Multi-mode coupling type closed-loop electrical stimulation sleep cooperation signal regulation and control device and system

Abstract

The invention belongs to the technical field of human body electroencephalogram signal processing and brain regulation and control, and provides a multi-modal coupling type closed-loop electrical stimulation sleep synergy signal regulation and control device and system.The technical scheme includes that multi-modal physiological signals including EEG slow wave power, cerebral blood flow and cerebrospinal fluid flow signals are obtained; screening and preprocessing the obtained multi-modal physiological signal to obtain a preprocessed multi-modal signal; calculating the time domain correlation between the EEG slow wave power and the cerebral blood flow and the frequency domain coherence between the cerebral blood flow and the cerebrospinal fluid flow signal; introducing regulatory factors of time domain correlation of EEG slow wave power and cerebral blood flow and frequency domain coherence of cerebral blood flow and cerebrospinal fluid flow signals to obtain a signal stimulation intensity calculation formula; and optimizing the signal stimulation intensity by combining a signal stimulation intensity calculation formula and the constructed double-layer optimization architecture to obtain the optimized signal stimulation intensity. And an accurate sleep regulation and control signal is generated.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of human brain electrical signal processing and brain regulation, and particularly relates to a multi-modal coupling closed-loop electric stimulation sleep cooperative signal regulation device and system. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute prior art.

[0003] Sleep disorder patients show insomnia, circadian rhythm disorder, night restlessness or daytime excessive sleepiness, etc., which is closely related to the pathological changes of the disease itself (such as deposition of beta-amyloid in the brain, tau protein tangle, neurotransmitter disorder) and age-related physiological degradation, and early intervention may delay sleep deterioration associated with cognitive decline; The traditional sleep regulation method adopts a single target stimulation (Single-Target Modulation) method. The traditional single target stimulation is a new emerging precise intervention strategy, that is, by specifically regulating a key neurotransmitter, receptor or biological clock related molecule to improve sleep problems. However, the single target stimulation cannot effectively coordinate the multiple rhythms of nerves, blood vessels and cerebrospinal fluid, resulting in poor regulation effect. At the same time, the traditional electroencephalogram (EEG) and deep brain stimulation (DBS) electrodes have low spatial resolution (usually > 5 mm), which will introduce significant off-target effects in the research and treatment of Alzheimer's disease related sleep disorders, that is, non-specific effects of stimulation or recording, resulting in mixed signals or unintended neural regulation, and the generated regulation signal is inaccurate. SUMMARY

[0004] In order to solve at least one technical problem in the background art, the present application provides a multi-modal coupling closed-loop electric stimulation sleep cooperative signal regulation device and system, which constructs a multi-scale cooperative regulation mechanism and generates a precise sleep regulation signal.

[0005] In order to achieve the above purpose, the present application adopts the following technical solutions: The first aspect of the present application provides a multi-modal coupling closed-loop electric stimulation sleep cooperative signal regulation device, comprising: A signal acquisition module for acquiring multi-modal physiological signals, including EEG slow wave power, cerebral blood flow and cerebrospinal fluid flow signals; A preprocessing module for screening and preprocessing the acquired multi-modal physiological signals to obtain preprocessed multi-modal signals; A correlation calculation module for calculating the time domain correlation of EEG slow wave power and cerebral blood flow, and the frequency domain coherence of cerebral blood flow and cerebrospinal fluid flow signals; a signal stimulation intensity adjustment module, which is used to introduce a regulation factor of the time-domain correlation of EEG slow wave power and cerebral blood flow, and the frequency-domain coherence of the cerebral blood flow and cerebrospinal fluid flow signals, to obtain a signal stimulation intensity calculation formula; and the signal stimulation intensity is optimized by combining the signal stimulation intensity calculation formula and the constructed double-layer optimization framework to obtain an optimized signal stimulation intensity.

[0006] Further, in the preprocessing module, the acquired multi-modal physiological signals are screened and preprocessed to obtain preprocessed multi-modal signals, which include: The process of preprocessing the acquired EEG slow wave power includes: performing adaptive filtering processing on the EEG slow wave power, and performing phase synchronization enhancement on the adaptive filtered EEG slow wave power; The process of preprocessing the acquired cerebral blood flow includes: using a blood vessel pulsation model to eliminate respiratory low-frequency interference; The process of preprocessing the acquired cerebrospinal fluid flow signal includes: gradient field correction, pulsation cycle locking, and temperature drift compensation.

[0007] Further, in the correlation calculation module, the calculation formula of the time-domain correlation of the EEG slow wave power and the cerebral blood flow is: , wherein, represents the EEG slow wave power signal, represents the CSF flow signal, represents the mean value of the EEG slow wave power signal, represents the mean value of the CSF flow signal, represents the total number of the EEG slow wave power signal, t represents time.

[0008] Further, in the correlation calculation module, the calculation formula of the frequency-domain coherence of the cerebral blood flow and the cerebrospinal fluid flow signal is: , wherein, represents a coherence coefficient threshold value, and a coherence coefficient exceeding the threshold value indicates that the cerebral blood flow CBF and the CSF pulsation exist significant phase synchronization in the 0.1 Hz frequency band, reflecting the blood vessel-neural coupling state.

[0009] Further, in the signal stimulation intensity adjustment module, the signal stimulation intensity calculation formula is: , wherein, is an EEG-CBF correlation coefficient regulation factor, is a CBF-CSF correlation coefficient regulation factor, Independently adjust weights for CSF; 、 、 are the weight coefficients corresponding to each signal; EEG Normalized power of the frequency band, , is the EEG-CBF correlation coefficient, , is the frequency domain coherence coefficient of CBF-CSF; when the coherence is greater than 0.7 Close to saturation value 1.8; Dynamic adjustment using PID controller: ,in, , is the proportionality coefficient, is the integration coefficient, is the differential coefficient.

[0010] Furthermore, in the signal stimulation intensity adjustment module, the constructed two-layer optimization architecture includes an outer loop and an inner loop; In the outer loop, the eigenvalues ​​λ of the trimodal joint covariance matrix of EEG slow wave power, cerebral blood flow CBF and CSF flow signals are calculated respectively. i , and then according to the set period, the joint eigenvalue λ i , update the basic weights α, β, γ; In the inner circulation, the EEG-CBF time domain correlation coefficient matrix and the CBF-CSF frequency domain coherence coefficient matrix are calculated in real time based on the sliding window method, and the optimal correlation coefficient adjustment factor is predicted in combination with the vascular response model. 、 and .

[0011] A second aspect of the present invention provides a multimodal coupled closed-loop electrical stimulation sleep coordinated regulation system, comprising the multimodal coupled closed-loop electrical stimulation sleep coordinated signal regulation device as described in the first embodiment.

[0012] A third aspect of the present invention provides a computer-readable storage medium.

[0013] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps: Acquire multimodal physiological signals, including EEG slow wave power, cerebral blood flow, and cerebrospinal fluid flow signals; Screening and preprocessing the acquired multimodal physiological signals to obtain preprocessed multimodal signals; Calculate the time domain correlation between EEG slow wave power and cerebral blood flow, and the frequency domain coherence between cerebral blood flow and cerebrospinal fluid flow signals; The time-domain correlation of the EEG slow wave power and the cerebral blood flow, the frequency-domain coherence of the cerebral blood flow and the cerebrospinal fluid flow signal are introduced as a regulation factor to obtain a signal stimulation intensity calculation formula. The signal stimulation intensity is optimized by combining the signal stimulation intensity calculation formula and the double-layer optimization architecture to obtain the optimized signal stimulation intensity.

[0014] The fourth aspect of the present application provides a computer device.

[0015] The computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the following steps when executing the program: Obtaining multi-modal physiological signals, including EEG slow wave power, cerebral blood flow and cerebrospinal fluid flow signals; Screening and preprocessing the obtained multi-modal physiological signals to obtain preprocessed multi-modal signals; Calculating the time-domain correlation of the EEG slow wave power and the cerebral blood flow, the frequency-domain coherence of the cerebral blood flow and the cerebrospinal fluid flow signal; The time-domain correlation of the EEG slow wave power and the cerebral blood flow, the frequency-domain coherence of the cerebral blood flow and the cerebrospinal fluid flow signal are introduced as a regulation factor to obtain a signal stimulation intensity calculation formula. The signal stimulation intensity is optimized by combining the signal stimulation intensity calculation formula and the double-layer optimization architecture to obtain the optimized signal stimulation intensity.

[0016] The fifth aspect of the present application provides a program product.

[0017] The program product is a computer program product, comprising a computer program, and the computer program implements the following steps when executed by a processor: Obtaining multi-modal physiological signals, including EEG slow wave power, cerebral blood flow and cerebrospinal fluid flow signals; Screening and preprocessing the obtained multi-modal physiological signals to obtain preprocessed multi-modal signals; Calculating the time-domain correlation of the EEG slow wave power and the cerebral blood flow, the frequency-domain coherence of the cerebral blood flow and the cerebrospinal fluid flow signal; The time-domain correlation of the EEG slow wave power and the cerebral blood flow, the frequency-domain coherence of the cerebral blood flow and the cerebrospinal fluid flow signal are introduced as a regulation factor to obtain a signal stimulation intensity calculation formula. The signal stimulation intensity is optimized by combining the signal stimulation intensity calculation formula and the double-layer optimization architecture to obtain the optimized signal stimulation intensity.

[0018] Compared with the prior art, the present application has the following advantages: The application calculates the time domain correlation of EEG slow wave power and cerebral blood flow, the frequency domain coherence of cerebral blood flow and cerebrospinal fluid flow signals based on the obtained multi-modal physiological signals, introduces the regulation factor of the time domain correlation of EEG slow wave power and cerebral blood flow, the frequency domain coherence of cerebral blood flow and cerebrospinal fluid flow signals, and optimizes the signal stimulation intensity through the double-layer optimization architecture to obtain the optimized signal stimulation intensity, so that the regulation signal can be generated more accurately.

[0019] The advantages of the additional aspects of the application will be partially given in the following description, partially become obvious from the following description, or be known through the practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0020] The drawings accompanying the specification of this application form a part thereof, serve to provide further understanding of the application, and together with the description of the exemplary embodiments of the application, serve to explain the application, and do not constitute improper limitations on the application.

[0021] Figure 1 It is a multi-modal coupling type closed-loop electric stimulation sleep cooperative signal regulation device block diagram provided by the embodiment of the application. Figure 2 It is a multi-modal coupling type closed-loop electric stimulation sleep cooperative signal regulation method flow chart provided by the embodiment of the application. DETAILED DESCRIPTION

[0022] The application will be further described below in combination with the drawings and embodiments.

[0023] It should be pointed out that the following detailed description is all exemplary, and is intended to provide further explanation of the application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as generally understood by those skilled in the art to which the application belongs.

[0024] It should be noted that the terms used herein are only for the purpose of describing specific embodiments, and are not intended to limit the exemplary embodiments according to the application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form, and in addition, it should be understood that when the terms "comprise" and / or "include" are used in the specification, they indicate the presence of a feature, step, operation, device, component and / or their combination.

[0025] TERMINOLOGY Electroencephalogram (EEG) is a method of recording brain activity using electrophysiological indicators. When the brain is active, a large number of neuron synchronous post-synaptic potentials are summed to form. It records the change of electric wave when the brain is active, which is the overall reflection of the electrical physiological activity of brain nerve cells on the surface of the cerebral cortex or scalp.

[0026] Cerebral Blood Flow (CBF) refers to the amount of blood flowing through brain tissue per unit time, usually measured in milliliters per 100 grams of brain tissue per minute (mL / 100g / min). It is a key physiological indicator for maintaining normal brain function and metabolism, closely related to brain oxygen supply, energy metabolism and neural activity.

[0027] CSF flow signal refers to a specific image manifestation produced by Cerebrospinal Fluid (CSF) during flow, usually detected by Magnetic Resonance Imaging (MRI) technology. The flow of CSF is different from Cerebral Blood Flow (CBF), which is produced by the choroid plexus, flows through the ventricular system (lateral ventricle→third ventricle→mesencephalic aqueduct→fourth ventricle) and subarachnoid space, and is finally absorbed into the venous system through the arachnoid granules.

[0028] Embodiment One As shown in Figure 1 and Figure 2 , the present embodiment provides a multi-modal coupled closed-loop electrical stimulation sleep cooperative signal regulation device, comprising: a signal acquisition module 101 for acquiring multi-modal physiological signals; In the present embodiment, the multi-modal physiological signals acquired in the signal acquisition module include EEG slow wave power, Cerebral Blood Flow (CBF) and CSF flow signal; Among them, the EEG slow wave power is collected by a set of high-density microelectrode array, specifically including 62 channels of EEG original signals; Cerebral Blood Flow (CBF) is collected by an Optical Coherence Tomography (OCT) array or an ultrasonic Doppler probe group; The CSF flow is obtained by a phase contrast MRI sequence array or an implanted piezoelectric sensor matrix.

[0029] a pre-processing module 102 for screening and pre-processing the acquired multi-modal physiological signals to obtain pre-processed multi-modal signals; Among them, the process of pre-processing the acquired EEG slow wave power in the pre-processing module includes: Adaptive filtering of EEG slow wave power, specifically, the acquired 62 channels of EEG original signals are combined with independent component analysis to decompose the EEG original signals into independent components, calculate the time domain waveform, spectral features (such as Electro-oculogram (EOG) low frequency high amplitude, electromyogram (EMG) high frequency burst) and spatial topology map of each independent component; calculate the Pearson correlation coefficient of EOG and EMG channels of each independent component, and eliminate components with absolute value of correlation coefficient >0.7; The adaptive filtered EEG slow wave power is phase-synchronized enhanced, specifically, a common spatial pattern (CSP) method is used to calculate a specific frequency band, such as a delta band (0.5-4Hz) signal covariance matrix and corresponding eigenvalues, and principal component signals with eigenvalues >0.8 are reserved; Wherein, the process of preprocessing the obtained cerebral blood flow CBF includes: using existing blood vessel pulsation models such as elastic cavity model and transmission line model, using 1Hz high-pass filter to separate arterial pulsation component and eliminate respiratory low-frequency (0.1-0.3Hz) interference; The laser speckle image is reconstructed by using a PCA processing method to reduce the influence of the reflectivity difference of the cortex surface; Perfusion compensation: according to the covariance matrix formula, a CBF-EEG phase coupling model is established, and a signal compensation algorithm is triggered when the phase difference is greater than π / 2; Wherein, the process of preprocessing the obtained CSF flow signal includes: Real-time PCA technology is used to extract the first three principal components of the MRI scanning gradient field for dynamic cancellation, the signal-to-noise ratio is improved by 4.6dB, and the CSF flow signal after gradient field correction is obtained; The CSF flow signal after gradient field correction is matched with the cardiac cycle (200-600ms window after R wave) through the blood vessel-CSF coupling model through cross-correlation analysis, the flow pulse with a correlation coefficient greater than 0.6 is retained, and the pulsation cycle is locked; Finally, the PID controller integrates the probe temperature change rate (dT / dt>0.1℃ / s) to adjust the flow meter baseline in real time. Through the above preprocessing process, the logic progression of basic noise elimination, physiological signal synchronization and environmental factor compensation is ensured, and the data quality of the CSF flow signal is guaranteed.

[0030] The correlation calculation module 103 is configured to perform time domain cross-correlation analysis and frequency domain coherence analysis on the preprocessed multi-modal signals respectively. The correlation calculation module includes a time domain cross-correlation analysis module and a frequency domain coherence analysis module. The time domain cross-correlation analysis module is configured to perform time domain cross-correlation analysis on the preprocessed EEG slow wave power and cerebral blood flow CBF signal. In this embodiment, the correlation of the EEG slow wave power and the cerebral blood flow CBF is calculated The calculation formula is: , Wherein, EEG slow wave power signal, CSF flow signal, a mean value representing an EEG slow wave power signal, a mean value representing a CBF signal, a total number representing an EEG slow wave power signal, t representing time; determining a coupling state of the multi-modal signals based on the time-domain cross-correlation analysis result and a preset condition; When the time-domain cross-correlation analysis result of the EEG slow wave power and the CBF is greater than a set value, such as 0.6, it is considered that the signals are coupled. For example, when the absolute value of the time-domain cross-correlation analysis result of the EEG slow wave power and the CBF is greater than 0.6, it is determined that the strong blood vessel-neural coupling state is determined. The frequency domain coherence analysis module is configured to perform frequency domain coherence analysis on the preprocessed CBF and CSF flow signals. By using a common spatial pattern (CSP) filtering method, the coherence coefficient of the CBF and the CSF flow signals in the 0.1 Hz frequency band is calculated, and the calculation formula is: , wherein, The coherence coefficient threshold is 0.4 in this embodiment. When the coherence coefficient exceeds the threshold 0.4, it indicates that there is significant phase synchronization between the CBF and the CSF pulsation in the 0.1 Hz frequency band, reflecting the blood vessel-neural coupling state. This result provides a key input for closed-loop electrical stimulation parameter optimization, for example, for dynamically adjusting the target value of the PID controller. This criterion is one of the core indicators for quantifying the coordination of multi-modal physiological signals during sleep. The signal stimulation intensity adjustment module is used to dynamically adjust the stimulation intensity based on the time-domain cross-correlation analysis result and the frequency domain coherence analysis result, and the signal stimulation intensity calculation formula is obtained. The signal stimulation intensity is optimized by combining the signal stimulation intensity calculation formula and the constructed double-layer optimization architecture to obtain the optimized signal stimulation intensity.

[0031] The signal stimulation intensity adjustment module 104 includes a signal stimulation intensity calculation module and a signal optimization module. The signal stimulation intensity calculation module is configured to dynamically adjust the stimulation intensity based on the time-domain cross-correlation analysis result and the frequency domain coherence analysis result, and obtain the signal stimulation intensity calculation formula. The original stimulation intensity formula directly weights and sums the final stimulation intensity according to the weight proportion of each modal signal, and cannot dynamically adjust according to the correlation between signals. The formula is: , The embodiment can introduce a correlation coefficient adjustment factor θ, which can dynamically adjust according to the correlation between signals. The specific stimulation intensity adjustment formula is: , in, is the EEG-CBF correlation coefficient adjustment factor, is the CBF-CSF correlation coefficient adjustment factor, Independently adjust weights for CSF; 、 、 are the weight coefficients corresponding to each signal; For EEG Normalized power of the frequency band, , is the EEG-CBF correlation coefficient, , is the frequency domain coherence coefficient of CBF-CSF; when the coherence is greater than 0.7 Close to saturation value 1.8; Dynamic adjustment using PID controller: ,in, , is the proportionality coefficient, is the integration coefficient, is the differential coefficient; In this embodiment, , , ; The signal optimization module is configured to: optimize the signal stimulation intensity by combining the signal stimulation intensity calculation formula and the constructed two-layer optimization architecture to obtain the optimized signal stimulation intensity; In this embodiment, the two-layer optimization architecture includes an inner loop and an outer loop; In the outer loop, the eigenvalue λ of the trimodal joint covariance matrix of EEG slow wave power, cerebral blood flow CBF and CSF flow signals is first calculated i , and then according to the set period, the joint eigenvalue λ i , update the basic weights α, β, γ, such as updating the basic weights α, β, γ every 24 hours; In this embodiment, the eigenvalue λ of the trimodal joint covariance matrix of EEG slow wave power, cerebral blood flow CBF and CSF flow signal is i The calculation method is as follows: the pre-processed EEG slow wave power, cerebral blood flow CBF and CSF flow signals are time-aligned to obtain multidimensional time series features, the multidimensional time series features are combined with the covariance matrix formula to calculate the joint covariance, and then the joint covariance is characteristically decomposed to obtain the eigenvalues ​​λ1, λ2, λ3 of each modal signal in the joint covariance matrix; The specific update formula is: ; In the inner loop, the EEG-CBF time-domain cross-correlation coefficient matrix and the CBF-CSF frequency-domain coherence coefficient matrix are calculated in real time based on the sliding window method, and the optimal correlation coefficient adjustment factor is predicted by combining the vascular response model 、 and ; Specifically, the method comprises the following steps: The EEG-CBF time-domain cross-correlation coefficient matrix and the CBF-CSF frequency-domain coherence coefficient matrix are calculated in real time based on the sliding window method, and the qEC reference value and the qCC factor are determined; In this embodiment, the sliding window can be set according to the requirements of the EEG-CBF time-domain cross-correlation coefficient matrix and the CBF-CSF frequency-domain coherence coefficient matrix. For example, a 30-second sliding window can be set for calculating the EEG-CBF time-domain cross-correlation coefficient matrix, and a 10-minute sliding window can be set for calculating the CBF-CSF frequency-domain coherence coefficient matrix; When the EEG-CBF time-domain cross-correlation coefficient matrix is obtained, the maximum absolute value of the EEG-CBF time-domain cross-correlation coefficient is taken as the qEC reference value; When the CBF-CSF frequency-domain coherence coefficient matrix is obtained, the 0.1 Hz feature value is extracted and mapped by a Sigmoid function to obtain the qCC factor; In this embodiment, the vascular response model adopts a two-chamber windkessel model; The two-chamber windkessel model divides the cerebral vascular system into an arterial chamber (high elasticity) and a venous chamber (high capacity), and describes the pressure-flow relationship through a hemodynamic equation: The arterial chamber equation is: , wherein, is the arterial compliance, is the arterial pressure, is the input blood flow, which is modulated by the CBF pulsation amplitude, is the arterial resistance, which is negatively correlated with the qEC reference value; The venous chamber equation is: , wherein, is the venous compliance, is the venous pressure, is the venous resistance, which is modulated by the CSF pulse frequency, is the cerebrospinal fluid pressure, which is correlated with the CSF pulse frequency; The specific prediction process comprises: The qEC reference value, qCC factor, CBF pulsatile amplitude and CSF pulse frequency are input as input parameters into the two-compartment windkessel model, the input CBF pulsatile amplitude modulates the input blood flow , the input qCC factor adjusts the venous resistance , the differential equations are solved to obtain and , and finally the adjustment factors , and are calculated wherein , , ; a target function and constraint conditions of maximizing dynamic coordination of the neurovascular-cerebrospinal fluid system are constructed , wherein and are ideal correlation coefficients the constraint conditions are: physiological range constraints , and values can be set according to actual needs dynamic stability conditions do not exceed the upper limit of intracranial pressure. At the same time, the optimization also includes: when >2.5 or >2.0, triggering the gradient descent algorithm to limit the weight increase ≤10% / min, and if the CSF pulse frequency >0.3Hz, immediately reducing the γ coefficient by 50% to prevent overstimulation if the three-modal correlation coefficients are all less than 0.2 for 5 minutes, switch to the standby single-modal mode, fixing =1.0, =0.5 In addition, when the EEG and CSF phase difference >π / 2, an artificial phase offset Δφ=π / 4 is inserted to optimize the synchronization.

[0032] Embodiment Two The embodiment provides a multi-modal coupled closed-loop electrical stimulation sleep cooperative signal regulation system, which comprises the multi-modal coupled closed-loop electrical stimulation sleep cooperative signal regulation device as described in Embodiment One.

[0033] It should be noted that the specific implementation mode of the multi-modal coupled closed-loop electrical stimulation sleep cooperative signal regulation system of the embodiment of the present application is similar to that of the multi-modal coupled closed-loop electrical stimulation sleep cooperative signal regulation device of the embodiment of the present application, and specific reference can be made to the description in the device part. In order to reduce redundancy, this part will not be repeated here.

[0034] Embodiment three The embodiment provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to realize the following steps: acquiring multi-modal physiological signals including EEG slow wave power, cerebral blood flow and cerebrospinal fluid flow signals; screening and preprocessing the acquired multi-modal physiological signals to obtain preprocessed multi-modal signals; calculating time-domain correlation of the EEG slow wave power and the cerebral blood flow, and frequency-domain coherence of the cerebral blood flow and the cerebrospinal fluid flow signals; introducing a regulation factor of the time-domain correlation of the EEG slow wave power and the cerebral blood flow, and the frequency-domain coherence of the cerebral blood flow and the cerebrospinal fluid flow signals to obtain a signal stimulation intensity calculation formula; optimizing the signal stimulation intensity by combining the signal stimulation intensity calculation formula and a constructed double-layer optimization architecture to obtain an optimized signal stimulation intensity.

[0035] Embodiment four The embodiment provides a computer device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, and the processor realizes the following steps when executing the program: acquiring multi-modal physiological signals including EEG slow wave power, cerebral blood flow and cerebrospinal fluid flow signals; screening and preprocessing the acquired multi-modal physiological signals to obtain preprocessed multi-modal signals; calculating time-domain correlation of the EEG slow wave power and the cerebral blood flow, and frequency-domain coherence of the cerebral blood flow and the cerebrospinal fluid flow signals; introducing a regulation factor of the time-domain correlation of the EEG slow wave power and the cerebral blood flow, and the frequency-domain coherence of the cerebral blood flow and the cerebrospinal fluid flow signals to obtain a signal stimulation intensity calculation formula; optimizing the signal stimulation intensity by combining the signal stimulation intensity calculation formula and a constructed double-layer optimization architecture to obtain an optimized signal stimulation intensity.

[0036] Embodiment five The embodiment provides a program product, which is a computer program product, comprising a computer program, and the computer program is executed by a processor to realize the following steps: acquiring multi-modal physiological signals including EEG slow wave power, cerebral blood flow and cerebrospinal fluid flow signals; screening and preprocessing the acquired multi-modal physiological signals to obtain preprocessed multi-modal signals; calculating time-domain correlation of the EEG slow wave power and the cerebral blood flow, and frequency-domain coherence of the cerebral blood flow and the cerebrospinal fluid flow signals; The signal stimulation intensity calculation formula is obtained by introducing a time domain correlation of EEG slow wave power and cerebral blood flow, and a frequency domain coherence of the cerebral blood flow and cerebrospinal fluid flow signals; The signal stimulation intensity is optimized by combining the signal stimulation intensity calculation formula and the constructed double-layer optimization framework to obtain the optimized signal stimulation intensity.

[0037] Those skilled in the art will understand that embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage, etc.) containing computer-usable program code.

[0038] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in one or more blocks.

[0039] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction apparatus, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in one or more blocks.

[0040] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide a process for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in one or more blocks.

[0041] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing relevant hardware, and the program can be stored in a computer readable storage medium. When the program is executed, the program can include the processes of the above-mentioned embodiment methods. The storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM), a random access memory (RAM), or the like.

[0042] The above only describes the preferred embodiments of the present application and is not used to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A multi-modal coupled closed-loop electrical stimulation sleep co-regulatory signal modulation device, characterized in that, The method comprises the following steps: a signal acquisition module for acquiring multi-modal physiological signals including EEG slow wave power, cerebral blood flow and cerebrospinal fluid flow signals; a preprocessing module for screening and preprocessing the acquired multi-modal physiological signals to obtain preprocessed multi-modal signals; a correlation calculation module for calculating the time domain correlation of EEG slow wave power and cerebral blood flow, and the frequency domain coherence of cerebral blood flow and cerebrospinal fluid flow signals; a signal stimulation intensity adjustment module for introducing an adjustment factor of the time domain correlation of EEG slow wave power and cerebral blood flow, and the frequency domain coherence of cerebral blood flow and cerebrospinal fluid flow signals to obtain a signal stimulation intensity calculation formula; and optimizing the signal stimulation intensity by combining the signal stimulation intensity calculation formula and a constructed double-layer optimization architecture to obtain an optimized signal stimulation intensity.

2. The multi-modal coupled closed loop electrical stimulation sleep co-regulatory signaling device of claim 1, wherein, In the preprocessing module, the screening and preprocessing of the acquired multi-modal physiological signals to obtain preprocessed multi-modal signals comprises: the preprocessing process of the acquired EEG slow wave power comprises: adaptive filtering of the EEG slow wave power, and phase synchronization enhancement of the adaptive filtered EEG slow wave power; the preprocessing process of the acquired cerebral blood flow comprises: elimination of respiratory low-frequency interference by using a blood vessel pulsation model; the preprocessing process of the acquired cerebrospinal fluid flow signal comprises: gradient field correction, pulsation cycle locking and temperature drift compensation.

3. The multi-modal coupled closed loop electrical stimulation sleep co-regulatory signaling device of claim 1, wherein, In the correlation calculation module, the calculation formula of the time domain correlation of EEG slow wave power and cerebral blood flow is: ， wherein, denotes the EEG slow wave power signal, denotes the CSF flow signal, denotes the mean of the EEG slow wave power signal, denotes the mean of the CSF flow signal, denotes the total number of the EEG slow wave power signal, t denotes the time.

4. The multi-modal coupled closed loop electrical stimulation sleep co-regulatory signaling device of claim 1, wherein, In the correlation calculation module, the calculation formula of the frequency domain coherence of cerebral blood flow and cerebrospinal fluid flow signals is: ， wherein, represents the coherence coefficient threshold value, and the coherence coefficient exceeding the threshold value indicates that the CBF and the CSF pulsation have significant phase synchronization in the 0.1 Hz frequency band, reflecting the vascular-neural coupling state.

5. The multi-modal coupled closed loop electrical stimulation sleep co-regulatory signaling device of claim 1, wherein, In the signal stimulation intensity adjustment module, the signal stimulation intensity calculation formula is: ， Wherein, is the EEG-CBF correlation coefficient adjustment factor, is the CBF-CSF correlation coefficient adjustment factor, is the CSF independent adjustment weight; , , is the weight coefficient corresponding to each signal respectively; is the EEG normalization power of the frequency band, , is the EEG-CBF correlation coefficient, , is the CBF-CSF frequency domain coherence coefficient; when the coherence is greater than 0.7 approaches the saturation value 1.8; PID controller is used for dynamic adjustment: , wherein, , is the proportional coefficient, is the integral coefficient, is the differential coefficient.

6. The multi-modal coupled closed loop electrical stimulation sleep co-regulatory signaling device of claim 5, wherein, In the signal stimulation intensity adjusting module, the constructed double-layer optimization architecture comprises an outer loop and an inner loop; in the outer loop, eigenvalues λ of a three-modal joint covariance matrix of EEG slow wave power, cerebral blood flow CBF and CSF flow signal are respectively calculated i Then, according to a set period, the eigenvalues λ are combined to update the basic weights α, β and γ i ​ In the inner loop, the EEG-CBF time-domain cross-correlation coefficient matrix and the CBF-CSF frequency-domain coherence coefficient matrix are calculated in real time based on the sliding window method, and the optimal correlation coefficient adjustment factor is predicted by combining the vascular response model , and .

7. A multimodal coupled closed-loop electrical stimulation sleep co-regulatory signal modulation system, characterized in that, The method comprises the following steps:

8. A computer readable storage medium having stored thereon a computer program, characterized in that, acquiring multi-modal physiological signals including EEG slow wave power, cerebral blood flow and cerebrospinal fluid flow signals; screening and preprocessing the acquired multi-modal physiological signals to obtain preprocessed multi-modal signals; calculating the time domain correlation of EEG slow wave power and cerebral blood flow, and the frequency domain coherence of cerebral blood flow and cerebrospinal fluid flow signals; introducing an adjustment factor of the time domain correlation of EEG slow wave power and cerebral blood flow, and the frequency domain coherence of cerebral blood flow and cerebrospinal fluid flow signals to obtain a signal stimulation intensity calculation formula; optimizing the signal stimulation intensity by combining the signal stimulation intensity calculation formula and a constructed double-layer optimization architecture to obtain an optimized signal stimulation intensity. The processor implements the following steps when executing the program:

9. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, acquiring multi-modal physiological signals including EEG slow wave power, cerebral blood flow and cerebrospinal fluid flow signals; screening and preprocessing the acquired multi-modal physiological signals to obtain preprocessed multi-modal signals; calculating the time domain correlation of EEG slow wave power and cerebral blood flow, and the frequency domain coherence of cerebral blood flow and cerebrospinal fluid flow signals; introducing an adjustment factor of the time domain correlation of EEG slow wave power and cerebral blood flow, and the frequency domain coherence of cerebral blood flow and cerebrospinal fluid flow signals to obtain a signal stimulation intensity calculation formula; ​ The signal stimulation intensity is optimized by combining the signal stimulation intensity calculation formula and the constructed double-layer optimization architecture to obtain an optimized signal stimulation intensity.

10. A program product, the program product being a computer program product comprising a computer program, characterized in that The computer program, when executed by a processor, implements the following steps: Obtaining multi-modal physiological signals, including EEG slow wave power, cerebral blood flow and cerebrospinal fluid flow signals; Screening and preprocessing the obtained multi-modal physiological signals to obtain preprocessed multi-modal signals; Calculating the time-domain correlation of EEG slow wave power and cerebral blood flow, and the frequency-domain coherence of cerebral blood flow and cerebrospinal fluid flow signals; Introducing a regulation factor of the time-domain correlation of EEG slow wave power and cerebral blood flow, and the frequency-domain coherence of cerebral blood flow and cerebrospinal fluid flow signals to obtain a signal stimulation intensity calculation formula; Optimizing the signal stimulation intensity by combining the signal stimulation intensity calculation formula and the constructed double-layer optimization architecture to obtain an optimized signal stimulation intensity.

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