Multi-modal coupled closed-loop electrical stimulation sleep co-signaling signal modulation device and system

Abstract

The application belongs to the technical field of human brain electrical signal processing and brain regulation, and provides a multi-modal coupling type closed-loop electric stimulation sleep cooperative signal regulation device and system, which acquires multi-modal physiological signals including EEG slow wave power, cerebral blood flow and cerebrospinal fluid flow signals, filters and pre-processes the acquired multi-modal physiological signals to obtain pre-processed multi-modal signals, calculates 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, introduces the 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, and combines the signal stimulation intensity calculation formula and a constructed double-layer optimization architecture to optimize the signal stimulation intensity to obtain optimized signal stimulation intensity. A precise sleep regulation 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 coupled closed-loop electrical stimulation sleep cooperative signal regulation device and system. BACKGROUND

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

[0003] Sleep disorder patients show insomnia, circadian rhythm disorders, night restlessness or daytime excessive sleepiness, etc., which is closely related to the pathological changes of the disease itself (such as brain beta-amyloid deposition, tau protein tangle, neurotransmitter disorder) and age-related physiological degradation, and early intervention may delay sleep deterioration associated with cognitive decline;

[0004] 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, but the single target stimulation cannot effectively coordinate the multiple rhythms of nerves, blood vessels and cerebrospinal fluid, resulting in poor regulation effect;

[0005] 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 signal confusion or unintended neural regulation, leading to the problem of inaccurate generated regulation signals. SUMMARY

[0006] In order to solve at least one technical problem in the background art, the present application provides a multi-modal coupled closed-loop electrical stimulation sleep cooperative signal regulation device and system, which constructs a multi-scale cooperative regulation mechanism and generates a precise sleep regulation signal.

[0007] In order to achieve the above purpose, the present application adopts the following technical solutions:

[0008] The first aspect of the present application provides a multi-modal coupled closed-loop electrical stimulation sleep cooperative signal regulation device, comprising:

[0009] A signal acquisition module for acquiring multi-modal physiological signals, including EEG slow wave power, cerebral blood flow and cerebrospinal fluid flow signals;

[0010] A preprocessing module for filtering and preprocessing the acquired multi-modal physiological signals to obtain preprocessed multi-modal signals;

[0011] a correlation calculation module for calculating 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;

[0012] 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 the cerebral blood flow and cerebrospinal fluid flow signals, to obtain a signal stimulation intensity calculation formula; and combining the signal stimulation intensity calculation formula and the constructed double-layer optimization framework to optimize the signal stimulation intensity to obtain an optimized signal stimulation intensity.

[0013] Further, in the preprocessing module, the acquired multi-modal physiological signals are screened and preprocessed to obtain preprocessed multi-modal signals, which include:

[0014] The process of preprocessing the acquired EEG slow wave power includes: performing adaptive filtering on the EEG slow wave power, and performing phase synchronization enhancement on the adaptive filtered EEG slow wave power.

[0015] The process of preprocessing the acquired cerebral blood flow includes: using a blood vessel pulsation model to eliminate respiratory low-frequency interference.

[0016] The process of preprocessing the acquired cerebrospinal fluid flow signal includes: gradient field correction, pulsation cycle locking, and temperature drift compensation.

[0017] Further, in the correlation calculation module, the calculation formula of the time-domain correlation coefficient representing the time-domain correlation of the EEG slow wave power and the cerebral blood flow is:

[0018] ,

[0019] 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.

[0020] Further, in the correlation calculation module, the calculation formula of the frequency-domain coherence coefficient representing the frequency-domain coherence of the cerebral blood flow and the cerebrospinal fluid flow signal is:

[0021] ,

[0022] wherein, The threshold value represents the coherence coefficient. When the coherence coefficient exceeds the threshold, it indicates that there is significant phase synchronization between cerebral blood flow (CBF) and cerebral blood flow (CSF) pulsation in the 0.1 Hz frequency band, reflecting the vascular-neural coupling state.

[0023] Furthermore, in the signal stimulus intensity adjustment module, the formula for calculating the signal stimulus intensity is:

[0024] ,

[0025] in, This is the adjustment factor for the EEG-CBF correlation coefficient. This is a moderating factor for the CBF-CSF correlation coefficient. Adjust the weights independently for CSF; , , These are the weighting coefficients for each signal; For EEG Normalized power of the frequency band , The time-domain correlation coefficient is used to express the time-domain correlation of EEG-CBF. , The frequency domain coherence coefficients are used to express the frequency domain coherence of CBF-CSF; Dynamic adjustment using a PID controller: ,in, This is the proportionality coefficient. The integral coefficient is... is the differential coefficient.

[0026] Furthermore, in the signal stimulation intensity adjustment module, the constructed dual-layer optimization architecture includes an outer loop and an inner loop;

[0027] In the outer loop, the eigenvalues ​​λ of the joint covariance matrix of the three modes of EEG slow wave power, cerebral blood flow (CBF), and CSF flow signals are calculated respectively. i Then, based on the set period, the joint eigenvalue λ i Update the basic weights α, β, and γ;

[0028] In the inner loop, the time-domain cross-correlation matrix of EEG-CBF and the frequency-domain coherence coefficient matrix of CBF-CSF are calculated in real time using the sliding window method. The optimal correlation coefficient modulator is then predicted using a vascular response model. , and .

[0029] A second aspect of the present invention provides a multimodal coupled closed-loop electrical stimulation sleep coordination modulation system, including the multimodal coupled closed-loop electrical stimulation sleep coordination signal modulation device as described in Embodiment 1.

[0030] The third aspect of the present application provides a computer readable storage medium.

[0031] A computer readable storage medium, having stored thereon a computer program, which, when executed by a processor, implements the following steps:

[0032] Obtaining multi-modal physiological signals, including EEG slow wave power, cerebral blood flow and cerebrospinal fluid flow signals;

[0033] Screening and preprocessing the obtained multi-modal physiological signals to obtain preprocessed multi-modal signals;

[0034] 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;

[0035] 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;

[0036] 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.

[0037] The fourth aspect of the present application provides a computer device.

[0038] A computer device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0039] Obtaining multi-modal physiological signals, including EEG slow wave power, cerebral blood flow and cerebrospinal fluid flow signals;

[0040] Screening and preprocessing the obtained multi-modal physiological signals to obtain preprocessed multi-modal signals;

[0041] 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;

[0042] 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;

[0043] 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.

[0044] The fifth aspect of the present application provides a program product.

[0045] A program product, which is a computer program product, comprises a computer program which, when executed by a processor, implements the following steps:

[0046] Obtaining a multi-modal physiological signal, including EEG slow wave power, cerebral blood flow and cerebrospinal fluid flow signals;

[0047] Screening and preprocessing the obtained multi-modal physiological signal to obtain a preprocessed multi-modal signal;

[0048] 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;

[0049] 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;

[0050] 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.

[0051] Compared with the prior art, the beneficial effects of the present application are:

[0052] The present application calculates 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 based on the obtained multi-modal physiological signal, introduces 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, and optimizes the signal stimulation intensity by combining the constructed double-layer optimization architecture to obtain an optimized signal stimulation intensity, which can generate a more accurate regulation signal.

[0053] The advantages of the additional aspects of the present application will be partially given in the following description, partially will become apparent from the following description, or will be understood by the practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0054] The drawings accompanying the specification of the present application form a part thereof, serve to provide further understanding of the present application, and together with the description of the exemplary embodiments of the present application and their description serve to explain the present application, and do not constitute an improper limitation of the present application.

[0055] Figure 1 is a multi-modal coupling type closed-loop electric stimulation sleep cooperative signal regulation device block diagram provided by the embodiment of the present application;

[0056] Figure 2 is a multi-modal coupling type closed-loop electric stimulation sleep cooperative signal regulation method flow chart provided by the embodiment of the present application. DETAILED DESCRIPTION

[0057] The application will be further described below with reference to the drawings and embodiments.

[0058] It should be noted that the following detailed description is illustrative only and is intended to provide further description of the application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0059] It should be noted that the terms used herein are only intended to describe specific embodiments and are not intended to limit exemplary embodiments according to the present application. As used herein, the singular form is intended to include the plural form unless the context clearly indicates otherwise, and it should also be understood that when the terms "comprise" and / or "include" are used in the specification, they refer to the presence of a feature, step, operation, device, component, and / or combination thereof.

[0060] Term explanation

[0061] Electroencephalogram (EEG) is a method of recording brain activity using electrophysiological indicators. When the brain is active, a large number of neurons synchronously generate postsynaptic potentials, which form after summation. 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.

[0062] Cerebral Blood Flow (CBF) refers to the amount of blood flowing through brain tissue per unit time, usually expressed 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, and is closely related to brain oxygen supply, energy metabolism and neural activity.

[0063] CSF flow signal refers to the specific image performance generated by cerebrospinal fluid (CSF) during flow, which is usually detected by magnetic resonance imaging (MRI) technology. The flow of CSF is different from that of cerebral blood flow (CBF). CSF 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.

[0064] Example 1

[0065] As shown in Figure 1 and Figure 2 The present embodiment provides a multi-modal coupled closed-loop electrical stimulation sleep cooperative signal modulation device, which comprises:

[0066] The signal acquisition module 101 is configured to acquire multi-modal physiological signals.

[0067] In this embodiment, the acquired multi-modal physiological signals in the signal acquisition module include EEG slow wave power, cerebral blood flow CBF and CSF flow signal;

[0068] The EEG slow wave power is collected by a set high-density microelectrode array, and specifically includes 62-channel EEG original signals.

[0069] The cerebral blood flow CBF is collected by an optical coherence tomography (OCT) array or an ultrasonic Doppler probe group;

[0070] The CSF flow is acquired by a phase contrast MRI sequence array or an implanted piezoelectric sensor matrix.

[0071] The pre-processing module 102 is used for screening and pre-processing the acquired multi-modal physiological signals to obtain pre-processed multi-modal signals.

[0072] The pre-processing module includes the following processes for pre-processing the acquired EEG slow wave power:

[0073] The acquired 62-channel EEG original signals are subjected to adaptive filtering processing, and specifically, the EEG original signals are decomposed into independent components by using independent component analysis, and the time-domain waveform, spectral features (such as Electro-oculogram (EOG) low-frequency high-amplitude and electromyogram (EMG) high-frequency burst) and spatial topology map of each independent component are calculated; the Pearson correlation coefficient of the EOG and EMG channels of each independent component is calculated, and the components with an absolute value of the correlation coefficient greater than 0.7 are removed.

[0074] The EEG slow wave power after adaptive filtering is subjected to phase synchronization enhancement, and specifically, the Common Spatial Pattern (CSP) method is used to calculate the signal covariance matrix and the corresponding eigenvalue in a specific frequency band such as the δ frequency band (0.5-4Hz), and the principal component signal with an eigenvalue greater than 0.8 is retained.

[0075] The acquired cerebral blood flow CBF is pre-processed by using existing blood vessel pulsation models such as the elastic cavity model and the transmission line model, using 1Hz high-pass filtering to separate the arterial pulsation component and eliminate the respiratory low-frequency (0.1-0.3Hz) interference.

[0076] The laser speckle image is reconstructed by using the first five principal components by using the PCA processing method to reduce the influence of the reflectivity difference on the cortical surface.

[0077] 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.

[0078] The process of preprocessing the acquired CSF flow signal includes:

[0079] The first three principal components of the MRI scanning gradient field are extracted by real-time PCA technology for dynamic cancellation, the signal-to-noise ratio is improved by 4.6dB, and the CSF flow signal after gradient field correction is obtained;

[0080] 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 and cross-correlation analysis, the flow pulse with a correlation coefficient greater than 0.6 is retained, and the pulsation cycle is locked;

[0081] Finally, the PID controller is integrated to adjust the flow meter baseline according to the probe temperature change rate (dT / dt>0.1℃ / s). Through the above preprocessing process, the logic progression of basic noise elimination→physiological signal synchronization→environmental factor compensation is ensured to guarantee the data quality of the CSF flow signal.

[0082] 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.

[0083] The correlation calculation module includes a time domain cross-correlation analysis module and a frequency domain coherence analysis module.

[0084] 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 signals.

[0085] In this embodiment, the time domain correlation coefficient of the time domain correlation representing the correlation between the EEG slow wave power and the cerebral blood flow CBF is The calculation formula is:

[0086] ,

[0087] Wherein, EEG slow wave power signal, CSF flow signal, EEG slow wave power signal mean, CSF flow signal mean, EEG slow wave power signal total number, t Time;

[0088] The coupling state of the multi-modal signal is determined based on the time domain cross-correlation analysis result and the preset condition.

[0089] When the time-domain cross-correlation analysis result of the EEG slow wave power and the cerebral blood flow 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 cerebral blood flow CBF is greater than 0.6, it is determined that the strong blood vessel-neural coupling state;

[0090] The frequency domain coherence analysis module is configured to perform frequency domain coherence analysis on the preprocessed cerebral blood flow CBF and CSF flow signals;

[0091] The frequency domain coherence analysis module is configured to perform frequency domain coherence analysis on the preprocessed cerebral blood flow CBF and CSF flow signals;

[0092] ,

[0093] Among them, The coherence coefficient threshold is 0.4 in this embodiment, and when the coherence coefficient exceeds the threshold 0.4, it indicates that there is significant phase synchronization between the cerebral blood flow 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. The criterion is one of the core indicators for quantifying the sleep period multi-modal physiological signal synergy. 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 based on the signal stimulation intensity calculation formula and the constructed double-layer optimization architecture to obtain the optimized signal stimulation intensity.

[0094] The signal stimulation intensity adjustment module 104 includes a signal stimulation intensity calculation module and a signal optimization module;

[0095] 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.

[0096] 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, and the formula is:

[0097] ,

[0098] The embodiment can introduce a correlation coefficient adjustment factor θ, which can dynamically adjust according to the correlation between signals, and the specific stimulation intensity adjustment formula is:

[0099] ,

[0100] in, The adjustment factor for the EEG-CBF correlation coefficient. The adjustment factor for the CBF-CSF correlation coefficient. Adjust the weights independently for CSF; , , These are the weighting coefficients for each signal; For EEG Normalized power of the frequency band , To express the time-domain correlation coefficient of EEG-CBF, , The frequency domain coherence coefficients are used to express the frequency domain coherence of CBF-CSF; Dynamic adjustment using a PID controller: ,in, This is the proportionality coefficient. The integral coefficient is... These are the differential coefficients;

[0101] In this embodiment, , , ;

[0102] The signal optimization module is configured to optimize the signal stimulus intensity by combining the signal stimulus intensity calculation formula and the constructed two-layer optimization architecture to obtain the optimized signal stimulus intensity.

[0103] In this embodiment, the two-layer optimized architecture includes an inner loop and an outer loop;

[0104] In the outer loop, the eigenvalues ​​λ of the joint covariance matrix of the three modes of EEG slow wave power, cerebral blood flow (CBF), and CSF flow signals are first calculated. i Then, based on the set period, the joint eigenvalue λ i Update the basic weights α, β, and γ, for example, update the basic weights α, β, and γ every 24 hours;

[0105] In this embodiment, the eigenvalue λ of the joint covariance matrix of the three modes of EEG slow wave power, cerebral blood flow (CBF), and CSF flow signals is used. i The calculation method is as follows: the preprocessed EEG slow wave power, cerebral blood flow (CBF) and CSF flow signals are time-aligned to obtain multidimensional time series features. The joint covariance is calculated by combining the multidimensional time series features with the covariance matrix formula. Then, the joint covariance is decomposed to obtain the eigenvalues ​​λ1, λ2, λ3 of each modal signal in the joint covariance matrix.

[0106] The specific update formula is as follows:

[0107] ;

[0108] 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 a vascular response model 、 and ;

[0109] Specifically, the method comprises the following steps:

[0110] 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;

[0111] 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;

[0112] 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;

[0113] 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;

[0114] In this embodiment, the vascular response model adopts a two-chamber windkessel model;

[0115] 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:

[0116] The arterial chamber equation is:

[0117] ,

[0118] wherein, is the arterial compliance, is the arterial pressure, is the input blood flow, which is modulated by the CBF pulsatile amplitude, is the arterial resistance, which is negatively correlated with the qEC reference value;

[0119] The venous chamber equation is:

[0120] ,

[0121] where, is venous compliance, is venous pressure, is venous resistance, modulated by CSF pulse frequency, is cerebrospinal fluid pressure, related to CSF pulse frequency;

[0122] The specific prediction process includes:

[0123] The qEC reference value, qCC factor, CBF pulsatility amplitude, and CSF pulse frequency are input as input parameters into the two-compartment windkessel model, the input CBF pulsatility 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;

[0124] where, , , ;

[0125] The objective function and constraint conditions that maximize the dynamic coordination of the neurovascular-cerebrospinal fluid system are constructed;

[0126] ,

[0127] where, and are ideal correlation coefficients;

[0128] The constraint conditions are:

[0129] physiological range constraints, , and values can be set according to actual needs;

[0130] dynamic stability conditions, does not exceed the upper limit of intracranial pressure. At the same time, the optimization also includes: when > 2.5 or > 2.0, trigger the gradient descent algorithm to limit the weight increase ≤ 10% / min, and if the CSF pulse frequency > 0.3 Hz, immediately reduce the γ coefficient by 50% to prevent overstimulation;

[0131] If the three-modal correlation coefficient is less than 0.2 for 5 minutes, switch to the backup single-modal mode, fix = 1.0, = 0.5;

[0132] In addition, when the EEG-CSF phase difference is > π / 2, an artificial phase offset Δφ = π / 4 is inserted to optimize the synchrony.

[0133] Embodiment Two

[0134] 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.

[0135] It should be noted that the specific implementation mode of the multi-modal coupled closed-loop electrical stimulation sleep cooperative signal regulation system in the embodiment of the present application is similar to that of the multi-modal coupled closed-loop electrical stimulation sleep cooperative signal regulation device in 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.

[0136] Embodiment Three

[0137] The embodiment provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the following steps:

[0138] Obtaining multi-modal physiological signals, including EEG slow wave power, cerebral blood flow and cerebrospinal fluid flow signals;

[0139] Screening and preprocessing the obtained multi-modal physiological signals to obtain preprocessed multi-modal signals;

[0140] 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;

[0141] 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;

[0142] 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.

[0143] Embodiment Four

[0144] The embodiment provides a computer device, which 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 computer program:

[0145] Obtaining multi-modal physiological signals, including EEG slow wave power, cerebral blood flow and cerebrospinal fluid flow signals;

[0146] Screening and preprocessing the acquired multi-modal physiological signals to obtain preprocessed multi-modal signals;

[0147] Calculating 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;

[0148] 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;

[0149] 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.

[0150] Embodiment five

[0151] 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 implement the following steps:

[0152] Acquiring multi-modal physiological signals, including EEG slow wave power, cerebral blood flow and cerebrospinal fluid flow signals;

[0153] Screening and preprocessing the acquired multi-modal physiological signals to obtain preprocessed multi-modal signals;

[0154] Calculating 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;

[0155] 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;

[0156] 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.

[0157] Those skilled in the art will understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a hardware embodiment, a software embodiment, or an embodiment 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 magnetic disk storage and optical storage, etc.) containing computer-usable program code.

[0158] The embodiments of methods, apparatuses (systems) and computer program products according to the present application can be described in the general context of method steps and processes, which can be implemented in one embodiment by a program of instructions on a computer-readable storage medium executed by a computer or other programmable apparatus. The apparatuses can be specially constructed for executing the embodiments of methods, apparatuses (systems) and computer program products according to the present application or can include a computer or other programmable apparatus. Figure 1 one or more functions specified in the flow or flows and / or blocks. Figure 1 one or more functions specified in the flow or flows and / or blocks.

[0159] These computer program instructions can also be stored in a computer-readable storage medium that can direct a computer or other programmable apparatus to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instructions which implement the flow Figure 1 one or more functions specified in the flow or flows and / or blocks. Figure 1 one or more functions specified in the flow or flows and / or blocks.

[0160] These computer program instructions can also be loaded onto a computer or other programmable apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flow Figure 1 one or more functions specified in the flow or flows and / or blocks. Figure 1 one or more functions specified in the flow or flows and / or blocks.

[0161] Those skilled in the art can understand that all or part of the flow of the above-mentioned embodiment method can be completed by computer program instructions instructing related hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the flow of the above-mentioned embodiment of each method. The storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM) or a random access memory (RAM) and the like.

[0162] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. For those skilled in the art, the present application can have various changes and modifications. 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; combining the signal stimulation intensity calculation formula and the constructed double-layer optimization architecture to optimize the signal stimulation intensity to obtain an optimized signal stimulation intensity; wherein, in the signal stimulation intensity adjustment module, the signal stimulation intensity calculation formula is: ， wherein, is an EEG-CBF correlation coefficient adjustment factor, is a CBF-CSF correlation coefficient adjustment factor, is a CSF independent adjustment weight; , , are weight coefficients corresponding to each signal respectively; is a normalized power of EEG frequency band, , is a time domain correlation coefficient expressing time domain correlation of EEG-CBF, , is a frequency domain coherence coefficient expressing frequency domain coherence of CBF-CSF; using a PID controller to dynamically adjust: wherein, is a proportional coefficient, is an integral coefficient, is a differential coefficient, , In the signal stimulation intensity adjustment module, the constructed double-layer optimization architecture includes an outer loop and an inner loop; in the outer loop, the eigenvalues λ of the three-modal joint covariance matrix of the EEG slow wave power, the cerebral blood flow CBF and the CSF flow signal are respectively calculated i Then, according to the set period, the eigenvalues λ are combined i The base weights α, β and γ are updated; specifically, the preprocessed EEG slow wave power, the cerebral blood flow CBF and the CSF flow signal are time-aligned to obtain multi-dimensional time sequence features, the multi-dimensional time sequence features are combined, the covariance matrix formula is used to calculate the joint covariance, and then the joint covariance is subjected to characteristic decomposition to obtain the eigenvalues λ1, λ2 and λ3 of the joint covariance matrix of each modal signal; and the specific update formula is as follows: ​ 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 ; the specific prediction process includes: based on the sliding window method, the EEG-CBF time domain cross-correlation coefficient matrix and the CBF-CSF frequency domain coherence coefficient matrix are calculated in real time to determine the qEC reference value and the qCC factor; the maximum absolute value of the EEG-CBF time domain cross-correlation coefficient is taken as the qEC reference value; the 0.1Hz feature value is extracted to obtain the qCC factor through the Sigmoid function mapping; The qEC reference value, the qCC factor, the CBF pulsatile amplitude and the CSF pulsatile frequency are inputted as input parameters into the two-compartment windkessel model, the inputted CBF pulsatile amplitude modulates the inputted blood flow , the inputted qCC factor adjusts the venous resistance , the differential equations are solved to obtain the arterial pressure and the venous pressure , the adjustment factors are finally calculated by the dynamic coordination objective function and the constraint conditions , and , , , , is the cerebrospinal fluid pressure; , , are the weight coefficients of the corresponding adjustment factors respectively; the dynamic coordination objective function and the constraint condition are: ， Physiological range constraints: , and The values are set according to the actual requirements; wherein and is the ideal correlation coefficient.

2. The multi-modal coupled closed loop electrical stimulation sleep co-regulatory signaling device of claim 1, wherein, 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 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.

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 coefficient representing 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 coefficient representing 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. A multimodal coupled closed-loop electrical stimulation sleep co-regulatory signal modulation system, characterized in that, The computer program is executed by the processor to realize the following steps of the multi-modal coupled closed-loop electrical stimulation sleep cooperative signal regulation device according to any one of claims 1-4:

6. 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; combining the signal stimulation intensity calculation formula and the constructed double-layer optimization architecture to optimize the signal stimulation intensity to obtain an optimized signal stimulation intensity. ​ 7. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the following steps of the multi-modal coupled closed-loop electrical stimulation sleep synergistic signal regulation device according to any one of claims 1-4: 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 the 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 the optimized signal stimulation intensity.

8. A program product, the program product being a computer program product comprising a computer program, characterized in that The processor executes the computer program to realize the following steps of the multi-modal coupled closed-loop electrical stimulation sleep synergistic signal regulation device according to any one of claims 1-4: 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 the 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 the optimized signal stimulation intensity.

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