Sleep state monitoring system and method integrating EEG and neuron cluster electric signals

Abstract

The invention discloses a sleep state monitoring system and method integrating EEG and neuron cluster electric signals, and relates to the technical field of sleep state monitoring, and the technical key points of the scheme are as follows: respectively decomposing EEG signals and neuron cluster electric signals into a plurality of IMF components, constructing signal features of each IMF component, and combining the signal features of each sleep state to monitor the sleep state. The method comprises the steps of identifying and reconstructing IMF components related to each sleep state, performing Fourier transform on EEG reconstruction signals and neuron cluster reconstruction electric signals to obtain frequency spectrums of the EEG reconstruction signals and the neuron cluster reconstruction electric signals, constructing a comprehensive feature vector, and constructing a sleep state identification model based on historical EEG signals and neuron cluster electric signal data. According to the method, the current comprehensive feature vector is recognized, the current sleep state is determined, more comprehensive brain activity information can be captured, and different sleep stages can be recognized more accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of sleep state monitoring, and in particular to a sleep state monitoring system and method integrating EEG and neuron cluster electrical signals. Background Art

[0002] EEG signal is a non-invasive neural signal detection technology, which is widely used in monitoring and analyzing sleep status. EEG signal collects the electrical activity of the brain through scalp electrodes, which can reflect the changes in the state of the brain in different sleep stages. Neuron cluster electrical signal refers to the electrical activity generated by the synchronous discharge of a large number of neurons in the cerebral cortex. This electrical signal is closely related to the brain's advanced functions such as cognition, emotion and behavior. In the sleep state, the neuron cluster electrical signal will also change accordingly, reflecting the different activities and functional states of the brain during sleep.

[0003] In a Chinese invention application with application publication number CN114662530A, a sleep stage classification method based on time series signal convolution and multi-signal fusion is disclosed, including: obtaining physiological signal data of a subject in a sleep state, including: 4-channel EEG signals, 1-channel ECG signals, 2-channel EOG signals, and 1-channel EMG signals; inputting the physiological signal data of the subject in a sleep state into a trained monitoring model to obtain a classification result; the trained monitoring model includes: a time series signal convolution network for performing one-dimensional convolution on each channel in the input physiological signal data separately to extract the time domain waveform change characteristics of the signal of each channel; a spatial functional connection network for extracting the relationship between any two channels in the physiological signal data; and a fully connected network for classifying the physiological signal data according to the time domain waveform change characteristics of the signal of each channel in the physiological signal data and the relationship between any two channels to obtain a classification result.

[0004] In view of the above invention, the prior art has the following deficiencies: 1. The existing technology faces the problem of limited accuracy when identifying different sleep stages. It fails to fully analyze and utilize the intrinsic structure of EEG signals and neuron cluster electrical signals, and lacks the ability to integrate EEG signals and neuron cluster electrical signals. This makes it difficult for the existing technology to accurately capture the comprehensive information of brain activity, thus affecting the accuracy of sleep state recognition. 2. The existing technology lacks effective methods to accurately identify EEG signals that are highly correlated with each sleep state, lacks means to screen and combine signals, and does not perform sufficient processing on complex signals, making it impossible to fully reflect the characteristics of each sleep state, thereby affecting the accuracy of sleep state recognition. Summary of the invention

[0005] 1. Technical issues to be resolved In view of the shortcomings of the prior art, the present invention provides a sleep state monitoring system and method that integrates EEG and neuron cluster electrical signals. The system decomposes the EEG signal and the neuron cluster electrical signal into several IMF components respectively, constructs the signal characteristics of each IMF component, identifies the IMF components related to each sleep state for reconstruction, constructs a comprehensive feature vector, and determines the current sleep state, thereby solving the problems mentioned in the background technology.

[0006] (II) Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: a sleep state monitoring method integrating EEG and neuron cluster electrical signals, comprising the following steps: Use EEG technology to collect EEG signals from different brain regions in real time, and use in vivo multi-channel synchronous recording technology of central neuron discharge to synchronously record the electrical signals of neuron clusters in different brain regions; The EEG signal and the neuron cluster electrical signal are respectively taken as the original signal, decomposed into several IMF components and a residual, each IMF component is Hilbert transformed, the IMF component signal and the signal after Hilbert transformation are combined to form an analytical signal, and the signal characteristics of each IMF component are constructed; Acquire the signal characteristics of each sleep state, combine the signal characteristics of each IMF component, identify and mark the IMF components related to each sleep state, reconstruct the IMF components to which the EEG signal and the neuron cluster electrical signal belong and are marked, and obtain the EEG reconstructed signal and the neuron cluster reconstructed electrical signal; Fourier transform is performed on the EEG reconstructed signal and the neuron cluster reconstructed electrical signal respectively to obtain the frequency spectrum of the EEG reconstructed signal and the neuron cluster reconstructed electrical signal, and a comprehensive feature vector is constructed through the frequency spectrum of the EEG reconstructed signal and the neuron cluster reconstructed electrical signal; Based on historical EEG signals and neuron cluster electrical signal data, a sleep state recognition model is constructed. The sleep state recognition model is used to identify the current comprehensive feature vector to determine the current sleep state.

[0007] Furthermore, the EEG signal and the neuron cluster electrical signal are respectively taken as the original signal and decomposed into several IMF components and a residual, including: Find all the local maximum and minimum points in the original signal, use the cubic spline interpolation method to fit the local maximum and minimum points into the upper and lower envelopes of the signal, and calculate the mean of the upper and lower envelopes to obtain the mean line m1; Subtract the mean line m1 from the original signal to obtain the new signal h1, and check whether the new signal h1 meets the IMF condition. The IMF condition is: the number of extreme points and the number of zero points are equal or differ by 1, and the mean of the upper and lower envelopes is zero.

[0008] Furthermore, if the new signal h1 does not satisfy the IMF condition, the new signal h1 is used as the original signal and the above steps are repeated until a component satisfying the IMF condition is obtained; If the new signal h1 satisfies the IMF condition, the new signal h1 is used as the first IMF component c1; Repeat the above steps for the residual signal until the residual signal becomes a monotonic function and can no longer be decomposed into components that meet the IMF conditions. In each iteration, the extracted IMF component is subtracted from the original signal to obtain the next residual signal to be decomposed.

[0009] Furthermore, the signal characteristics of each IMF component are constructed, including: By analyzing the real and imaginary parts of the signal, the instantaneous frequency and instantaneous amplitude of each IMF component signal are calculated, and the instantaneous frequency and instantaneous amplitude of each IMF component signal are combined to construct the signal feature V: , the calculation formulas of instantaneous frequency and instantaneous amplitude are as follows: in, represents the instantaneous frequency, represents the instantaneous phase, represents the instantaneous amplitude, represents the real part of the analytical signal, represents the imaginary part of the analytical signal, and t represents the time.

[0010] Furthermore, the IMF components associated with each sleep state are identified and labeled, including: The correlation between each IMF component and each sleep state is calculated by the Pearson correlation coefficient. The correlation threshold is set in advance, and the absolute value of the Pearson correlation coefficient of each IMF component is compared with the correlation threshold. When the absolute value of the Pearson correlation coefficient is greater than the correlation threshold, the corresponding IMF component is marked, otherwise, no operation is performed.

[0011] Furthermore, the IMF components to which the EEG signal and the neuron cluster electrical signal belong and are marked are reconstructed respectively. The reconstruction process is as follows: The weighting coefficient of each marked IMF component is calculated by the Pearson correlation coefficient of the marked IMF component. The weighting coefficient is: ,in, represents the weight coefficient of the k-th marked IMF component, represents the Pearson correlation coefficient of the kth marked IMF component, and the marked IMF components are reconstructed by weighted combination: ,in, represents the reconstructed signal, represents the IMF component of the kth marker.

[0012] Furthermore, constructing a comprehensive feature vector includes: Calculate the power spectral density of EEG reconstructed signal and neuron cluster reconstructed electrical signal respectively. The power spectral density is: ,in, represents the power spectral density, represents the Fourier transform of the signal, f represents the frequency, and N represents the length of the signal; Calculate the cross power spectral density of EEG reconstructed signals and neuron cluster reconstructed electrical signals, cross power spectral density: ,in, represents the cross power spectral density, , They represent the Fourier transform of EEG reconstructed signal and neuron cluster reconstructed electrical signal respectively; Furthermore, the coherence between the EEG reconstructed signal and the neuron cluster reconstructed electrical signal is calculated. ,in, Indicates coherence, represents the cross power spectral density, and They represent the power spectral density of EEG reconstructed signals and neuron cluster reconstructed electrical signals respectively; The power spectral density of EEG reconstructed signals, the power spectral density of neuron cluster reconstructed electrical signals, and coherence are spliced ​​and fused to construct a comprehensive feature vector.

[0013] Furthermore, a sleep state recognition model is constructed, including: Collect historical EEG signals and neuron cluster electrical signal data, extract comprehensive feature vectors, use clustering algorithms to perform cluster analysis on the comprehensive feature vectors, and assign a label to each cluster based on the clustering results to represent different sleep states; The clustered comprehensive feature vector data set is divided into a training set and a test set. The model is established using a machine learning algorithm. The model is trained using the data in the training set and the trained model is evaluated using the test set data. Based on the evaluation results, the model is adjusted and optimized to obtain a sleep state recognition model.

[0014] A sleep state monitoring system integrating EEG and neuron cluster electrical signals, comprising: The signal acquisition module uses EEG technology to collect EEG signals from different brain regions in real time, and synchronously records the electrical signals of neuron clusters in different brain regions through in vivo multi-channel synchronous recording technology of central neuron discharge; The signal processing module takes the EEG signal and the neuron cluster electrical signal as the original signal, decomposes them into several IMF components and a residual, performs Hilbert transform on each IMF component, combines the IMF component signal and the signal after Hilbert transform to form an analytical signal, and constructs the signal characteristics of each IMF component; obtains the signal characteristics of each sleep state, combines the signal characteristics of each IMF component, identifies and marks the IMF components related to each sleep state, and reconstructs the marked IMF components to which the EEG signal and the neuron cluster electrical signal belong, respectively, to obtain the EEG reconstructed signal and the neuron cluster reconstructed electrical signal; The feature extraction module performs Fourier transform on the EEG reconstructed signal and the neuron cluster reconstructed electrical signal respectively to obtain the frequency spectrum of the EEG reconstructed signal and the neuron cluster reconstructed electrical signal, and constructs a comprehensive feature vector through the frequency spectrum of the EEG reconstructed signal and the neuron cluster reconstructed electrical signal; The sleep state recognition module builds a sleep state recognition model based on historical EEG signals and neuron cluster electrical signal data, and uses the sleep state recognition model to identify the current comprehensive feature vector to determine the current sleep state.

[0015] (III) Beneficial effects The present invention provides a sleep state monitoring system and method integrating EEG and neuron cluster electrical signals, which has the following beneficial effects: (1) By analyzing the IMF components and instantaneous characteristics of EEG signals and neuronal cluster electrical signals, we can achieve refined processing of complex signals, which helps to better understand and analyze the intrinsic structure of the signals, thereby improving the accuracy of subsequent feature extraction, capturing the dynamic changes of signals at different time points in a more detailed manner, and understanding the generation mechanism of dynamic patterns of neurons in different brain regions.

[0016] (2) By calculating the Pearson correlation coefficient between each IMF component and each sleep state and setting a correlation threshold for screening, the IMF components that are highly correlated with each sleep state can be accurately identified. By weighted combination and reconstruction of all marked IMF components, a signal that can better reflect each sleep state can be obtained.

[0017] (3) By performing Fourier transform on the EEG reconstructed signals and the neuron cluster reconstructed electrical signals and calculating their power spectral density, cross-power spectral density, and coherence, a comprehensive feature vector containing rich information can be constructed, which can accurately reflect the electrical activity patterns of the brain in different sleep states.

[0018] (4) By integrating EEG signals and neuronal cluster electrical signals and constructing a comprehensive feature vector, more comprehensive brain activity information can be captured, which helps to more accurately identify different sleep stages. The sleep state recognition model trained using historical data can learn the subtle differences between different sleep stages, thereby improving recognition accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram of the steps of the sleep state monitoring method of the present invention integrating EEG and neuron cluster electrical signals; Figure 2 This is a schematic diagram of the structure of a sleep state monitoring system that integrates EEG and neuron cluster electrical signals according to the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] See also Figure 1 The present invention provides a sleep state monitoring method integrating EEG and neuron cluster electrical signals, comprising the following steps: Step 1: Use EEG technology to collect EEG signals from different brain regions in real time, and use in vivo multi-channel synchronous recording technology of central neuron discharge to synchronously record the electrical signals of neuron clusters in different brain regions; The step 1 includes the following contents: Step 101: Use EEG technology to collect EEG signals from different brain regions in real time. According to the 10-20 international system standard, place electrodes on the target scalp to ensure that the impedance between the electrode and the scalp is low, usually kept below 10kΩ, or even below 3kΩ, to reduce noise interference; It should be noted that the 10-20 system electrode placement method is the standard electrode placement method specified by the International Electroencephalography Society. The distance from the midpoint of the frontal pole to the root of the nose and the distance from the occipital point to the external occipital protuberance each account for 10% of the total length of this line, and the remaining points are separated by 20% of the total length of this line, so it is named the 10-20 system. An EEG amplifier is used to amplify the EEG signal. The amplifier includes multiple channels and records signals from multiple brain regions at the same time. The brain regions include the frontal lobe, parietal lobe, temporal lobe, and occipital lobe. The EEG frequency band range includes: δ band: 0.5~ 5Hz, θ band: 4~ 7.5Hz, α band: 8~ 12.5Hz, β band: 13~ 30Hz, γ band: > 30Hz; Step 102: synchronously record the electrical signals of neuron clusters in different brain regions by using an in vivo multi-channel synchronous recording technique of central neuron discharge; It should be noted that the in vivo multi-channel synchronous recording technology of central neuron discharge is a technology for monitoring the synchronous action potential of neuron groups. This method can be used to synchronously record the electrical signals of a large number of neurons in multiple brain regions. Sleep and wakefulness can selectively activate the corresponding neuron groups, and the appearance pattern of the electrical signals of the targeted neuron clusters is directly related to the occurrence of sleep and wakefulness. Step 103: pre-process the EEG signal and neuron cluster electrical signal data, and use a bandpass filter or a notch filter to filter and remove DC drift and high-frequency noise. For example, in the offline pre-processing analysis, a bandpass filter between 0.5 Hz and 75 Hz is used for the EEG signal, and a notch filter of 50 Hz is added to filter the power frequency signal; bad track detection and interpolation processing are used to process artifacts caused by electrode failure or EEG drift; When using, combine the contents of step 101 to step 103: EEG technology can directly reflect the electrical activity of the brain, and the electrical signals of neuronal clusters are a direct reflection of brain activity. Through multi-channel synchronous recording technology, the changes in electrical activity in different brain regions during sleep can be captured, thereby more accurately judging the sleep stage and depth.

[0022] Step 2: Take the EEG signal and the neuron cluster electrical signal as the original signal, decompose them into several IMF components and a residual, perform Hilbert transform on each IMF component, combine the IMF component signal and the signal after Hilbert transform to form an analytical signal, use the analytical signal to calculate the instantaneous frequency and instantaneous amplitude of each IMF component signal, and construct the signal feature; The step 2 includes the following contents: Step 201: Obtain an EEG signal and a neuron cluster electrical signal, and decompose the EEG signal and the neuron cluster electrical signal as original signals into several IMF components and a residual, specifically including: Step 211: Find all the local maximum and minimum points in the original signal, use cubic spline interpolation or other interpolation methods to fit the local maximum and minimum points into the upper and lower envelopes of the signal, and calculate the mean of the upper and lower envelopes to obtain the mean line m1: = (mean value of the upper envelope + mean value of the lower envelope) / 2; Step 2012: Subtract the mean line m1 from the original signal to obtain a new signal h1, and check whether the new signal h1 meets the IMF condition. The IMF condition is: the number of extreme points and the number of zero points are equal or differ by 1, and the mean of the upper and lower envelopes is zero; Step 2013: If the new signal h1 does not satisfy the IMF condition, the new signal h1 is used as the original signal, and the above steps 2011-2012 are repeated until a component satisfying the IMF condition is obtained; If the new signal h1 satisfies the IMF condition, the new signal h1 is used as the first IMF component c1. The calculation formula of the IMF component is: ,in, represents the mean line obtained at the i-th iteration, represents the signal before the i-th iteration, represents the IMF component of the i-th iteration; Step 2014: Repeat the above steps 2011-2013 for the residual signal until the residual signal becomes a monotonic function (i.e., residual error) and can no longer be decomposed into components that meet the IMF conditions. In each iteration, the extracted IMF components are subtracted from the original signal to obtain the next residual signal to be decomposed. Step 2015: record all extracted IMF components and the final residual (i.e., monotonic function); Step 202: Perform Hilbert transform on each IMF component, and combine the IMF component signal and the signal after Hilbert transform to form an analytical signal z: , where j is the imaginary unit, the Hilbert transform formula is as follows: in, is the IMF component signal, i.e. the real part of the analytical signal, is the signal after Hilbert transformation, that is, the imaginary part of the analytical signal, t represents the time, represents the time shift, is the time shift of the IMF component signal The value at Step 203: By analyzing the real part and the imaginary part of the signal, the instantaneous frequency and instantaneous amplitude of each IMF component signal are calculated, and the instantaneous frequency and instantaneous amplitude of each IMF component signal are combined to construct the signal feature V: , the calculation formulas of instantaneous frequency and instantaneous amplitude are as follows: in, represents the instantaneous frequency, represents the instantaneous phase, Indicates the instantaneous amplitude; It should be noted that the instantaneous frequency reflects the frequency characteristics of the signal at a certain moment, and the instantaneous amplitude represents the strength or size of the signal at a certain moment. The instantaneous frequency and instantaneous amplitude of each IMF component signal together constitute the time-frequency characteristics of the signal, which can fully reflect the dynamic changes of EEG signals and neuron cluster electrical signals in the time-frequency domain. When used, combine the contents of step 201 to step 203: By analyzing the IMF components and instantaneous characteristics of EEG signals and neuronal cluster electrical signals, we can achieve refined processing of complex signals, which helps to better understand and analyze the intrinsic structure of the signals, thereby improving the accuracy of subsequent feature extraction, capturing the dynamic changes of signals at different time points in more detail, and understanding the generation mechanism of dynamic patterns of neurons in different brain regions.

[0023] Step 3: Obtain the signal characteristics of each sleep state, combine the signal characteristics of each IMF component, identify the IMF components related to each sleep state and mark them, reconstruct the IMF components to which the EEG signal and the neuron cluster electrical signal belong and are marked, and obtain the EEG reconstructed signal and the neuron cluster reconstructed electrical signal; The step three includes the following contents: Step 301: Obtain the signal characteristics of each sleep state, combine the signal characteristics of each IMF component, and calculate the correlation between each IMF component and each sleep state through the Pearson correlation coefficient. The Pearson correlation coefficient calculation formula is: , where r represents the correlation coefficient, , Represent the IMF component and the nth data point of each sleep state signal feature, respectively. , Respectively and The mean of Step 302: pre-set a correlation threshold, compare the absolute value of the Pearson correlation coefficient of each IMF component with the correlation threshold, and when the absolute value of the Pearson correlation coefficient is greater than the correlation threshold, mark the corresponding IMF component, otherwise, do not perform any operation; Step 303: Reconstructing the marked IMF components of the EEG signal and the neuron cluster electrical signal respectively to obtain the EEG reconstructed signal and the neuron cluster reconstructed electrical signal, specifically including: The weighting coefficient of each marked IMF component is calculated by the Pearson correlation coefficient of the marked IMF component. The weighting coefficient is: ,in, represents the weight coefficient of the k-th marked IMF component, Represents the Pearson correlation coefficient of the kth marked IMF component, and reconstructs the marked IMF components of the EEG signal and the neuron cluster electrical signal respectively through weighted combination: ,in, represents the reconstructed signal, represents the IMF component of the kth marker; It should be noted that the above reconstruction is to reconstruct the EEG signal and the neuron cluster electrical signal respectively, and the operations on the IMF components to which they belong are performed separately. For example, in the calculation of the weighting coefficient, It only involves one of the EEG signals and the electrical signals of the neuron cluster. Also; It should be noted that the correlation threshold is determined according to the actual application scenario and data analysis requirements to ensure that the screened IMF components can reflect the main characteristics of each sleep state without introducing too much noise or irrelevant information. The purpose is to screen out the IMF components that are highly correlated with each sleep state, and use the IMF components that are highly correlated with each sleep state to reconstruct a signal that can better reflect each sleep state; When used, combine the contents of step 301 to step 303: By calculating the Pearson correlation coefficient between each IMF component and each sleep state and setting a correlation threshold for screening, we can accurately identify the IMF components that are highly correlated with each sleep state. By weighted combination and reconstruction of all marked IMF components, we can obtain a signal that can better reflect each sleep state.

[0024] Step 4: Perform Fourier transform on the EEG reconstructed signal and the neuron cluster reconstructed electrical signal respectively to obtain the frequency spectrum of the EEG reconstructed signal and the neuron cluster reconstructed electrical signal, and construct a comprehensive feature vector through the frequency spectrum of the EEG reconstructed signal and the neuron cluster reconstructed electrical signal; The step 4 includes the following contents: Step 401: obtaining an EEG reconstructed signal and a neuron cluster reconstructed electrical signal, performing Fourier transform on the EEG reconstructed signal and the neuron cluster reconstructed electrical signal respectively, and obtaining frequency spectra of the EEG reconstructed signal and the neuron cluster reconstructed electrical signal; Step 402: Calculate the power spectrum density of the EEG reconstructed signal and the neuron cluster reconstructed electrical signal through the spectrum of the EEG reconstructed signal and the neuron cluster reconstructed electrical signal, respectively. The power spectrum density is: ,in, represents the power spectral density, represents the Fourier transform of the signal (i.e., EEG reconstructed signal and neuron cluster reconstructed electrical signal), f represents the frequency, N represents the length of the signal, calculates the cross power spectral density of the EEG reconstructed signal and the neuron cluster reconstructed electrical signal, and calculates the coherence, coherence: ,in, Indicates coherence, represents the cross power spectral density, and Respectively represent the power spectral density and cross power spectral density of EEG reconstructed signal and neuron cluster reconstructed electrical signal: ,in, , They represent the Fourier transform of EEG reconstructed signal and neuron cluster reconstructed electrical signal respectively; Step 403: The power spectral density of the EEG reconstructed signal, the power spectral density of the neuron cluster reconstructed electrical signal and the coherence are concatenated and fused to construct a comprehensive feature vector P: ; It should be noted that concatenation is one of the most direct feature fusion methods. It simply concatenates different feature vectors together to form a longer feature vector. This method retains the information of all original features, but may increase the dimension of the feature vector, thereby increasing the complexity of the model. When used, combine the contents of step 401 to step 403: By performing Fourier transform on EEG reconstructed signals and neuron cluster reconstructed electrical signals and calculating their power spectral density, cross-power spectral density, and coherence, a comprehensive feature vector containing rich information can be constructed that can accurately reflect the electrical activity patterns of the brain in different sleep states.

[0025] Step 5: Based on the historical EEG signals and neuron cluster electrical signal data, a sleep state recognition model is constructed, and the sleep state recognition model is used to identify the current comprehensive feature vector to determine the current sleep state.

[0026] The step five includes the following contents: Step 501: Collect historical EEG signals and neuron cluster electrical signal data, extract comprehensive feature vectors, perform cluster analysis on the comprehensive feature vectors using a clustering algorithm (such as K-means, hierarchical clustering, etc.), and assign a label to each cluster based on the clustering results to represent different sleep states; Step 502: Divide the clustered comprehensive feature vector data set into a training set and a test set, use a machine learning algorithm to build a model, use the data in the training set to train the model, use the test set data to evaluate the trained model, calculate the model's accuracy, recall rate, F1 score and other evaluation indicators, and adjust and optimize the model according to the evaluation results, such as adjusting hyperparameters, selecting new features, trying different algorithms, etc., to obtain a final sleep state recognition model; Step 503: Use the sleep state recognition model to identify the current comprehensive feature vector, determine the current sleep state, pre-set the normal threshold range, compare the current comprehensive feature vector with the normal threshold range, and trigger an early warning if any component of the current comprehensive feature vector exceeds the normal threshold range; otherwise, no operation is performed; It should be noted that the threshold range is set based on the distribution range of the feature vector of the normal sleep state, and one or more thresholds are set. These thresholds can be based on statistics (such as mean, standard deviation) or data distribution (such as quantile); When used, combine the contents of step 501 to step 503: By integrating EEG signals and electrical signals of neuronal clusters and constructing comprehensive feature vectors, more comprehensive brain activity information can be captured, which helps to more accurately identify different sleep stages. The sleep state recognition model trained using historical data can learn the subtle differences between different sleep stages, thereby improving recognition accuracy.

[0027] See also Figure 2 The present invention also provides a sleep state monitoring system integrating EEG and neuron cluster electrical signals, including: a signal acquisition module, a signal processing module, a feature extraction module and a sleep state recognition module; wherein, The signal acquisition module uses EEG technology to collect EEG signals from different brain regions in real time, and synchronously records the electrical signals of neuron clusters in different brain regions through in vivo multi-channel synchronous recording technology of central neuron discharge; The signal processing module takes the EEG signal and the neuron cluster electrical signal as the original signal, decomposes them into several IMF components and a residual, performs Hilbert transform on each IMF component, combines the IMF component signal and the signal after Hilbert transform to form an analytical signal, and constructs the signal characteristics of each IMF component; obtains the signal characteristics of each sleep state, combines the signal characteristics of each IMF component, identifies and marks the IMF components related to each sleep state, and reconstructs the marked IMF components to which the EEG signal and the neuron cluster electrical signal belong, respectively, to obtain the EEG reconstructed signal and the neuron cluster reconstructed electrical signal; The feature extraction module performs Fourier transform on the EEG reconstructed signal and the neuron cluster reconstructed electrical signal respectively to obtain the frequency spectrum of the EEG reconstructed signal and the neuron cluster reconstructed electrical signal, and constructs a comprehensive feature vector through the frequency spectrum of the EEG reconstructed signal and the neuron cluster reconstructed electrical signal; The sleep state recognition module builds a sleep state recognition model based on historical EEG signals and neuron cluster electrical signal data, and uses the sleep state recognition model to identify the current comprehensive feature vector to determine the current sleep state.

[0028] In the application, the several formulas involved are all calculated by removing dimensions and taking their numerical values. The formula is a formula of the most recent real situation obtained by collecting a large amount of data and performing software simulation. The coefficients in the formula are set by technicians in this field according to actual conditions.

[0029] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in combination with electronic hardware, computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0030] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0031] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A sleep state monitoring method integrating EEG and neuron cluster electrical signals, characterized in that: The following steps are involved: Use EEG technology to collect EEG signals from different brain regions in real time, and use in vivo multi-channel synchronous recording technology of central neuron discharge to synchronously record the electrical signals of neuron clusters in different brain regions; The EEG signal and the neuron cluster electrical signal are respectively taken as the original signal, decomposed into several IMF components and a residual, each IMF component is Hilbert transformed, the IMF component signal and the signal after Hilbert transformation are combined to form an analytical signal, and the signal characteristics of each IMF component are constructed; Acquire the signal characteristics of each sleep state, combine the signal characteristics of each IMF component, identify and mark the IMF components related to each sleep state, reconstruct the IMF components to which the EEG signal and the neuron cluster electrical signal belong and are marked, and obtain the EEG reconstructed signal and the neuron cluster reconstructed electrical signal; Fourier transform is performed on the EEG reconstructed signal and the neuron cluster reconstructed electrical signal respectively to obtain the frequency spectrum of the EEG reconstructed signal and the neuron cluster reconstructed electrical signal, and a comprehensive feature vector is constructed through the frequency spectrum of the EEG reconstructed signal and the neuron cluster reconstructed electrical signal; Based on historical EEG signals and neuron cluster electrical signal data, a sleep state recognition model is constructed. The sleep state recognition model is used to identify the current comprehensive feature vector to determine the current sleep state.

2. A sleep state monitoring method integrating EEG and neuron cluster electrical signals according to claim 1, characterized in that: The EEG signal and the neuron cluster electrical signal are respectively taken as the original signal and decomposed into several IMF components and a residual, including: Find all the local maximum and minimum points in the original signal, use the cubic spline interpolation method to fit the local maximum and minimum points into the upper and lower envelopes of the signal, and calculate the mean of the upper and lower envelopes to obtain the mean line m1; Subtract the mean line m1 from the original signal to obtain a new signal h1, and check whether the new signal h1 meets the IMF condition: the number of extreme points and the number of zero points are equal or differ by 1, and the mean of the upper and lower envelopes is zero; If the new signal h1 does not meet the IMF condition, take the new signal h1 as the original signal and repeat the above steps until a component that meets the IMF condition is obtained; If the new signal h1 satisfies the IMF condition, the new signal h1 is used as the first IMF component c1; Repeat the above steps for the residual signal until the residual signal becomes a monotonic function and can no longer be decomposed into components that meet the IMF conditions. In each iteration, the extracted IMF component is subtracted from the original signal to obtain the next residual signal to be decomposed.

3. The sleep state monitoring method integrating EEG and neuron cluster electrical signals according to claim 2, characterized in that: Construct the signal characteristics of each IMF component, including: By analyzing the real and imaginary parts of the signal, the instantaneous frequency and instantaneous amplitude of each IMF component signal are calculated, and the instantaneous frequency and instantaneous amplitude of each IMF component signal are combined to construct the signal feature V: , the calculation formulas of instantaneous frequency and instantaneous amplitude are as follows: in, represents the instantaneous frequency, represents the instantaneous phase, represents the instantaneous amplitude, represents the real part of the analytical signal, represents the imaginary part of the analytical signal, and t represents the time.

4. The sleep state monitoring method integrating EEG and neuron cluster electrical signals according to claim 1, characterized in that: Identify and label the IMF components associated with each sleep state, including: The correlation between each IMF component and each sleep state is calculated by the Pearson correlation coefficient. The correlation threshold is set in advance, and the absolute value of the Pearson correlation coefficient of each IMF component is compared with the correlation threshold. When the absolute value of the Pearson correlation coefficient is greater than the correlation threshold, the corresponding IMF component is marked, otherwise, no operation is performed.

5. The sleep state monitoring method integrating EEG and neuron cluster electrical signals according to claim 4, characterized in that: The IMF components to which the EEG signal and the neuron cluster electrical signal belong and are marked are reconstructed respectively. The reconstruction process is as follows: The weighting coefficient of each marked IMF component is calculated by the Pearson correlation coefficient of the marked IMF component. The weighting coefficient is: ,in, represents the weight coefficient of the k-th marked IMF component, represents the Pearson correlation coefficient of the kth marked IMF component, and the marked IMF components are reconstructed by weighted combination: ,in, represents the reconstructed signal, represents the IMF component of the kth marker.

6. The sleep state monitoring method integrating EEG and neuron cluster electrical signals according to claim 1, characterized in that: Constructing a comprehensive feature vector includes: Calculate the power spectral density of EEG reconstructed signal and neuron cluster reconstructed electrical signal respectively. The power spectral density is: ,in, represents the power spectral density, represents the Fourier transform of the signal, f represents the frequency, and N represents the length of the signal.

7. The sleep state monitoring method integrating EEG and neuron cluster electrical signals according to claim 6, characterized in that: Calculate the cross power spectral density of EEG reconstructed signals and neuron cluster reconstructed electrical signals, cross power spectral density: ,in, represents the cross power spectral density, , They represent the Fourier transform of EEG reconstructed signal and neuron cluster reconstructed electrical signal respectively.

8. The sleep state monitoring method integrating EEG and neuron cluster electrical signals according to claim 7, characterized in that: Calculate the coherence of EEG reconstructed signals and neuron cluster reconstructed electrical signals. Coherence: ,in, Indicates coherence, represents the cross power spectral density, and They represent the power spectral density of EEG reconstructed signals and neuron cluster reconstructed electrical signals respectively; The power spectral density of EEG reconstructed signals, the power spectral density of neuron cluster reconstructed electrical signals, and coherence are spliced ​​and fused to construct a comprehensive feature vector.

9. The sleep state monitoring method integrating EEG and neuron cluster electrical signals according to claim 1, characterized in that: Build a sleep state recognition model, including: Collect historical EEG signals and neuron cluster electrical signal data, extract comprehensive feature vectors, use clustering algorithms to perform cluster analysis on the comprehensive feature vectors, and assign a label to each cluster based on the clustering results to represent different sleep states; The clustered comprehensive feature vector data set is divided into a training set and a test set. The model is established using a machine learning algorithm. The model is trained using the data in the training set and the trained model is evaluated using the test set data. Based on the evaluation results, the model is adjusted and optimized to obtain a sleep state recognition model.

10. A sleep state monitoring system integrating EEG and neuron cluster electrical signals, used to implement the method according to any one of claims 1 to 9, characterized in that: include: The signal acquisition module uses EEG technology to collect EEG signals from different brain regions in real time, and synchronously records the electrical signals of neuron clusters in different brain regions through in vivo multi-channel synchronous recording technology of central neuron discharge; The signal processing module takes the EEG signal and the neuron cluster electrical signal as the original signal, decomposes them into several IMF components and a residual, performs Hilbert transform on each IMF component, combines the IMF component signal and the signal after Hilbert transform to form an analytical signal, and constructs the signal characteristics of each IMF component; obtains the signal characteristics of each sleep state, combines the signal characteristics of each IMF component, identifies and marks the IMF components related to each sleep state, and reconstructs the marked IMF components to which the EEG signal and the neuron cluster electrical signal belong, respectively, to obtain the EEG reconstructed signal and the neuron cluster reconstructed electrical signal; The feature extraction module performs Fourier transform on the EEG reconstructed signal and the neuron cluster reconstructed electrical signal respectively to obtain the frequency spectrum of the EEG reconstructed signal and the neuron cluster reconstructed electrical signal, and constructs a comprehensive feature vector through the frequency spectrum of the EEG reconstructed signal and the neuron cluster reconstructed electrical signal; The sleep state recognition module builds a sleep state recognition model based on historical EEG signals and neuron cluster electrical signal data, and uses the sleep state recognition model to identify the current comprehensive feature vector to determine the current sleep state.

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