Brain-heart linkage transcranial strong alternating current stimulation feedback control method and system

Abstract

The application belongs to the technical field of biomedical engineering and neural regulation technology. A brain-heart linkage transcranial strong alternating current stimulation feedback control method and system are provided. Heart rate variability characteristics are obtained according to electroencephalogram signals, electroencephalogram signal characteristics are obtained according to the electroencephalogram signals, and a dynamic coupling index is determined according to the electroencephalogram signals and electrocardiogram signals. When the heart rate variability characteristics are greater than or equal to a corresponding heart rate characteristic baseline threshold, the electroencephalogram signal characteristics are greater than or equal to a corresponding electroencephalogram characteristic baseline threshold, and the dynamic coupling index is greater than or equal to a set threshold, it is determined that emotional disorders are improved, and the intensity of the stimulation current is unchanged. Otherwise, the intensity of the stimulation current is increased by a set threshold until the emotional disorders are improved. Through joint calculation of the electroencephalogram signals and the electrocardiogram signals, dynamic monitoring and accurate evaluation of the process of treating emotional disorders by using transcranial strong alternating current stimulation are realized, and the accuracy of the electric stimulation is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of biomedical engineering and neural regulation technology, and particularly relates to a brain-heart linkage transcranial alternating current stimulation feedback control method and system. BACKGROUND

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

[0003] Currently, the association between visceral signals and emotions has become a research focus, and more and more studies have confirmed that visceral factors play a key role in social interaction, emotional regulation and decision-making process. With the increasing attention to the physiological interaction between central nervous system and peripheral organs, brain-heart axis research has gradually become a popular research direction. The two-way pathway of brain-heart axis has been identified: the pathway from brain to heart originates from the medial prefrontal cortex and insular cortex, and finally acts on myocardial cells and coronary artery epithelial cells; the pathway from heart to brain starts from the peripheral circulatory system chemoreceptors and baroreceptors, and is transmitted to the insular cortex through the thalamus. At the same time, it is found that emotional disorders are closely related to heart health, such as causing the functional connection between the rostral anterior cingulate cortex and the autonomic brainstem nucleus to be disconnected, and the amygdala also plays an important role in the influence of negative emotions on the heart (especially the heart risk caused by post-traumatic stress disorder). Therefore, the quantitative evaluation of the interaction between the brain and the heart system can supplement the key information for the research of a single physiological system, and how to realize such quantitative measurement across the fields of neuroscience, biomedicine, computer science and other fields has become the core direction to promote the brain-heart interaction research.

[0004] In the technical application layer, the autonomic nervous system related technology has provided support for brain-heart interaction research. The central nervous system of the autonomic nervous system is distributed at various levels from the spinal cord to the cerebral cortex, and can regulate sympathetic and parasympathetic nerve activity. The biofeedback technology based on heart rate variability can evaluate brain function and emotional state, and transcranial magnetic stimulation technology can improve peripheral organ autonomic nervous function by acting on the brain. Rat experiments have also found a "top-down" regulation of heart rate neural pathway. In addition, bioelectric signals have become a new choice for research due to the advantages of easy recording, high time resolution and multi-channel electroencephalogram (EEG) signals that can locate neurons. However, the existing technology has obvious limitations: most systems rely on a single physiological signal (such as only using EEG or ECG) or a single evaluation index (such as only analyzing certain frequency characteristics of brain waves), and cannot construct a fusion evaluation system using the complementarity of EEG and electrocardiogram (ECG) signals, which cannot fully reflect the dynamic coupling relationship between the brain and heart system, not only leading to one-sided cognition of the pathogenesis of mental illness, but also difficult to evaluate the synergistic abnormal state of brain and autonomic nervous function as a whole. SUMMARY

[0005] In order to solve the problems in the prior art, the application provides a brain-heart interaction transcranial strong alternating current stimulation feedback control method and system.

[0006] In order to achieve the above-mentioned purpose, the application adopts the following technical scheme:

[0007] In a first aspect, the application provides a brain-heart interaction transcranial strong alternating current stimulation feedback control method.

[0008] A brain-heart interaction transcranial strong alternating current stimulation feedback control method comprises the following steps:

[0009] Obtaining electroencephalogram signals and electrocardiogram signals after transcranial strong alternating current stimulation;

[0010] Obtaining heart rate variability features from the electroencephalogram signals, obtaining electroencephalogram signal features from the electroencephalogram signals, and determining a dynamic coupling index according to the electroencephalogram signals and the electrocardiogram signals;

[0011] When the heart rate variability features are greater than or equal to corresponding heart rate feature baseline thresholds, the electroencephalogram signal features are greater than or equal to corresponding electroencephalogram feature baseline thresholds, and the dynamic coupling index is greater than or equal to a set threshold, it is determined that the emotional disorder is improved, and the intensity of the stimulation current is unchanged; otherwise, the intensity of the stimulation current is increased by the set threshold until the emotional disorder is improved.

[0012] In an implementation form of the first aspect of the application, the heart rate variability features comprise: a standard deviation of all R-R intervals of the electrocardiogram signals, a mean square of adjacent R-R interval differences of the electrocardiogram signals, and a ratio of a low-frequency component to a high-frequency component of the R-R intervals of the electrocardiogram signals.

[0013] In an implementation form of the first aspect of the application, the electroencephalogram signal features comprise: wave power and wave power.

[0014] In an implementation form of the first aspect of the application, the dynamic coupling index is determined according to the electroencephalogram signals and the electrocardiogram signals, comprising: calculating a first time-varying mutual information between a wave component of the electroencephalogram signals and a low-frequency component of the R-R intervals of the electrocardiogram signals, and a second time-varying mutual information between the wave component of the electroencephalogram signals and a high-frequency component of the R-R intervals of the electrocardiogram signals, and obtaining the dynamic coupling index according to the first time-varying mutual information and the second time-varying mutual information.

[0015] As a further limitation of the first aspect of the application, the first time-varying mutual information and the second time-varying mutual information obtaining a dynamic coupling index comprising: wherein, representing a brain electrical signal wave component, representing a low-frequency component of a heart signal R-R interval, representing a high-frequency component of a heart signal R-R interval, representing a brain electrical signal wave component, representing a mean of a brain electrical signal wave component, representing a standard deviation of a brain electrical signal wave component, representing a mean of a brain electrical signal wave component, representing a standard deviation of a brain electrical signal wave component, representing a time-varying mutual information of a delta wave component and a beta wave component, representing a time-varying mutual information of a delta wave component and a beta wave component.

[0016] In a second aspect, the present application provides a brain-heart interaction transcranial alternating current stimulation feedback control system.

[0017] A brain-heart interaction transcranial alternating current stimulation feedback control system, comprising:

[0018] a brain-heart data acquisition unit configured to acquire a brain electrical signal and a heart electrical signal after transcranial alternating current stimulation;

[0019] a feature extraction unit configured to obtain a heart rate variability feature according to the brain electrical signal, obtain a brain electrical signal feature according to the brain electrical signal, and determine a dynamic coupling index according to the brain electrical signal and the heart electrical signal;

[0020] an improved tracking control unit configured to determine that an emotional disorder is improved when the heart rate variability feature is greater than or equal to a corresponding heart rate feature baseline threshold, the brain electrical signal feature is greater than or equal to a corresponding brain electrical feature baseline threshold, and the dynamic coupling index is greater than or equal to a set threshold, and then keep the intensity of the stimulation current unchanged; otherwise, increase the intensity of the stimulation current by a set threshold until the emotional disorder is improved.

[0021] In a third aspect, the present application provides a computer device, comprising: a processor and a computer readable storage medium;

[0022] the processor is adapted to execute a computer program;

[0023] The computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the brain-heart linkage transcranial strong alternating current stimulation feedback control method of the first aspect of the application.

[0024] In a fourth aspect, the application provides a computer readable storage medium storing a computer program, and the computer program is adapted to be loaded by the processor and execute the brain-heart linkage transcranial strong alternating current stimulation feedback control method of the first aspect of the application.

[0025] In a fifth aspect, the application provides a computer program product comprising a computer program, and the computer program is executed by the processor to realize the brain-heart linkage transcranial strong alternating current stimulation feedback control method of the first aspect of the application.

[0026] Compared with the prior art, the application has the following beneficial effects:

[0027] The application innovatively provides a brain-heart linkage transcranial strong alternating current stimulation feedback control method, introduces an objective neurological marker for the evaluation of the treatment effect of emotional disorders by using the change of the electroencephalogram-electrocardiogram synchronization index, overcomes the deficiency of the traditional simple dependence on a scale, and reflects the improvement degree of the brain function state before and after treatment by the electroencephalogram-electrocardiogram synchronization index, thereby supporting the efficacy evaluation from the physiological level and ensuring the precision of the electrical stimulation.

[0028] The application can quantitatively determine the neurological effect produced by the treatment by calculating the power of the electroencephalogram at a specific frequency and the heart rate variability component index of the electrocardiogram. For example, the decrease of the power of the electroencephalogram at a specific frequency reflects the abnormality of the cognitive function of the patient, and the decrease of the heart rate variability component of the electrocardiogram reflects the abnormality of the sympathetic nerve of the patient.

[0029] The application synchronously records the electroencephalogram and the electrocardiogram, constructs a joint evaluation index system, forms a dynamic decision feedback mechanism, realizes the individualized tracking of each patient, and if the electroencephalogram-electrocardiogram synchronization index is obviously improved in the early stage of treatment, the intervention can be terminated in advance; if the index is not improved, treatment suggestions can be given in time to avoid delay, and such a feedback mechanism improves the efficiency and safety of the treatment.

[0030] The application is not only suitable for clinical monitoring, but also can be used for studying the response mechanism of emotional disorders to brain stimulation, and the quantitative change data of the electroencephalogram-electrocardiogram are helpful to reveal the essence of the brain-heart function abnormality in emotional disorders and provide a scientific basis for the neural regulation therapy.

[0031] Advantages of the additional aspects of the application will be partially apparent in the following description, partially will become apparent from the following description, or will be learned by practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0032] The accompanying drawings, which form a part of this specification, are included to provide a further understanding of the application, and are incorporated in and constitute a part of this specification. The embodiments illustrated in the drawings are intended to explain the aspects of the present application and are not limiting of the present application in any way.

[0033] Figure 1 A flow chart of a brain-heart interactive transcranial alternating current stimulation feedback control method is provided for an exemplary embodiment of the present application;

[0034] Figure 2 A principle diagram of a brain-heart interactive transcranial alternating current stimulation feedback control system is provided for an exemplary embodiment of the present application;

[0035] Figure 3 A schematic diagram of a computer device is provided for an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0036] The application will be further described with reference to the drawings and examples.

[0037] It should be noted that the following detailed description is exemplary in nature 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.

[0038] Existing research has found that the body's visceral signals are often linked to emotions, and more and more research believes that visceral factors play a key role in social, emotional and decision-making processes. In recent years, the physiological interaction between the central nervous system and peripheral organs has attracted people's attention, and research around the brain-heart axis has gradually become a hot topic in current research. The brain-heart axis pathway from brain to heart starts from the medial prefrontal cortex and insular cortex, and finally reaches the myocardial cells and coronary artery epithelial cells. The pathway from heart to brain starts from the peripheral circulatory system chemoreceptors and baroreceptors, is transmitted through the thalamus, and finally conducts to the insular cortex. Existing research has found that emotional disorders are related to cardiomyopathy, and the state of emotional disorders is related to the disconnection of functional connections between the rostral anterior cingulate cortex and the autonomic brainstem nuclei. In addition, the amygdala is under the inhibitory regulation of the prefrontal cortex and is connected with the hypothalamus and brainstem nuclei, and is involved in the influence of negative emotions on the heart, especially the heart risk caused by post-traumatic stress disorder. Therefore, the quantitative evaluation of the interaction between the brain and the heart physiological system can provide supplementary information for the research based on a single physiological system. How to combine neuroscience, biomedical science and computer science to realize the quantitative measurement of brain-heart interaction is a key problem in the understanding of brain-heart interaction.

[0039] The autonomic nervous system (ANS) has central regulation and control of autonomic function at all levels from the spinal cord to the cerebral cortex. They can all affect sympathetic and parasympathetic nerve activity, so the autonomic nervous system is not completely autonomous. Some biofeedback techniques, such as heart rate variability-based autonomic nervous system biofeedback regulation technology, provide new ideas for assessing brain function and overall mood, stress state. Indicators of autonomic nervous system dysfunction, such as decreased heart rate variability, are also considered to be closely related to the prognosis of patients with stroke and brain trauma. Transcranial magnetic stimulation and transcutaneous electrical stimulation can improve the function of the peripheral organ autonomic nervous system by directly acting on the brain. These results establish the link between the brain and the autonomic nervous system and confirm the interaction between the brain and peripheral organs, especially the interaction between the brain and the heart. Some researchers have established a rat heart rate feedback experiment model and found that rats can regulate their heart rate through biofeedback reinforcement learning. Studies have found that the anterior cingulate cortex (ACC) to the ventromedial thalamic nucleus (VMT) oscillation is involved in the regulation of the slow heart rate and verifies the multisynaptic neural pathway from the ACC to the heart: ACC→VMT→DMH (dorsomedial hypothalamic nucleus)→Amb (ambiguity nucleus), which is considered to be an important mechanism for 'top-down' regulation of heart rate. From the perspective of clinical transformation, anxiety and emotional disorders can change heart rate, and increased heart rate can also exacerbate anxiety or fear responses, so controlling heart rate and reducing it can help improve anxiety-related behaviors. This study opens up new avenues for brain-heart axis research, and in the future, non-invasive means such as transcranial magnetic stimulation can be used to precisely regulate brain regions and improve the symptoms of patients with emotional disorders by controlling heart rate.

[0040] ​Compared with neuroimaging, bioelectric signals are easy to record and have high temporal resolution, and multi-channel EEG signals can also achieve spatial positioning of active neurons. Analyzing the linear / nonlinear, directional / non-directional relationship between complex physiological systems based on physiological electrical signals (time series) is of great significance to the mechanism research of mental illness, can realize the detection and quantification of the direct interaction between the brain and the peripheral system, and is the latest interest field of neuroscience research. However, the existing system is limited to a single physiological signal (such as relying only on EEG or only focusing on ECG) or a single evaluation index (such as only analyzing a certain frequency feature of the electroencephalogram or only monitoring the heart rate change), and fails to fully utilize the complementarity of EEG and ECG to construct a brain-heart fusion evaluation system. This single-dimensional analysis method cannot fully reflect the dynamic coupling relationship between the brain-heart system, leading to one-sidedness in the understanding of the pathological mechanism of mental illness, and it is also difficult to evaluate the synergistic abnormal state of brain function and autonomic nervous function from the overall level.

[0041] In view of the problems existing in the prior art, the present embodiment proposes a brain-heart interaction transcranial alternating current stimulation feedback control method, which integrates key indicators of electroencephalogram and electrocardiogram signals, realizes quantitative and objective evaluation of the therapeutic effect of emotion disorder patients, and helps to improve the accuracy and efficiency of treatment monitoring, which has significant social and clinical value. As shown in Figure 1 As shown in the figure, one minute of data is collected, then stimulated for five minutes, and then one minute of data is collected, and the cycle is repeated multiple times until the collection is completed. Each time the data is collected, the indicators are adaptively calculated and compared with the previous time to adaptively judge the next round of stimulation. Subsequently, the data processed each time is calculated in parallel for two indicators: one is the heart rate variability based on electrocardiogram (ECG_HRV) indicator (i.e. indicator A); the other is the power spectrum based on electroencephalogram (EEG_PSD) indicator (i.e. indicator B). In order to further fuse and optimize the evaluation indicators, the present application fuses EEG-ECG signals based on a dynamic modeling method to calculate a brain-heart coupling index (BCI) as a new objective biological indicator (i.e. indicator C) for secondary evaluation of the patient. After the calculation of the indicators of the two scans is completed, all the indicators are compared and analyzed. Finally, the results are determined and the treatment suggestions are classified and output.

[0042] The present application is used for evaluating the therapeutic effect of transcranial alternating current stimulation on emotion improvement in a clinical scene. By analyzing the EEG signals and ECG signals at multiple time points before and after treatment, quantitative brain-heart function detection indicators can be provided for doctors to assist in developing personalized treatment plans. The present application can realize a "collection-evaluation-intervention-feedback" full-chain closed-loop feedback mechanism for emotion disorder patients, and EEG signals and ECG signals are collected synchronously. The time stamp alignment technology is used to ensure the synchronization of EEG signal and ECG signal collection, and the hardware trigger error is controlled within ±5ms.

[0043] EEG signal collection needs to be preprocessed, the preprocessing process of EEG signal mainly includes filtering and artifact removal, the selected data is filtered by using a 5th order Butterworth filter, the high-pass cutoff frequency is 0.5Hz, so as to avoid the influence of low-frequency drift; the low-pass cutoff frequency is 35Hz, so as to avoid the influence of 50Hz power frequency noise and high-frequency electromyographic noise, and the main frequency band of EEG signal analysis is retained, and the Nyquist theorem requires that the down-sampling is not lower than the highest remaining frequency.

[0044] ECG signal collection needs to be preprocessed, and a 5th order Butterworth filter is used for band-pass filtering of the ECG signal, the high-pass cutoff frequency is 0.5Hz, the low-pass cutoff frequency is 30Hz, the filtering operation removes low-frequency disturbance and high-frequency environmental noise, and also removes signals with excessive noise caused by loose electrodes, the ECG signal is down-sampled to 1000Hz, and finally the classic Pan-Tompkins algorithm is used to identify the R-wave peak position of the ECG signal.

[0045] According to the preprocessed EEG signal and ECG signal, index calculation is performed, including the following process:

[0046] Step 1: signal processing.

[0047] High-frequency noise is suppressed by anti-aliasing filtering (EEG: 0.5-35Hz; ECG: 0.5-30Hz), and spectrum leakage is avoided during wavelet transform.

[0048] Step 2: EEG signal spectrum calculation based on wavelet transform.

[0049] The complex Morlet wavelet is used, and the calculation formula is as follows:

[0050] (1);

[0051] Where, the bandwidth parameter (i.e. the central angular frequency), is used to balance the time-frequency resolution of EEG; represents a normalized coefficient; represents a complex sine term, which provides an “oscillatory characteristic” and is the source of the wavelet’s central frequency in the frequency domain; represents a Gaussian window function, which is used to control the decay range of the wavelet in the time domain; represents a time variable; represents a wavelet function.

[0052] The scale of the frequency band (0.5-4Hz) is:

[0053] (2);

[0054] wherein, represents the frequency of the frequency band, represents the sampling frequency; represents the wavelet scale corresponding to the frequency band.

[0055] The scale of the frequency band (4-8 Hz) is:

[0056] (3);

[0057] wherein, represents the frequency of the frequency band.

[0058] The power calculation of the frequency band includes:

[0059] (4);

[0060] wherein, is the scale wavelet coefficient; represents the number of signal sampling points in the analysis period; represents the th sampling point; represents the absolute value of the wavelet coefficient of the th sampling point at the scale of the frequency band; represents the power value of the frequency band.

[0061] The power calculation of the frequency band includes:

[0062] (5);

[0063] wherein, is the scale wavelet coefficient; represents the absolute value of the wavelet coefficient of the th sampling point at the scale of the frequency band; represents the power value of the frequency band.

[0064] Step 3: HRV component analysis.

[0065] Pan-Tompkins algorithm is used to locate QRS complex in real time, and R-R interval is interpolated (cubic spline) to 4Hz uniform sampling.

[0066] Normalization processing is performed, specifically including:

[0067] (6);

[0068] (7);

[0069] where TP is the total power, VLF is the very low frequency power, is the normalized low frequency power, is the non-normalized low frequency power, is the normalized high frequency power, is the non-normalized high frequency power.

[0070] SDNN (standard deviation) calculation, including:

[0071] (8);

[0072] where, represents the standard deviation of all R-R intervals, reflecting the overall activity of the autonomic nervous system, and the normal value is usually greater than 50 ms; represents the duration of the R-R interval; represents the arithmetic mean of all R-R interval durations; represents the total number of R-R intervals within the analysis period. RMSSD (mean square of adjacent R-R interval differences) calculation, including:

[0073]

[0074] (9); where,

[0075] reflects the short-term regulation ability of the parasympathetic nervous system (vagus nerve), and is sensitive to fatigue and stress; represents the duration of the R-R interval. In the present application, the frequency domain index decomposes the R-R interval into energy distribution of different frequency bands through Fourier transform (FFT) or autoregressive model (AR). High frequency component (HF): 0.15-0.4 Hz, synchronized with respiratory rhythm, representing parasympathetic nervous activity; Low frequency component (LF): 0.04-0.15 Hz, reflecting the joint action of sympathetic and parasympathetic; LF / HF ratio (here the ratio of normalized LF and HF is used): assesses the sympathetic-vagus balance, and the increase of LF / HF ratio indicates the dominance of sympathetic nerve (such as anxiety state), and the decrease of LF / HF ratio may indicate the hyperactivity of parasympathetic nerve (such as chronic fatigue).

[0076] Step 4: dynamic coupling modeling.

[0077] Step 4: dynamic coupling modeling.

[0078] ​The application proposes a time-varying mutual information (TVMI) calculation method, which calculates in a sliding window (30 seconds, step 5 seconds) Coupling strength with LF and Coupling strength with HF , comprising:

[0079] (10);

[0080] Wherein, X is the power of EEG frequency band, Y is the HRV component, representing the joint probability distribution of X and Y, representing the marginal probability distribution of X, representing the marginal probability distribution of Y, represents the sum of all possible values x of X and all possible values y of Y, represents the logarithmic operation.

[0081] Step 5: Exponential standardization.

[0082] The coupling value is mapped to the interval [0, 1] after standardization, including:

[0083] (11);

[0084] Wherein, representing the delta wave component of the brain electrical signal representing the low-frequency component of the R-R interval of the heart signal, representing the high-frequency component of the R-R interval of the heart signal, representing the delta wave component of the brain electrical signal representing the mean of the delta wave component of the brain electrical signal representing the standard deviation of the delta wave component of the brain electrical signal representing the mean of the delta wave component of the brain electrical signal representing the standard deviation of the delta wave component of the brain electrical signal representing the delta wave component and the time-varying mutual information of the low-frequency component (LF) of HRV, representing the time-varying mutual information of the theta wave component and the high-frequency component (HF) of HRV, and the index interpretability is shown in Table 1. Table 1: Index interpretability results

[0085] Table 1: Index interpretability results

[0086]

[0087] ​​​​​If all of the N indicators of indicators A and indicators B are improved to complete improvement, and indicator C is greater than or equal to 0.6, it is recorded that the emotional disorder is improved, otherwise it is recorded that the emotional disorder is not improved.

[0088] More specifically, first, EEG-ECG signal (i.e., electroencephalogram-electrocardiogram) synchronous acquisition is performed, then EEG and ECG signals are intercepted, and then filtering and denoising processing is performed; thereafter, three parallel steps are divided: calculating EEG spectral power, extracting ECG signal HRV components, and performing coupling calculation and obtaining BCI indicators; subsequently, it is judged whether EEG_PSD and ECG_HRV are greater than or equal to the baseline threshold; finally, if all of the sub-indicators of indicators A and indicators B are improved, and indicator C is greater than or equal to 0.6, it is recorded that the emotional disorder is improved, at this time the intensity of the stimulation current is unchanged (i.e., the stimulation current amplitude is unchanged); otherwise, it is recorded that the emotional disorder is not improved, at this time the intensity of the stimulation current is increased by a set threshold (i.e., the current amplitude is increased by 0.5 mA), until the emotional disorder is improved, or the highest stimulation current threshold is reached.

[0089] The present application provides a specific example, assuming that an emotional disorder patient (for example, a patient with mood disorder) receives Hi-tACS treatment, every cycle is a continuous 2-hour stimulation intervention (example parameters: 15 mA current, 5 minutes / time), before the treatment starts, first, EEG-ECG synchronous recording (1 min) is performed once, to obtain baseline data; then, one minute of data is collected every five minutes of stimulation, and the next round of stimulation is changed in real time.

[0090] All collected EEG-ECG synchronous data is processed according to the above method process, first, the middle 1 min of signal data is intercepted for analysis, and EEG and ECG are filtered respectively to remove noise interference, so as to obtain clean signals; secondly, the signal characteristics of EEG and ECG are calculated respectively, the frequency band power spectrum density value of EEG is extracted, and the HRV component characteristics of ECG are extracted; finally, the coupling relationship between the two kinds of signal characteristics is calculated based on the time-varying mutual information method; if the emotional disorder is improved, the intensity of the stimulation current is unchanged (i.e., the stimulation current amplitude is unchanged); otherwise, it is recorded that the emotional disorder is not improved, at this time the intensity of the stimulation current is increased by a set threshold (i.e., the current amplitude is increased by 0.5 mA), until the emotional disorder is improved, or the highest stimulation current threshold is reached. and

[0091] The above examples are only used to illustrate the principles and methods of the present application, and in actual application, the parameters and processes can be appropriately adjusted according to needs. The present application realizes innovative auxiliary evaluation by combining Hi-tACS intervention and EEG-ECG brain-heart feature fusion analysis, and has high feasibility. ​

[0092] Figure 2 A brain-heart interaction transcranial alternating current stimulation feedback control system is shown, comprising:

[0093] A brain-heart data acquisition unit 201 is configured to acquire electroencephalogram signals and electrocardiogram signals after transcranial alternating current stimulation;

[0094] A feature extraction unit 202 is configured to obtain heart rate variability features from the electroencephalogram signals, obtain electroencephalogram signal features from the electroencephalogram signals, and determine a dynamic coupling index from the electroencephalogram signals and the electrocardiogram signals;

[0095] An improved tracking control unit 203 is configured to determine that emotional disorders are improved when the heart rate variability features are greater than or equal to corresponding heart rate feature baseline thresholds, the electroencephalogram signal features are greater than or equal to corresponding electroencephalogram feature baseline thresholds, and the dynamic coupling index is greater than or equal to a set threshold, and then the intensity of the stimulation current remains unchanged; otherwise, the intensity of the stimulation current is increased by a set threshold until the emotional disorders are improved.

[0096] It can be understood that the above-mentioned units can be combined into one or several other units respectively or entirely, or some of the units can be further split into a plurality of units with smaller functions to constitute, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present application. The above-mentioned units are divided based on logical functions, and the functions of one unit can also be realized by multiple units, or the functions of multiple units can be realized by one unit. In other embodiments of the present application, the system can also include other units, and these functions can also be realized by other units in actual application, and can be realized by multiple units in cooperation.

[0097] According to another embodiment of the present application, the system of the present embodiment can be constructed by running a computer program (including program code) capable of performing each step involved in the corresponding method of the present application on a general computing device such as a computer including processing elements and storage elements such as a Central Processing Unit (CPU), a Random Access Memory (RAM), and a Read Only Memory (ROM), the computer program can be recorded on a computer readable recording medium, and loaded into the above-mentioned computing device through the computer readable recording medium, and run therein.

[0098] Figure 3A computer device is shown, which includes a processor 301, a communication interface 302 and a computer readable storage medium 303. Wherein, the processor 301, the communication interface 302 and the computer readable storage medium 303 can be connected through a bus or other ways.

[0099] Wherein, the communication interface 302 is used for receiving and sending data, the computer readable storage medium 303 can be stored in the memory of the electronic device, the computer readable storage medium 303 is used for storing computer programs, the computer programs include program instructions, and the processor 301 is used for executing the program instructions stored in the computer readable storage medium 303.

[0100] The processor 301 is the computing core and control core of the electronic device, which is suitable for implementing one or more instructions, and is particularly suitable for loading and executing one or more instructions to realize the corresponding method flow or corresponding function.

[0101] The processor 301 is configured to execute the following process:

[0102] Obtaining the electroencephalogram signal and the electrocardiogram signal after transcranial alternating current stimulation;

[0103] Obtaining the heart rate variability feature according to the electroencephalogram signal, obtaining the electroencephalogram signal feature according to the electroencephalogram signal, and determining the dynamic coupling index according to the electroencephalogram signal and the electrocardiogram signal;

[0104] When the heart rate variability feature is greater than or equal to the corresponding heart rate feature baseline threshold, the electroencephalogram signal feature is greater than or equal to the corresponding electroencephalogram feature baseline threshold, and the dynamic coupling index is greater than or equal to the set threshold, it is determined that the emotional disorder is improved, and the intensity of the stimulation current is unchanged; otherwise, the intensity of the stimulation current is increased by a set threshold until the emotional disorder is improved.

[0105] The application also provides a computer readable storage medium, which is a memory device in the electronic device, and is used for storing programs and data. It can be understood that the computer readable storage medium herein can include the built-in storage medium in the electronic device, and of course can also include the expansion storage medium supported by the electronic device. The computer readable storage medium provides a storage space, and the storage space stores the processing system of the electronic device.

[0106] And in the storage space, one or more instructions suitable for being loaded and executed by the processor are also stored, and the instructions can be one or more computer programs (including program codes). It should be noted that the computer readable storage medium herein can be a high-speed RAM memory, or a non-volatile memory such as at least one disk memory; optionally, it can also be at least one computer readable storage medium located away from the aforementioned processor.

[0107] In one embodiment, the computer readable storage medium stores one or more instructions; the one or more instructions stored in the computer readable storage medium are loaded and executed by the processor to implement the following process:

[0108] Obtaining electroencephalogram signals and electrocardiogram signals after transcranial alternating current stimulation;

[0109] Obtaining heart rate variability features from the electroencephalogram signals, obtaining electroencephalogram signal features from the electroencephalogram signals, and determining a dynamic coupling index from the electroencephalogram signals and the electrocardiogram signals;

[0110] When the heart rate variability features are greater than or equal to corresponding heart rate feature baseline thresholds, the electroencephalogram signal features are greater than or equal to corresponding electroencephalogram feature baseline thresholds, and the dynamic coupling index is greater than or equal to a set threshold, it is determined that the emotional disorder is improved, and the intensity of the stimulation current is unchanged; otherwise, the intensity of the stimulation current is increased by a set threshold until the emotional disorder is improved.

[0111] The application also provides a computer program product or a computer program, which includes computer instructions stored in a computer readable storage medium. The processor of the electronic device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to enable the electronic device to implement the following process:

[0112] Obtaining electroencephalogram signals and electrocardiogram signals after transcranial alternating current stimulation;

[0113] Obtaining heart rate variability features from the electroencephalogram signals, obtaining electroencephalogram signal features from the electroencephalogram signals, and determining a dynamic coupling index from the electroencephalogram signals and the electrocardiogram signals;

[0114] When the heart rate variability features are greater than or equal to corresponding heart rate feature baseline thresholds, the electroencephalogram signal features are greater than or equal to corresponding electroencephalogram feature baseline thresholds, and the dynamic coupling index is greater than or equal to a set threshold, it is determined that the emotional disorder is improved, and the intensity of the stimulation current is unchanged; otherwise, the intensity of the stimulation current is increased by a set threshold until the emotional disorder is improved.

[0115] Those of ordinary skill in the art can be aware that, in combination with the examples described in the embodiments disclosed in the present application, units and algorithm steps of each example can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether the functions are performed by hardware or software depends on the specific application and design constraints of the technical solution. Those of ordinary skill in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0116] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to the embodiments of the present application are generated. The computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in or transmitted by a computer readable storage medium. The computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through a wired (for example, coaxial cable, optical fiber, digital line) or wireless (for example, infrared, wireless, microwave, etc.) manner. The computer readable storage medium can be any available medium that can be accessed by a computer or a data processing device such as a server, data center, etc. integrated with one or more available media. The available media can be magnetic media (for example, floppy disk, hard disk, magnetic tape), optical media (for example, DVD), or semiconductor media (for example, solid state disk) and the like.

[0117] The above merely describes the preferred embodiments of the present application and is not intended to limit the present application. The present application can have various modifications and changes for those skilled in the art. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A transcranial strong alternating current stimulation feedback control system for brain-heart linkage, characterized in that, include: The brain-heart data acquisition unit is configured to acquire electroencephalogram (EEG) signals and electrocardiogram (ECG) signals after transcranial strong alternating current stimulation. The feature extraction unit is configured to: obtain heart rate variability features based on electrocardiogram (ECG) signals, obtain brainwave (EEG) signal features based on electroencephalogram (EEG) signals, and determine the dynamic coupling index based on EEG signals and ECG signals. Heart rate variability characteristics include: the standard deviation of all RR intervals of the ECG signal, the mean square of the difference between adjacent RR intervals of the ECG signal, and the ratio of low-frequency components to high-frequency components of the RR intervals of the ECG signal. EEG signal characteristics include: Wave power and Wave power; In the feature extraction unit, based on the first time-varying mutual information and the second time-varying mutual information Obtain the dynamic coupling index ,include: ,in, Represents brain signals wave components, This represents the low-frequency component of the RR interval in cardiac signals. Represents the high-frequency component of the RR interval in cardiac signals. Represents brain signals wave components, Represents brain signals Wave component mean, Represents brain signals Standard deviation of wave components Represents brain signals Wave component mean, Represents brain signals Standard deviation of wave components Representing delta wave components and Time-varying mutual information, represent Wave components and Time-varying mutual information; Calculate within the sliding window With LF and Coupling strength with HF ,include: Where X represents the EEG band power, and Y represents the HRV component. Represents the joint probability distribution of X and Y. Represents the marginal probability distribution of X. Represents the marginal probability distribution of Y. This represents summing over all possible values ​​x of X and all possible values ​​y of Y. Represents logarithmic operations; The improved tracking control unit is configured to: when the heart rate variability feature is greater than or equal to the corresponding heart rate feature baseline threshold, the EEG signal feature is greater than or equal to the corresponding EEG feature baseline threshold, and the dynamic coupling index is greater than or equal to a set threshold, it is determined that the mood disorder has been improved, and the intensity of the stimulation current remains unchanged; otherwise, the intensity of the stimulation current is increased by a set threshold until the mood disorder is improved.

2. The transcranial strong alternating current stimulation feedback control system for brain-heart linkage as described in claim 1, characterized in that, Determining the dynamic coupling index based on EEG and ECG signals includes: calculating EEG signals... First time-varying mutual information between wave components and low-frequency components of the RR interval of cardiac signals and EEG signals The second time-varying mutual information between the wave component and the high-frequency component of the RR interval of the cardiac signal is used to obtain the dynamic coupling index based on the first and second time-varying mutual information.

3. A computer device, characterized in that, include: Processor and computer-readable storage media; A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by the processor, implements the following method: Acquire electroencephalogram (EEG) and electrocardiogram (ECG) signals after transcranial strong alternating current stimulation; Heart rate variability characteristics are obtained from electrocardiogram (ECG) signals, brain signal characteristics are obtained from electroencephalogram (EEG) signals, and dynamic coupling index is determined based on EEG and ECG signals. Heart rate variability characteristics include: the standard deviation of all RR intervals of the ECG signal, the mean square of the difference between adjacent RR intervals of the ECG signal, and the ratio of low-frequency components to high-frequency components of the RR intervals of the ECG signal. EEG signal characteristics include: Wave power and Wave power; In the feature extraction unit, based on the first time-varying mutual information and the second time-varying mutual information Obtain the dynamic coupling index ,include: ,in, Represents brain signals wave components, This represents the low-frequency component of the RR interval in cardiac signals. Represents the high-frequency component of the RR interval in cardiac signals. Represents brain signals wave components, Represents brain signals Wave component mean, Represents brain signals Standard deviation of wave components Represents brain signals Wave component mean, Represents brain signals Standard deviation of wave components Representing delta wave components and Time-varying mutual information, represent Wave components and Time-varying mutual information; Calculate within the sliding window With LF and Coupling strength with HF ,include: Where X represents the EEG band power, and Y represents the HRV component. Represents the joint probability distribution of X and Y. Represents the marginal probability distribution of X. Represents the marginal probability distribution of Y. This represents summing over all possible values ​​x of X and all possible values ​​y of Y. Represents logarithmic operations; When the heart rate variability feature is greater than or equal to the corresponding heart rate feature baseline threshold, the EEG signal feature is greater than or equal to the corresponding EEG feature baseline threshold, and the dynamic coupling index is greater than or equal to the set threshold, it is determined that the mood disorder has improved, and the intensity of the stimulation current remains unchanged; otherwise, the intensity of the stimulation current is increased by the set threshold until the mood disorder is improved.

4. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program adapted to be loaded by a processor and executed as follows: Acquire electroencephalogram (EEG) and electrocardiogram (ECG) signals after transcranial strong alternating current stimulation; Heart rate variability characteristics are obtained from electrocardiogram (ECG) signals, brain signal characteristics are obtained from electroencephalogram (EEG) signals, and dynamic coupling index is determined based on EEG and ECG signals. Heart rate variability characteristics include: the standard deviation of all RR intervals of the ECG signal, the mean square of the difference between adjacent RR intervals of the ECG signal, and the ratio of low-frequency components to high-frequency components of the RR intervals of the ECG signal. EEG signal characteristics include: Wave power and Wave power; In the feature extraction unit, based on the first time-varying mutual information and the second time-varying mutual information Obtain the dynamic coupling index ,include: ,in, Represents brain signals wave components, This represents the low-frequency component of the RR interval in cardiac signals. Represents the high-frequency component of the RR interval in cardiac signals. Represents brain signals wave components, Represents brain signals Wave component mean, Represents brain signals Standard deviation of wave components Represents brain signals Wave component mean, Represents brain signals Standard deviation of wave components Representing delta wave components and Time-varying mutual information, represent Wave components and Time-varying mutual information; Calculate within the sliding window With LF and Coupling strength with HF ,include: Where X represents the EEG band power, and Y represents the HRV component. Represents the joint probability distribution of X and Y. Represents the marginal probability distribution of X. Represents the marginal probability distribution of Y. This represents summing over all possible values ​​x of X and all possible values ​​y of Y. Represents logarithmic operations; When the heart rate variability feature is greater than or equal to the corresponding heart rate feature baseline threshold, the EEG signal feature is greater than or equal to the corresponding EEG feature baseline threshold, and the dynamic coupling index is greater than or equal to the set threshold, it is determined that the mood disorder has improved, and the intensity of the stimulation current remains unchanged; otherwise, the intensity of the stimulation current is increased by the set threshold until the mood disorder is improved.

5. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the following method: Acquire electroencephalogram (EEG) and electrocardiogram (ECG) signals after transcranial strong alternating current stimulation; Heart rate variability characteristics are obtained from electrocardiogram (ECG) signals, brain signal characteristics are obtained from electroencephalogram (EEG) signals, and dynamic coupling index is determined based on EEG and ECG signals. Heart rate variability characteristics include: the standard deviation of all RR intervals of the ECG signal, the mean square of the difference between adjacent RR intervals of the ECG signal, and the ratio of low-frequency components to high-frequency components of the RR intervals of the ECG signal. EEG signal characteristics include: Wave power and Wave power; In the feature extraction unit, based on the first time-varying mutual information and the second time-varying mutual information Obtain the dynamic coupling index ,include: ,in, Represents brain signals wave components, This represents the low-frequency component of the RR interval in cardiac signals. Represents the high-frequency component of the RR interval in cardiac signals. Represents brain signals wave components, Represents brain signals Wave component mean, Represents brain signals Standard deviation of wave components Represents brain signals Wave component mean, Represents brain signals Standard deviation of wave components Representing delta wave components and Time-varying mutual information, represent Wave components and Time-varying mutual information; Calculate within the sliding window With LF and Coupling strength with HF ,include: Where X represents the EEG band power, and Y represents the HRV component. Represents the joint probability distribution of X and Y. Represents the marginal probability distribution of X. Represents the marginal probability distribution of Y. This represents summing over all possible values ​​x of X and all possible values ​​y of Y. Represents logarithmic operations; When the heart rate variability feature is greater than or equal to the corresponding heart rate feature baseline threshold, the EEG signal feature is greater than or equal to the corresponding EEG feature baseline threshold, and the dynamic coupling index is greater than or equal to the set threshold, it is determined that the mood disorder has improved, and the intensity of the stimulation current remains unchanged; otherwise, the intensity of the stimulation current is increased by the set threshold until the mood disorder is improved.

Images