same time, it also provides professionals with the opportunity to directly participate in the analysis process and use their own professional knowledge and experience to verify and correct automated analysis results.

[0103] For step S6:

[0104] Step S6 is to summarize the results of the manual analysis in step S5 and the undisputed automatic analysis results to generate the final sleep state staging results; the specific operations are as follows:

[0105] Step S61, preliminarily integrating the sleep state staging result obtained by frequency domain analysis in step S3 with the sleep state staging result obtained by image recognition technology in step S4; at the same time, collecting the results of manual analysis in step S5, especially the analysis results of the time period that have differences in the automatic analysis stage;

[0106] Step S62: directly confirm the accuracy of the analysis results of the time periods with no objections in the automatic analysis stage; for the time periods with differences, verify and adjust them with reference to the results of manual analysis to ensure the reliability of the final results; when necessary, further comprehensive judgment and analysis of the summarized data is carried out in combination with the professional knowledge and clinical experience of the doctor, which is helpful to solve possible complex situations or controversial points and improve the accuracy of the overall analysis;

[0107] Step S63, integrating all verified and adjusted sleep state staging results into a complete report; the report should list in detail the sleep state of each time period and its confidence or probability value, and mark any key points that have been manually intervened;

[0108] Step S64: Feedback the generated final report to relevant doctors or researchers as an important basis for diagnosis and treatment decisions, and can also be used for subsequent research and improvement of existing algorithms and models.

[0109] Step S6 combines automated analysis with manual analysis, making full use of the advantages of both to improve the accuracy and reliability of sleep EEG analysis; at the same time, step S6 can also provide clear and coherent sleep state staging results, providing doctors and researchers with valuable information, which helps to better understand patients' sleep patterns and quality, thereby making more accurate diagnosis and treatment decisions.

[0110] like Figure 2 , Figure 3 As shown, an embodiment of the present invention provides a sleep EEG analysis device. The device embodiment can be implemented by software, or by hardware or a combination of software and hardware. From the hardware level, Figure 2 As shown in the figure, it is a hardware architecture diagram of an electronic device in which a sleep EEG analysis device provided by an embodiment of the present invention is located. Figure 2 In addition to the processor, memory, network interface, and non-volatile memory shown, the electronic device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing messages, etc. Taking software implementation as an example, Figure 3 As shown, as a device in a logical sense, the CPU of the electronic device in which it is located reads the corresponding computer program in the non-volatile memory into the internal memory and runs it.

[0111] like Figure 3 As shown, this embodiment provides a sleep EEG analysis device, comprising:

[0112] The sleep EEG time domain information acquisition module is used to obtain the sleep EEG time domain information of the user to be analyzed; the sleep EEG time domain information includes a variety of physiological signals such as electrooculogram, electroencephalogram, electrocardiogram and mandibular electromyogram, providing basic data for subsequent analysis;

[0113] The EEG time-frequency domain conversion module is used to input the collected sleep EEG time-domain information into the EEG time-frequency domain conversion model to obtain the sleep EEG frequency domain feature set; this module converts the time domain signal into frequency domain features through the time-frequency domain conversion technology, which facilitates the subsequent in-depth analysis of the sleep state;

[0114] The sleep state frequency domain analysis module is used to classify and count the sleep EEG frequency domain feature set using the pre-set sleep activity feature benchmark to obtain the sleep state stage frequency domain analysis results of the user to be analyzed; this module can identify and classify sleep states in different time periods, such as non-rapid eye movement (NREM) and rapid eye movement (REM);

[0115] The EEG image recognition module is used to input the sleep EEG time domain information of the user to be analyzed into the EEG image recognition model to obtain the sleep state stage image analysis results; this module uses image recognition technology to image process physiological signals such as EEG and recognize the characteristic patterns therein to realize image analysis of sleep state;

[0116] The analysis result comparison and manual review module is used to compare the sleep state staging frequency domain analysis results with the sleep state staging image analysis results, extract different time periods of the analysis results, and send these time periods to the manual analysis link; this module can automatically detect and identify inconsistencies in the analysis results to ensure the accuracy and reliability of the analysis results; at the same time, for controversial analysis results, this module can also provide a means of manual review to ensure the accuracy of the final analysis results;

[0117] The result summary module is used to summarize the manual analysis results with the undisputed analysis results to obtain the final sleep state staging results of the user to be analyzed; this module can integrate all analysis results to generate a complete sleep state staging report, providing doctors and researchers with intuitive and comprehensive analysis data.

[0118] It is to be understood that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on a sleep EEG analysis device. In other embodiments of the present invention, a sleep EEG analysis device may include more or fewer components than shown in the figure, or combine some components, or split some components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0119] The information interaction, execution process and other contents between the modules in the above-mentioned device are based on the same concept as the embodiment of the method of the present invention. For the specific contents, please refer to the description in the embodiment of the method of the present invention, and no further description is given here.

[0120] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, a sleep EEG analysis method in any embodiment of the present invention is implemented.

[0121] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the processor executes a sleep EEG analysis method in any embodiment of the present invention.

[0122] Specifically, a system or device equipped with a storage medium can be provided, on which software program code that implements the functions of any of the above-mentioned embodiments is stored, and a computer (or CPU or MPU) of the system or device can be enabled to read and execute the program code stored in the storage medium.

[0123] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.

[0124] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer by a communication network.

[0125] In addition, it should be clear that the functions of any of the above embodiments can be implemented not only by executing the program code read by the computer, but also by enabling an operating system operating on the computer to complete part or all of the actual operations based on instructions from the program code.

[0126] In addition, it can be understood that the program code read from the storage medium is written to a memory provided in an expansion board inserted into the computer or to a memory provided in an expansion module connected to the computer, and then based on the instructions of the program code, a CPU installed on the expansion board or expansion module is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above-mentioned embodiments.

[0127] It should be noted that, in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0128] A person of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk, etc., various media that can store program codes.

[0129] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A sleep EEG analysis method, characterized in that: The method comprises: Obtain the sleep EEG time domain information of the user to be analyzed; Inputting the sleep EEG time domain information into an EEG time-frequency domain conversion model to obtain a sleep EEG frequency domain feature set; Using the pre-set sleep activity feature benchmark, the sleep EEG frequency domain feature set is classified and counted to obtain the sleep state stage frequency domain analysis results of the user to be analyzed; Input the sleep EEG time domain information of the user to be analyzed into the EEG activity image recognition model to obtain the sleep state stage image analysis result of the user to be analyzed; Comparing the sleep state staging frequency domain analysis result with the sleep state staging image analysis result, extracting different time periods of the analysis results, and sending them to manual analysis; The manual analysis results are combined with the undisputed analysis results to obtain the sleep state staging results of the user to be analyzed.

2. A sleep EEG analysis method as claimed in claim 1, characterized in that: The sleep EEG time domain information includes at least one physiological signal including electrooculogram, electroencephalogram, electrocardiogram and mandibular electromyogram.

3. A sleep EEG analysis method as claimed in claim 2, characterized in that: The method for setting the sleep activity characteristic benchmark includes: Obtain basic personal information of the user to be analyzed and basic configuration information of the monitoring device; Extracting sleep impact features from the basic personal information to obtain a sleep impact feature information set; Inputting the sleep impact feature information set into a sleep impact comprehensive analysis model to obtain a sleep impact comprehensive index; Extracting features influencing data collection quality from the basic configuration information of the monitoring equipment to obtain heterogeneous vectors of monitoring equipment influencing features; Perform error correlation analysis based on the monitoring equipment influencing feature heterogeneous vectors to obtain equipment influencing error factors; Correcting the sleep impact comprehensive index based on the device impact error factor to obtain a sleep impact correction index; The sleep impact correction index is used as an index condition, and a pre-built sleep activity benchmark database is traversed to extract a sleep activity feature benchmark corresponding to the sleep impact correction index; the sleep activity feature benchmark includes an EEG frequency domain benchmark corresponding to sleep activities at different stages.

4. A sleep EEG analysis method as claimed in claim 3, characterized in that: The operation method of the EEG time-frequency domain conversion model is as follows: Segmenting the sleep EEG time domain signal according to a set time window; Applying fast Fourier transform to the sleep EEG time domain signal in each time window to convert the EEG signal from time domain representation to frequency domain representation; Based on the preset frequency classification requirements, the spectrum is divided into fast frequency components and slow frequency components; Extracting preset frequency components of interest from the spectrum, and performing statistical analysis on the extracted frequency components to extract characteristic values ​​of the frequency components; The extracted frequency features are saved as sleep EEG frequency domain feature sets, each of which contains multiple feature values ​​corresponding to different time windows.

5. A sleep EEG analysis method as claimed in claim 3, characterized in that: The sleep impact characteristic information set includes age characteristics, gender characteristics and health condition characteristics.

6. A sleep EEG analysis method as claimed in claim 5, characterized in that: The monitoring equipment influencing feature heterogeneous vector includes equipment model features, electrode position features, sampling frequency features, data transmission mode features and calibration information features.

7. A sleep EEG analysis method as claimed in claim 6, characterized in that: The structural framework of the EEG image recognition model includes a data input layer, a feature extraction layer, a deep learning model and a classification layer; Among them, the input data of the data input layer is the sleep EEG time domain information; The feature extraction layer is used to extract the key features of the sleep EEG time domain information, including the waveform shape, amplitude change and dynamic characteristics in the time series; and convert the extracted features into image form; The deep learning model uses a convolutional neural network model to automatically extract high-level features in the image form through multiple layers of convolution, pooling and activation functions; maps the extracted high-level features to classification labels, and outputs the sleep state classification results in each time period; The classification layer classifies the sleep state in each time period according to the preset sleep state classification standard and generates the sleep state stage image analysis results.

8. A sleep EEG analysis device, characterized in that: The system comprises: A sleep EEG time domain information acquisition module is used to obtain the sleep EEG time domain information of the user to be analyzed; the sleep EEG time domain information includes at least one physiological signal including electrooculogram, electroencephalogram, electrocardiogram and mandibular electromyogram; The EEG time-frequency domain conversion module is used to input the collected sleep EEG time-domain information into the EEG time-frequency domain conversion model to obtain a sleep EEG frequency domain feature set; The sleep state frequency domain analysis module is used to classify and count the sleep EEG frequency domain feature set using a preset sleep activity feature benchmark to obtain the sleep state stage frequency domain analysis result of the user to be analyzed; The EEG activity image recognition module is used to input the sleep EEG time domain information of the user to be analyzed into the EEG activity image recognition model to obtain the sleep state stage image analysis results; The analysis result comparison and manual review module is used to compare the sleep state staging frequency domain analysis results with the sleep state staging image analysis results, extract different time periods of the analysis results, and send these time periods to the manual analysis link; The result summary module is used to summarize the manual analysis results and the undisputed analysis results to obtain the final sleep state staging results of the user to be analyzed.

9. An electronic device for analyzing sleep EEG, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and characterized in that: When the computer program is executed by the processor, the steps in the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 7 are implemented.

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