Closed-loop vagus nerve intervention control system, method and device combined with meditation treatment, processor and computer readable storage medium thereof

Abstract

The invention relates to a closed-loop vagus nerve intervention control system combined with meditation treatment, and the system comprises a physiological signal collection module which is used for collecting an EEG (electroencephalogram) signal and an ECG (electrocardiosignal) signal of a patient in real time; the data processing and analyzing unit is used for calculating heart-brain and brain-heart bidirectional coupling coefficients on the basis of the acquired signal data and evaluating the execution control function indexes of the patient in real time; the stimulation parameter regulation and control module is used for dynamically adjusting the frequency, the pulse width and the current intensity of the transaural vagus nerve stimulation taVNS according to the calculated bidirectional coupling coefficient; the meditation guidance module is used for providing personalized meditation guidance based on the real-time heart and brain state; the feedback control module is connected with the stimulation parameter regulation and control module and the meditation guidance module and is used for realizing collaborative optimization of stimulation parameters and meditation intervention through a closed-loop adaptive mechanism; and the execution control evaluation module is connected with the feedback control module and is used for improving the risk decision-making capability of the system for joint meditation treatment by setting a plurality of task evaluation modes.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical neuromodulation, especially to the technical field of brain-body integrated intervention for the treatment of neuropsychiatric disorders, and particularly to a closed-loop vagus nerve intervention control system, method, device, processor and computer readable storage medium thereof combined with meditation therapy. BACKGROUND

[0002] At present, vagus nerve stimulation (VNS) and meditation therapy have been widely used as intervention means for neuropsychiatric disorders, but the existing technology still has the following significant defects: 1. Difficulty in parameter optimization: Traditional vagus nerve stimulation technology uses a fixed parameter scheme, which cannot be dynamically adjusted according to the real-time physiological state of the patient, resulting in unstable intervention effect and large individual differences. Moreover, the stimulation parameters are usually determined by clinical experience or trial-and-error method, lacking objective quantitative basis.

[0003] 2. Insufficient treatment integration: Existing vagus nerve stimulation and meditation intervention are usually implemented as independent therapies, failing to achieve the synergistic effect of the two intervention methods at the neural mechanism level, and the single intervention method has limited effect on the complex pathophysiological process of addictive disorders.

[0004] 3. Lack of feedback mechanism: Traditional intervention systems lack effective real-time physiological indicator monitoring and feedback channels, and cannot adjust the intervention strategy according to the patient's immediate response, resulting in a "open-loop" treatment mode with delayed efficacy evaluation and insufficient intervention accuracy.

[0005] 4. Lack of heart-brain coupling evaluation: Existing technology fails to effectively quantify and utilize the heart-brain functional coupling state as an evaluation indicator of intervention effect, ignoring the important role of the bidirectional regulation relationship between the autonomic nervous system and the central nervous system in addictive mechanisms.

[0006] 5. Insufficient improvement of executive control function: Existing intervention methods have limited improvement of executive control function in patients with addictive disorders, especially in key cognitive areas such as risk decision-making and impulse control, resulting in high relapse risk. SUMMARY

[0007] The purpose of the present application is to overcome the above-mentioned shortcomings of the prior art, and to provide a closed-loop vagus nerve intervention control system, method, device, processor and computer readable storage medium thereof combined with meditation therapy.

[0008] In order to achieve the above-mentioned purpose, the closed-loop vagus nerve intervention control system, method, device, processor and computer readable storage medium thereof combined with meditation therapy of the present application are as follows: The closed-loop vagus nerve intervention control system combined with meditation therapy, wherein the system comprises: a physiological signal acquisition module, configured to acquire electroencephalogram (EEG) and electrocardiogram (ECG) signals of a patient in real time; a data processing and analysis unit, connected to the physiological signal acquisition module, configured to calculate bidirectional coupling coefficients of heart-to-brain and brain-to-heart based on the acquired signal data, and to evaluate a patient's performance control index in real time; a stimulation parameter regulation module, connected to the data processing and analysis unit, configured to dynamically adjust the frequency, pulse width and current intensity of transauricular vagus nerve stimulation (taVNS) according to the calculated bidirectional coupling coefficients; a meditation guidance module, connected to the data processing and analysis unit, configured to provide personalized meditation guidance based on real-time heart-brain state; a feedback control module, connected to the stimulation parameter regulation module and the meditation guidance module, configured to realize the synergistic optimization of stimulation parameters and meditation intervention through a closed-loop adaptive mechanism; and a performance control evaluation module, connected to the feedback control module, configured to improve the risk decision-making ability of the system for combined meditation therapy by setting multiple task evaluation methods.

[0009] Preferably, the physiological signal acquisition module uses a 128-lead high-density EEG cap and a standard II-lead ECG electrode to form a sensor unit, and acquires the EEG and ECG signals according to the following configuration information: EEG: sampling rate 1000 Hz, filter bandwidth 0.1-100 Hz, focusing on collecting the Gamma band energy of the frontal lobe and parietal lobe at 30-100 Hz; ECG: sampling rate 500 Hz, filter bandwidth 0.5-40 Hz, extracting the low-frequency (LF) and high-frequency (HF) components of heart rate variability (HRV) from the R-R interval; and transmitting the collected data to the data processing and analysis unit in real time through a wireless Bluetooth module.

[0010] Preferably, the bidirectional coupling coefficients are obtained according to the following calculation model: heart-to-brain coupling coefficient: wherein, is the predicted EEG energy in the Gamma band at 30-100 Hz, is the ratio of low-frequency and high-frequency components of HRV, is a weight coefficient, is a time delay, is an error term; brain-to-heart coupling coefficient, wherein, For the predicted heart rate variability, EEGf is the phase information of the electroencephalogram signal in the 8-12Hz Alpha frequency band, bf is the frequency-related parameter, and c is the constant term.

[0011] Preferably, the stimulation parameter regulation module adopts an adaptive PID algorithm to dynamically adjust the stimulation parameters. ; Among them, is the real-time current intensity, is the basic current value, = 0.3, = 0.1, = 0.05 is the initial PID parameter, is the deviation of the heart-brain coupling coefficient from the target value, when the heart-brain coupling coefficient is lower than the threshold (H→B < 0.3 or B→H < 0.25), that is, , the system increases the transaural vagus nerve stimulation taVNS current intensity; when the heart-brain coupling coefficient recovers (H→B > 0.4 and B→H > 0.35), that is, , the system reduces the transaural vagus nerve stimulation taVNS current intensity.

[0012] Preferably, the meditation guidance module is based on the rhythm of the electroencephalogram wave to adjust the meditation breathing frequency to a phase-harmonic double-locking relationship with the wave dominant frequency, and the specific processing process is as follows: (a) Dynamic fundamental frequency extraction, individualized fundamental frequency is dynamically calculated as follows: (a1) Preprocess the original electroencephalogram data, extract the frontal lobe and parietal lobe region electroencephalogram signal, extract the frequency band using an 8-12Hz bandpass filter, and perform 50Hz power frequency notch processing to eliminate power supply interference; (a2) Instantaneous frequency calculation: set a sliding window (initial length 2s), calculate the mode of the instantaneous frequency in the window; (a3) Individualized baseline calibration: record the baseline for 5 minutes before each stimulation, calculate the resting peak frequency f_baseline as the reference frequency; (b) Design a dynamic harmonic order selection mechanism: Among them, is the target breathing frequency, that is, the ideal breathing frequency that the system guides the patient to reach, is the wave dominant frequency, that is, the individualized wave fundamental frequency extracted above, is the real-time breathing phase and peak phase difference, for the current respiratory cycle length T, the harmonic order = 1, , According to the frequency band coherence coefficient dynamically selects the harmonic order ; (c) Respiratory closed-loop feedback synchronization guiding control: through auditory guidance, the respiratory frequency is locked to , and the meditation module settings and guidance content are adjusted according to the heart-brain coupling state; (c1) Auditory guidance generation and synchronization: meditation voice intervention by professional psychotherapists to guide the adjustment of respiratory rhythm; (c2) According to the heart-brain coupling coefficient, set the meditation guidance level corresponding to the meditation guidance module; (d) Multi-sensory feedback mechanism: including visual, auditory and / or tactile signals, to strengthen the patient's perception of their own physiological state.

[0013] The closed-loop vagus nerve intervention control method for combined meditation therapy realized by the system described above, the main feature is that the method comprises the following steps: (1) Collect the brain electrical signal EEG and the heart electrical signal ECG of the patient in the baseline state, and calculate the initial heart-brain coupling coefficient; (2) According to the heart-brain coupling coefficient-addiction behavior state mapping model, set the initial parameters of the transauricular vagus nerve stimulation taVNS according to the calculated initial coupling coefficient; (3) Guide the patient to enter a meditation state synchronized with the wave rhythm; (4) Real-time monitoring of heart-brain coupling coefficient changes, dynamic adjustment of transauricular vagus nerve stimulation taVNS parameters and meditation guidance content; (5) Regularly assess the patient's executive control function indicators to verify the intervention effect.

[0014] Preferably, the method further comprises adopting differentiated parameters for patients with different addiction severity in the following manner: For acute withdrawal period patients, use higher frequency (25-40Hz), lower intensity (0.1-0.8mA) stimulation parameters and simple focused breathing meditation guidance; For patients in the recovery period, use medium frequency (15-25Hz), medium intensity (0.5-1.2mA) stimulation parameters and advanced body scanning meditation guidance; For patients in the maintenance period, use lower frequency (5-15Hz), higher intensity (0.8-1.5mA) stimulation parameters and high-order awareness meditation guidance.

[0015] The closed-loop vagus nerve intervention control device for the combined meditation treatment has the following main features: the device comprises: a sensor unit comprising a wireless EEG acquisition device and a wearable electrocardiogram monitoring device; a signal processing unit comprising signal amplification, filtering, denoising and feature extraction modules; an embedded controller for running a heart-brain coupling analysis algorithm and a PID control algorithm; a stimulation electrode arranged at an auricular vagus nerve stimulation site, an auricle or a neck vagus nerve stimulation site; a human-computer interaction terminal for presenting a meditation guidance interface and real-time feedback information; a processor configured to execute computer executable instructions; a memory storing one or more computer executable instructions, which, when executed by the processor, implement the steps of the closed-loop vagus nerve intervention control method for the combined meditation treatment.

[0016] The closed-loop vagus nerve intervention control processor for the combined meditation treatment has the following main features: the processor is configured to execute computer executable instructions, which, when executed by the processor, implement the steps of the closed-loop vagus nerve intervention control method for the combined meditation treatment.

[0017] The computer readable storage medium has the following main features: a computer program is stored thereon, which can be executed by a processor to implement the steps of the closed-loop vagus nerve intervention control method for the combined meditation treatment.

[0018] The closed-loop vagus nerve intervention control system, method, device, processor and computer readable storage medium for the combined meditation treatment of the present application can dynamically adjust vagus nerve stimulation parameters according to real-time physiological indicators of a patient, and form an effective closed-loop feedback mechanism in combination with personalized meditation guidance, so as to realize precise and personalized treatment of addiction disorders, significantly improve executive control function, reduce relapse risk, and provide a new technical solution for brain-heart-body integrated intervention. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 FIG. 1 is a schematic diagram of a framework structure of the closed-loop vagus nerve intervention control system for the combined meditation treatment of the present application. DETAILED DESCRIPTION

[0020] In order to more clearly describe the technical content of the present application, further description will be made in combination with specific embodiments.

[0021] Before embodiments consistent with the present application are described in detail, it is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. It should also be understood that where the description uses terms such as "including", "containing", or "comprising", these terms are used as equivalent synonyms for the word "comprising". Furthermore, it should be understood that the use of the terms "first", "second", "third", etc. to describe a variety of information are used to distinquish the information, but are not intended to signify that the information is in any way inferior to other information.

[0022] Referring to Figure 1 The closed-loop vagus nerve intervention control system for combined meditation therapy is shown, wherein the system comprises: A physiological signal acquisition module is configured to acquire electroencephalogram (EEG) and electrocardiogram (ECG) signals of a patient in real time. A data processing and analysis unit is connected to the physiological signal acquisition module, and is configured to calculate bidirectional coupling coefficients of heart-to-brain and brain-to-heart based on the acquired signal data, and to evaluate a control function index of the patient in real time. A stimulation parameter regulation module is connected to the data processing and analysis unit, and is configured to dynamically adjust a frequency, pulse width and current intensity of transauricular vagus nerve stimulation (taVNS) based on the calculated bidirectional coupling coefficients. A meditation guidance module is connected to the data processing and analysis unit, and is configured to provide personalized meditation guidance based on real-time heart-brain states. A feedback control module is connected to the stimulation parameter regulation module and the meditation guidance module, and is configured to realize collaborative optimization of stimulation parameters and meditation intervention through a closed-loop adaptive mechanism. An execution control evaluation module is connected to the feedback control module, and is configured to improve a risk decision-making capability of the system for combined meditation therapy by setting multiple task evaluation modes.

[0023] As a preferred embodiment of the present application, the physiological signal acquisition module uses a 128-lead high-density EEG cap and a standard II-lead ECG electrode to form a sensor unit, and acquires the EEG and ECG signals according to the following configuration information: The EEG signal has a sampling rate of 1000 Hz, a filter bandwidth of 0.1-100 Hz, and focuses on collecting Gamma band energy of 30-100 Hz in the frontal lobe and parietal lobe. The ECG signal has a sampling rate of 500 Hz, a filter bandwidth of 0.5-40 Hz, and extracts low-frequency component (LF) and high-frequency component (HF) of heart rate variability (HRV) from R-R interval. The acquired data is transmitted to the data processing and analysis unit in real time through a wireless Bluetooth module.

[0024] As a preferred embodiment of the present application, the control function index is obtained through one or more of the following task evaluations: Stop signal task (SST), measuring inhibition control ability; Balloon analog risk task (BART), assessing risk decision-making behavior; Delay discounting task (DDT), measuring time discounting tendency; Working memory task (n-back), assessing cognitive control level.

[0025] As a preferred embodiment of the present application, the bidirectional coupling coefficient is obtained according to the following calculation model: Heart-brain coupling coefficient: Wherein, EEGf is the predicted brain electrical signal energy in the 30-100Hz Gamma band, is the ratio of low frequency and high frequency components of heart rate variability, is the weight coefficient, is the time delay, is the error term; Brain-heart coupling coefficient, Wherein, EEGf is the predicted heart rate variability, EEGf is the phase information of the brain electrical signal in the 8-12Hz Alpha band, bf is the frequency related parameter, and c is the constant term.

[0026] As a preferred embodiment of the present application, the stimulation parameter regulation module adopts an adaptive PID algorithm to dynamically adjust the stimulation parameters: ; Wherein, is the real-time current intensity, is the basic current value, = 0.3, = 0.1, = 0.05 is the initial PID parameter, is the deviation of heart-brain coupling coefficient and target value, when the heart-brain coupling coefficient is lower than the threshold (H→B < 0.3 or B→H < 0.25), that is, , the system increases the transauricular vagus nerve stimulation taVNS current intensity; when the heart-brain coupling coefficient recovers (H→B > 0.4 and B→H > 0.35), that is, , the system reduces the transauricular vagus nerve stimulation taVNS current intensity.

[0027] As a preferred embodiment of the present application, the meditation guidance module adjusts the meditation breathing frequency to the phase-harmonic double locking relationship with the alpha main frequency based on the rhythm of the brain electrical wave, and the specific processing process is as follows: ​(a) Dynamic fundamental frequency extraction, dynamically calculate individualization fundamental frequency; (a1) Preprocess the raw EEG data, extract the frontal and parietal region EEG signals, extract the 8-12Hz band with a 8-12Hz bandpass filter, and perform 50Hz power frequency notch processing to eliminate power supply interference; (a2) Instantaneous frequency calculation: set a sliding window (initial length 2s), calculate the mode of the instantaneous frequency in the window; (a3) Individualized baseline calibration: record 5 minutes of baseline before each stimulation, calculate the resting state peak frequency f_baseline as reference frequency; (b) Design a dynamic harmonic order selection mechanism: , wherein is the target respiratory frequency, i.e. the ideal respiratory frequency that the system guides the patient to reach, is the wave main frequency, i.e. the individualized wave fundamental frequency extracted above, is the real-time respiratory phase and wave peak phase difference, is the current respiratory cycle length T, the harmonic order = 1, , , according to the coherence coefficient of the band to dynamically select the harmonic order ; (c) Respiratory closed-loop feedback synchronization control: through auditory guidance, lock the respiratory frequency to , and adjust the meditation module settings and guidance content according to the heart-brain coupling state; (c1) Auditory guidance generation and synchronization: meditation voice intervention by professional psychotherapists to guide the adjustment of respiratory rhythm; (c2) According to the heart-brain coupling coefficient, set the meditation guidance level corresponding to the meditation guidance module, as follows: ; (d) Multi-sensory feedback mechanism: including visual, auditory and / or tactile signals, to enhance the patient's perception of their own physiological state.

[0028] The processing process of each functional module of the system will be further described below with reference to specific application examples: 1.1 Physiological signal acquisition module ​The embodiment adopts 128-lead high-density electroencephalogram cap (Neuroscan SynAmps2) and standard II-lead electrocardiogram electrode (Ambu BlueSensor) to form a sensor unit, and the specific configuration is as follows: • EEG signal: sampling rate 1000Hz, filter bandwidth 0.1-100Hz, and Gamma band (30-100Hz) energy of frontal lobe (F3 / F4 / Fz) and parietal lobe (P3 / P4 / Pz) is collected; • ECG signal: sampling rate 500Hz, filter bandwidth 0.5-40Hz, and low-frequency component LF (0.04-0.15Hz) and high-frequency component HF (0.15-0.4Hz) of heart rate variability (HRV) are extracted by extracting R-R interval; Meanwhile, the module also transmits data to the data processing unit in real time through the wireless Bluetooth module (CSR8670), and the data transmission delay is less than 10ms.

[0029] 1.2 Data processing and analysis unit The unit is based on ARM Cortex-M4 processor (maximum frequency 180MHz) to run the heart-brain coupling analysis algorithm, and calculates the bidirectional coupling coefficients of heart-to-brain (H→B) and brain-to-heart (B→H): Heart-to-brain coupling (H→B) calculation formula: ; Wherein, is the predicted frontal lobe Gamma band energy, HLF / HF is the LF / HF ratio of heart rate variability, is the weight coefficient, is the time delay (50-500ms), is the error term.

[0030] Brain-to-heart coupling (B→H) calculation formula: ; Wherein, is the predicted HRV-HF power, is the Alpha band (8-12Hz) phase information, is the frequency-related parameter, is the constant term.

[0031] The system evaluates the executive control index of the patient in real time by monitoring the stop signal task (SST) and balloon analog risk task (BART), and the SST measures the inhibition control ability and the BART evaluates the risk decision behavior.

[0032] 1.3 Stimulus parameter regulation module This module uses transaural vagus nerve stimulation (taVNS) technology, with the stimulation electrode placed in the left ear cymba conchae area. The stimulation parameters are dynamically adjusted in the following range: • Frequency: 20-40 Hz • Pulse width: fixed at 500 μs • Current intensity: 0.1-1.5 mA The core of this module is the use of adaptive PID algorithm to dynamically adjust the stimulation parameters, with the formula: ; where, is the real-time current intensity, is the base current value (0.5 mA), is the deviation of the heart-brain coupling coefficient from the target value. When the heart-brain coupling coefficient is below the threshold (H→B < 0.3 or B→H < 0.25), the system increases the taVNS current intensity; when the heart-brain coupling coefficient recovers (H→B > 0.4 and B→H > 0.35), the system reduces the taVNS current intensity, is the initial PID parameter, which is continuously optimized based on the patient's historical response data through machine learning algorithms.

[0033] 1.4 Meditation Guidance Module This module includes a breath-synchronized guidance algorithm based on EEG Alpha rhythm (8-12 Hz), adjusting the meditation breathing frequency (4-8 times / minute) to form a harmonic and phase-synchronized relationship with the Alpha wave main frequency. The system presents a breath guidance animation through a 7-inch touch display screen, and is equipped with wireless earphones to provide voice guidance.

[0034] When the HRV-HF power decreases (indicating relative hyperactivity of the sympathetic nervous system), the system automatically triggers the voice instruction "Focus on breathing"; when the Alpha wave amplitude is detected to be enhanced, the system gives positive reinforcement feedback. The guidance content is designed based on the principles of Mindfulness-Based Stress Reduction (MBSR), including three levels of breath focus, body scan, and open awareness.

[0035] 1.5 Feedback Control Module This module integrates information from each unit to form a closed-loop feedback mechanism. When the heart-brain coupling coefficient is below the threshold (H→B < 0.3 or B→H < 0.25), the system automatically increases the taVNS current intensity to increase vagus nerve tension; at the same time, it adjusts the difficulty of meditation and guides the patient to focus on simple breath-focused exercises. When the coupling coefficient recovers (H→B > 0.4 and B→H > 0.35), the stimulation intensity is gradually reduced and the user is guided into a higher-order meditation state.

[0036] ​​The closed-loop control algorithm is updated every 100 ms, and the PID controller optimizes the parameters based on the trend of the coupling coefficient change within a sliding time window (5 minutes).

[0037] The closed-loop vagus nerve intervention control method for implementing joint meditation treatment by using the system described above, wherein the method comprises the following steps: (1) Collecting the electroencephalogram (EEG) and electrocardiogram (ECG) of a patient in a baseline state, and calculating an initial heart-brain coupling coefficient; (2) According to a heart-brain coupling coefficient-addiction behavior state mapping model, setting the initial parameters of transauricular vagus nerve stimulation (taVNS) according to the calculated initial coupling coefficient; (3) Guiding the patient to enter a meditation state synchronized with the wave rhythm; (4) Real-time monitoring of the change of the heart-brain coupling coefficient, and dynamically adjusting the parameters of taVNS and the content of meditation guidance; (5) Regularly evaluating the patient's executive control function index to verify the intervention effect.

[0038] As a preferred embodiment of the present application, the method further comprises adopting differentiated parameters for patients with different addiction severity in the following manner: For patients in the acute withdrawal period, higher frequency (25-40 Hz), lower intensity (0.1-0.8 mA) stimulation parameters and simple focused breathing meditation guidance are adopted; For patients in the recovery period, medium frequency (15-25 Hz), medium intensity (0.5-1.2 mA) stimulation parameters and advanced body scanning meditation guidance are adopted; For patients in the maintenance period, lower frequency (5-15 Hz), higher intensity (0.8-1.5 mA) stimulation parameters and high-order mindfulness meditation guidance are adopted.

[0039] The implementation of the technical solution will be further described below in combination with specific embodiments: Example 1: In an 8-week intervention trial of 10 alcohol-dependent patients (DSM-5 diagnostic criteria), the closed-loop system achieved the following effects compared with traditional fixed parameter VNS: • The HF component of heart rate variability (HF) was increased by an average of 32%, reflecting the improvement of parasympathetic nervous function; • The reaction inhibition time of the stop signal task (SST) was shortened by 28% (from 320 ms to 230 ms); • The high-risk selection rate of the balloon analog risk task (BART) was reduced by 40%; • The average reduction of craving score (VAS) was 45%; • The relapse rate during the 8-week follow-up period was reduced by 38%. ​

[0040] Example 2: Differentiated parameter settings for different addiction types This example provides differentiated implementations for three different types of addiction disorders (alcohol, methamphetamine, and opioids).

[0041] 2.1 Alcohol addiction patient program Based on the specific impact of alcohol on the GABA system, this program uses the following parameter settings: • taVNS parameters: medium frequency (25-35 Hz), medium intensity (0.5-1.2 mA); • Heart-brain coupling target threshold: H→B > 0.35, B→H > 0.30; • Meditation focus: body scan meditation, with special attention to somatic sensations; • PID parameters: Kp = 0.25, Ki = 0.15, Kd = 0.04.

[0042] 2.2 Methamphetamine addiction patient program Considering the strong effect of methamphetamine on the dopamine system, this program uses: • taVNS parameters: higher frequency (30-40 Hz), lower intensity (0.3-0.8 mA); • Heart-brain coupling target threshold: H→B > 0.40, B→H > 0.35; • Meditation focus: focused breathing meditation, with emphasis on inhibition control; • PID parameters: Kp = 0.35, Ki = 0.10, Kd = 0.06.

[0043] 2.3 Opioid addiction patient program Considering the characteristics of the action of opioid drugs on μ-opioid receptors, this program uses: • taVNS parameters: lower frequency (15-25 Hz), higher intensity (0.8-1.5 mA); • Heart-brain coupling target threshold: H→B > 0.30, B→H > 0.30; • Meditation focus: mindfulness meditation, with emphasis on emotional regulation; • PID parameters: Kp = 0.20, Ki = 0.20, Kd = 0.03.

[0044] Implementation effects In the clinical application of 30 patients with different types of addiction disorders (10 each), the differentiated program significantly improved the treatment effect compared to the standard program: • Alcohol addiction patients: improvement rate of executive control function increased by 32%, relapse rate decreased by 41%; • Methamphetamine addicts: 36% increase in the reduction of craving scores, 45% improvement in impulse control; • Opioid addicts: 29% reduction in withdrawal symptom scores, 38% improvement in emotional stability.

[0045] Example 3: Hardware and Software System Implementation Scheme 3.1 Hardware Architecture This embodiment provides a detailed hardware configuration of the system: 1. Sensor Unit: Wireless EEG Cap: Dry electrode technology, weight < 250g, battery life > 8 hours; Flexible ECG Electrode: Medical-grade conductive material, high comfort, can be worn continuously for 72 hours; Integrated Sensor Module Size: 90mm x 60mm x 15mm, weight < 80g; 2. Signal Processing Unit: Main Chip: Integrated ADC chip (ADS1299) and ARM Cortex-M4 processor; Digital Signal Processing: 4th order Butterworth filter, 50Hz notch filter; Storage: 32GB microSD card, can store >1 week of continuous data.

[0046] 3. Stimulation Module: Stimulation Electrode: Medical-grade silver / silver chloride electrode, contact area 50mm²; Stimulation Generator: Customized Auricular Vagus Nerve Stimulator (Model TR-01); Safety Mechanism: Overcurrent Protection (Maximum Current 1.8mA), Automatic Power Off Protection.

[0047] 4. Interaction Interface: 7-inch capacitive touch screen, resolution 1024x600; Bluetooth 5.0 module, connected with mobile devices; Charging Battery: 3000mAh Lithium Battery, Continuous Working Time > 12 hours.

[0048] 3.2 Software Algorithm This embodiment details the software implementation functions of the system: 1. Heart-Brain Coupling Algorithm: ① EEG-ECG Time-Frequency Coupling Calculation: Heart-to-Brain Coupling Coefficient: Where, is the predicted brain electrical signal energy in the 30-100Hz Gamma band, is the low-to-high frequency component ratio of heart rate variability, w is the weight coefficient, τ is the time delay, ε is the error term; brain-heart coupling coefficient, where, is the predicted heart rate variability, EEGf is the phase information of the electroencephalogram signal in the 8-12 Hz Alpha band, bf is the frequency-dependent parameter, and c is a constant term.

[0049] ② Granger causality test to assess bidirectional information flow: For two time series X (i.e., HRV) and Y (i.e., EEG α), two vector autoregressive (VAR) models are established; Restricted model (predict Y): Y(t) = Σ_{i=1}^{p} A_i · Y(t-i) + ε_1; Full model (predict Y): Y(t) = Σ_{i=1}^{p} B_i · Y(t-i) + Σ_{j=1}^{p} C_j· X(t-j) + ε_2; where p is the model order (determined by AIC / BIC criteria), and ε is the prediction error.

[0050] Calculate the causal strength Compare the prediction error variances of the two models. If the past values of X can significantly improve the prediction accuracy of Y, it is said that "X Granger causes Y". Formula: G_{X→Y} = ln( var(ε_1) / var(ε_2) ).

[0051] Similarly, by swapping X and Y, we can calculate G_{Y→X}. Two scalar values G_{H→B} and G_{B→H} represent the directional causal strength from heart to brain and from brain to heart, respectively. The larger the value, the stronger the driving effect.

[0052] ③ Machine learning model: Use support vector regression (SVR) to predict H→B and B→H coupling coefficients.

[0053] 2. Adaptive PID control algorithm implementation code framework: function calculateStimulationParams(currentCoupling, targetCoupling,previousError, integralError, lastTime) { const now = getCurrentTime(); const dt = now - lastTime; / / Calculate current error const error = targetCoupling - currentCoupling; / / Calculate error integral ; / / Calculate error derivative const derivativeError = (error - previousError) / dt; / / PID control to calculate new stimulation intensity ; / / Limit output range const limitedCurrent = Math.max(MIN_CURRENT, Math.min(MAX_CURRENT, outputCurrent)); return { stimCurrent: limitedCurrent, newError: error, newIntegralError: integralErrorNew, newTime: now }; } 3. Meditation guidance algorithm: Peak Alpha Frequency (PAF) detection; Breathing guidance rhythm adaptation algorithm, setting breathing frequency based on PAF / n (n=4,5,6); Voice feedback threshold: HF power drop >15% triggers intervention.

[0054] Implementation effect The hardware-software integrated system achieved the following effects in a 20-person addiction disorder patient home use test: • System stability: failure rate <2% within a 30-day test period; • User compliance: average daily use time reached 68 minutes, 40% higher than traditional solutions; • Physiological index improvement: resting state Alpha / Theta ratio increased by 28%, HRV index RMSSD increased by 35%; • Clinical outcomes: 65% reduction in withdrawal symptoms, a 31% increase over traditional follow-up models; • User satisfaction: 9.2 / 10, with main feedback being "easy to use" and "perceived significant improvement".

[0055] Example 4: Mobile Health Application Integration Solution This example describes the integration of a closed-loop system with a smartphone app, enabling extended functionality for remote monitoring and data analysis.

[0056] 4.1 Mobile Application Functional Architecture The closed-loop vagus nerve intervention system communicates in real-time with the phone application via Bluetooth, and the APP includes the following modules: • User Interface: Intuitive display of real-time heart-brain coupling status, intervention parameters, and treatment progress; • Data Visualization: Display of physiological indicator trend graphs, intervention effect statistics, and performance on executive control tasks; • Meditation Guidance: Provides audio / video guidance, adjusted in real-time with physiological feedback; • Behavior Tracking: Records behavior data such as craving level, emotional state, and sleep quality; • Expert Connection: Allows doctors to remotely adjust parameters, view reports, and provide advice.

[0057] 4.2 Data Synchronization and Cloud Platform Analysis The application uploads data to the cloud server through an encrypted channel, enabling the following functions: • Machine learning algorithms analyze long-term data trends to optimize personalized intervention plans; • Multi-center data aggregation to establish more accurate heart-brain coupling prediction models; • Generate personalized risk warnings to intervene in potential relapse risks in advance; • Doctor-side management system supporting remote monitoring and timely intervention for multiple patients.

[0058] 4.3 Remote Monitoring and Early Warning Mechanism The system is equipped with an intelligent early warning mechanism: • Real-time risk assessment: Calculate relapse risk scores based on physiological indicators and behavior data; • Tiered warning strategy: Different intervention intensities for low, medium, and high risk levels; • Automatic intervention response: Automatically increase taVNS stimulation intensity and meditation prompt frequency for high-risk situations; • Emergency support system: Automatically notify medical staff and family members for support in critical risk situations.

[0059] Implementation Effect This mobile health integration solution achieved the following results in a 6-month follow-up of 50 addiction patients: • Patient compliance improved by 63%, treatment completion rate reached 92%; • Early warning accuracy reached 83%, predicting possible relapse 2.6 days in advance; • Emergency intervention success rate reached 78%, effectively reducing severe relapse events; • Physician-patient communication frequency increased by 46%, treatment satisfaction improved by 52%; • Overall recovery rate increased by 37% compared to traditional treatment, and maintenance time was prolonged by 2.1 times.

[0060] Example 5: Extended application scheme for emotional disorders This example extends the system's functions to the treatment of depression and anxiety disorders.

[0061] 5.1 Depression specialization scheme According to the neurophysiological characteristics of patients with depression, the system makes the following adjustments: • taVNS parameters: low frequency (10-20 Hz), medium intensity (0.6-1.3 mA); • Heart-brain coupling target: focus on improving B→H coupling (target value > 0.45); • Meditation focus: Loving-kindness meditation; • Executive control assessment: add emotional Stroop task and reward delay discount task; • Physiological indicators: focus on monitoring Frontal Alpha Asymmetry.

[0062] 5.2 Anxiety disorder specialization scheme For patients with anxiety disorders, the system makes the following optimizations: • taVNS parameters: medium-high frequency (25-35 Hz), low intensity (0.2-0.7 mA); • Heart-brain coupling target: improve both H→B and B→H (both targets > 0.40); • Meditation focus: Body Scan and Breathing Space; • Executive control assessment: emotional attention regulation task and fear extinction assessment; • Physiological indicators: focus on monitoring respiration-heart rate synchrony and skin electrical activity.

[0063] 5.3 System self-learning mechanism In view of the variability of emotional disorders, this scheme increases the system's self-learning ability: • Intervention parameter optimization algorithm based on reinforcement learning; • Personalized emotion recognition model, recognizing emotional states through EEG and HRV patterns; • Dynamic intervention strategy adjustment based on emotional states; • Different parameter schemes for daytime and nighttime, adapting to emotional circadian fluctuation patterns.

[0064] Implementation effects In the clinical application of 40 patients with emotional disorders (20 patients with depression and 20 patients with anxiety disorders), the following results were achieved: • Depression group: HAMD scale score decreased by an average of 52%, which was 18% higher than that of traditional antidepressant monotherapy; • Anxiety disorder group: HAMA scale score decreased by an average of 48%, and SDNN of heart rate variability increased by 43%; • Emotional regulation ability (emotional Stroop interference effect) of both groups improved by an average of 56%; • Sleep quality: PSQI score decreased by 5.2 points, and rapid eye movement sleep increased by 15%; • Treatment compliance: 12-week completion rate reached 95%, which was significantly higher than that of the drug treatment group (76%).

[0065] The closed-loop vagus nerve intervention control device for the combined meditation therapy, wherein the device comprises: A sensor unit comprising a wireless EEG acquisition device and a wearable electrocardiogram monitoring device; A signal processing unit comprising signal amplification, filtering, denoising, and feature extraction modules; An embedded controller for running heart-brain coupling analysis algorithms and PID control algorithms; Stimulation electrodes arranged at auricular, pinna, or cervical vagus nerve stimulation sites; A human-computer interaction terminal for presenting meditation guidance interfaces and real-time feedback information; A processor configured to execute computer executable instructions; A memory storing one or more computer executable instructions, which, when executed by the processor, implement the steps of the closed-loop vagus nerve intervention control method for the combined meditation therapy described above.

[0066] In practical application, the device further comprises a wireless communication module for data exchange with a mobile terminal or a cloud service platform to realize remote monitoring and treatment scheme adjustment; and a self-learning module is arranged in the embedded controller, which analyzes historical physiological response data of the patient, optimizes heart-brain coupling model parameters and stimulation parameter control strategies, mines time-varying patterns of historical multi-dimensional physiological response data of the patient through a cross-period state migration learning mechanism, dynamically reconstructs topological parameters of the heart-brain coupling model, and forms an incremental personalized neural regulation strategy library based on online optimization of stimulation parameter control strategies through a reinforcement exploration-utilization strategy.

[0067] The processor for closed-loop vagus nerve intervention control in combination with meditation therapy, wherein the processor is configured to execute computer executable instructions, and the computer executable instructions, when executed by the processor, implement the steps of the closed-loop vagus nerve intervention control method in combination with meditation therapy described above. In practical application, the processor is configured to perform the following operations: Receive and process EEG and ECG data from the physiological signal acquisition module; Calculate the heart-brain coupling coefficient based on the synthetic data generation model; Perform adaptive PID algorithm to dynamically adjust taVNS stimulation parameters; Generate personalized meditation guidance content according to real-time physiological state; Optimize intervention strategies through a closed-loop feedback mechanism.

[0068] The processor for closed-loop vagus nerve intervention control in combination with meditation therapy, wherein a computer program is stored thereon, and the computer program can be executed by the processor to implement the steps of the closed-loop vagus nerve intervention control method in combination with meditation therapy described above.

[0069] Any process or method descriptions or any other information described herein in the flowchart form can be understood as representing at least one of the steps of an associated method, a portion of a process, or a portion of a method, and that the respective steps consist of at least one of the described steps or sub-steps. The scope of methods of the preferred embodiments of the present application encompass also other methods which consist of a combination of at least two of the described steps or sub-steps.

[0070] It should be understood that parts of the present application can be realized by hardware, software, firmware or a combination thereof. In the above-described embodiments, a plurality of steps or methods can be realized by software or firmware stored in a memory and executed by a suitable instruction execution apparatus.

[0071] Those skilled in the art of the present technology can understand that all or part of the steps carried out by the above-mentioned embodiment method can be completed by a program instructing related hardware, and the program can be stored in a computer readable storage medium. When the program is executed, it includes one of the steps of the method embodiment or a combination thereof.

[0072] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0073] In the description of the present specification, the description of the terms "an embodiment", "some embodiments", "an example", "a specific example" or "embodiments" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily mean the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0074] Although the embodiments of the present application have been shown and described above, it is understood that the above-mentioned embodiments are exemplary and cannot be understood as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above-mentioned embodiments within the scope of the present application.

[0075] The closed-loop vagus nerve intervention control system, method, device, processor and computer readable storage medium thereof using the combined meditation treatment of the present application can dynamically adjust the vagus nerve stimulation parameters according to the real-time physiological indicators of the patient, and form an effective closed-loop feedback mechanism combined with personalized meditation guidance, realize precise and personalized treatment of addictive disorders, significantly improve executive control function, reduce relapse risk, and provide a new technical solution for brain-heart-body integrated intervention.

[0076] In this specification, the present application has been described with reference to its specific embodiments. However, it is obvious that various modifications and changes can be made without departing from the spirit and scope of the present application. Therefore, the specification and drawings should be considered as illustrative rather than limiting.

Claims

1. A closed-loop vagus nerve intervention control system combined with meditation therapy, characterized in that, The system includes: The physiological signal acquisition module is used to acquire the patient's electroencephalogram (EEG) and electrocardiogram (ECG) signals in real time. The data processing and analysis unit is connected to the physiological signal acquisition module. Based on the acquired signal data, it calculates the bidirectional coupling coefficients between the heart and brain and between the brain and heart, and evaluates the patient's executive control function indicators in real time. The stimulation parameter control module, connected to the data processing and analysis unit, is used to dynamically adjust the frequency, pulse width, and current intensity of transauricular vagus nerve stimulation (taVNS) based on the calculated bidirectional coupling coefficient. The meditation guidance module, connected to the data processing and analysis unit, is used to provide personalized meditation guidance based on real-time mental and brain states. A feedback control module, connected to the stimulus parameter adjustment module and the meditation guidance module, is used to achieve synergistic optimization of stimulus parameters and meditation intervention through a closed-loop adaptive mechanism; and The execution control assessment module, connected to the feedback control module, improves the system's risk decision-making ability for combined meditation therapy by setting multiple task assessment methods.

2. The closed-loop vagus nerve intervention control system for combined meditation therapy according to claim 1, characterized in that, The physiological signal acquisition module uses a 128-lead high-density EEG cap and standard II-lead ECG electrodes to form a sensor unit, and acquires the EEG and ECG signals according to the following configuration information: EEG signal: sampling rate 1000Hz, filtering bandwidth 0.1-100Hz, focusing on acquiring the energy of the prefrontal and parietal lobes in the Gamma band of 30-100Hz; ECG signal: sampling rate 500Hz, filtering bandwidth 0.5-40Hz, extracting the low-frequency component LF and high-frequency component HF of heart rate variability to calculate the RR interval; The collected data is then transmitted in real time to the data processing and analysis unit via a wireless Bluetooth module.

3. The closed-loop vagus nerve intervention control system for combined meditation therapy according to claim 1, characterized in that, The bidirectional coupling coefficient is obtained according to the following calculation model: Heart-brain coupling coefficient: ,in, The predicted brainwave signal energy in the 30-100Hz Gamma band, This represents the ratio of low-frequency to high-frequency components of heart rate variability. These are the weighting coefficients. For time delay, This is the error term; Brain-heart coupling coefficient ,in, To predict heart rate variability, EEGf represents the phase information of the EEG signal in the 8-12Hz Alpha band, bf represents the frequency-related parameter, and c represents a constant term.

4. The closed-loop vagus nerve intervention control system for combined meditation therapy according to claim 3, characterized in that, The stimulation parameter control module uses an adaptive PID algorithm to dynamically adjust the stimulation parameters. ； in, For real-time current intensity, Based on the base current value, =0.3, =0.1, =0.05 is the initial PID parameter. The deviation between the cardio-brain coupling coefficient and the target value is considered when the cardio-brain coupling coefficient falls below a preset threshold. <0, the system increases the transauricular vagus nerve stimulation (taVNS) current intensity; when the cardio-brain coupling coefficient recovers, i.e. >0, the system reduces the transauricular vagus nerve stimulation (taVNS) current intensity.

5. The closed-loop vagus nerve intervention control system for combined meditation therapy according to claim 4, characterized in that, The meditation guidance module is based on EEG. The rhythmic breathing synchronization algorithm adjusts the meditation breathing frequency to match the... The phase-harmonic double-locked relationship of the dominant frequency is processed as follows: (a) Perform dynamic fundamental frequency extraction and dynamically calculate individualized frequencies in the following manner. Fundamental frequency; (a1) The raw EEG data was preprocessed and the EEG signals of the prefrontal and parietal regions were extracted. The α band was extracted using an 8-12Hz bandpass filter and the power frequency notch was processed to eliminate power interference. (a2) Instantaneous frequency calculation: Set a sliding window and calculate the mode of the instantaneous frequencies within the window; (a3) Individualized baseline calibration: Baseline recording was performed for 5 minutes before each stimulation session, and the resting-state α peak frequency f_baseline was calculated as the baseline. Reference frequency; (b) Design a dynamic harmonic order selection mechanism: ,in, The target respiratory rate is the ideal respiratory rate that the system guides the patient to achieve. The dominant frequency of the α wave, i.e., the individualized frequency extracted above. fundamental frequency, For real-time respiratory phase and Peak phase difference, The duration T of the current respiratory cycle, and the harmonic order. =1, , ,according to The harmonic order is dynamically selected based on the frequency band coherence coefficient. ; (c) Respiratory closed-loop feedback synchronous guidance control: The respiratory rate is locked to a specific value through auditory guidance. And adjust the meditation module settings and guidance content according to the state of mind-brain coupling; (c1) Auditory guidance generation and synchronization: A professional psychotherapist conducts meditative speech intervention to guide the adjustment of breathing rhythm; (c2) Set the meditation guidance level corresponding to the meditation guidance module according to the heart-brain coupling coefficient; (d) Multisensory feedback mechanisms: including visual, auditory and / or tactile signals, to enhance the patient’s perception of their own physiological state.

6. A closed-loop vagus nerve intervention and control method for combined meditation therapy using the system described in any one of claims 1 to 5, characterized in that, The method includes the following steps: (1) Collect the patient's EEG and ECG signals at baseline and calculate the initial cardio-brain coupling coefficient; (2) Based on the heart-brain coupling coefficient-addictive behavior state mapping model, the initial parameters of transauricular vagus nerve stimulation (taVNS) are set according to the calculated initial coupling coefficient. (3) Guide the patient into their... A meditative state synchronized with wave rhythm; (4) Monitor changes in the heart-brain coupling coefficient in real time and dynamically adjust the transauricular vagus nerve stimulation (taVNS) parameters and meditation guidance content; (5) Regularly assess the patient’s executive control function indicators to verify the intervention effect.

7. The closed-loop vagus nerve intervention and control method for achieving combined meditation therapy according to claim 6, characterized in that, The method also includes using differentiated parameter settings for patients with different levels of addiction severity in the following manner: For patients in the acute withdrawal period, use higher frequency (25-40Hz), lower intensity (0.1-0.8mA) stimulation parameters and simple guidance on focused breathing meditation; For patients in the recovery period, use stimulation parameters of medium frequency (15-25Hz) and medium intensity (0.5-1.2mA) and advanced body scan meditation guidance; For patients in the maintenance phase, use lower frequency (5-15Hz), higher intensity (0.8-1.5mA) stimulation parameters and higher-order awareness meditation guidance.

8. A closed-loop vagus nerve intervention control device for combined meditation therapy, characterized in that, The device includes: The sensor unit includes a wireless EEG acquisition device and a wearable ECG monitoring device; The signal processing unit includes signal amplification, filtering, denoising, and feature extraction modules; Embedded controller for running heart-brain coupling analysis algorithm and PID control algorithm; Stimulation electrodes are placed at the vagus nerve stimulation sites in the concha, auricle, or neck. Human-computer interaction terminal, used to present meditation guidance interface and real-time feedback information; A processor is configured to execute computer-executable instructions; The memory stores one or more computer-executable instructions, which, when executed by the processor, implement the steps of the closed-loop vagus nerve intervention control method of combined meditation therapy as described in any one of claims 6 to 7.

9. A closed-loop vagus nerve intervention control processor for combined meditation therapy, characterized in that, The processor is configured to execute computer-executable instructions, which, when executed by the processor, implement the steps of the closed-loop vagus nerve intervention control method of combined meditation therapy as described in any one of claims 6 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that can be executed by a processor to implement the steps of the closed-loop vagus nerve intervention control method of combined meditation therapy as described in any one of claims 6 to 7.

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