Closed-loop electro-acupuncture therapeutic apparatus based on cardio-cerebral coupling information feedback

Abstract

The invention relates to the technical field of intelligent medical instruments, and discloses a closed-loop electro-acupuncture therapeutic apparatus based on heart and brain coupling information feedback. The device comprises a forehead electroencephalogram signal acquisition module, a single-lead electrocardio acquisition module, a Bluetooth transmission module, a signal preprocessing module, an ECG and EEG feature extraction module, a dynamic coupling analysis module, an embedded XGBoost classifier and an electroacupuncture control module. According to the system, collected EEG and ECG signals are wirelessly transmitted through Bluetooth, HRV indexes and EEG frequency band power spectral density are extracted after preprocessing, and frequency domain coherence analysis is carried out to obtain heart and brain bidirectional coupling characteristics. The embedded XGBoost classifier outputs optimal electroacupuncture stimulation parameters based on the characteristics, and the electroacupuncture control module generates corresponding bidirectional pulse waves for stimulation. According to the therapeutic apparatus, closed-loop feedback control is achieved, therapeutic parameters can be dynamically optimized, the individuation and precision level is improved, and meanwhile safety is ensured through impedance monitoring and electrical isolation.

Description

Technical Field

[0001] This invention relates to the field of intelligent medical device technology, and discloses a closed-loop electroacupuncture therapy device based on heart-brain coupling information feedback. Background Technology

[0002] Electroacupuncture, as an important non-pharmacological treatment for insomnia, is widely used in clinical practice. However, current electroacupuncture equipment and methods have shortcomings: 1. Subjectivity in parameter selection: The selection of stimulation frequency of electroacupuncture device mainly relies on the doctor's clinical experience and lacks objective physiological indicators, making it difficult to standardize and optimize treatment plans; 2. Poor individual adaptability: The fixed frequency stimulation pattern cannot be dynamically adjusted according to the differences in autonomic nerve function among individual insomnia patients, which affects the treatment effect; 3. Lack of real-time feedback mechanism: Existing equipment generally lacks the ability to monitor the patient's physiological state in real time during treatment and adjust treatment parameters accordingly, which prevents the formation of a closed-loop optimization and limits further improvement in efficacy.

[0003] Therefore, there is an urgent need to develop an electroacupuncture device that can dynamically optimize treatment parameters based on real-time feedback of objective physiological signals in order to improve the personalization and precision of insomnia treatment. Summary of the Invention

[0004] To address the aforementioned technical shortcomings, the purpose of this invention is to provide a closed-loop electroacupuncture therapy device based on heart-brain coupling information feedback, which solves the problems of subjective parameter selection, poor individual adaptability, and lack of real-time feedback parameter adjustment capabilities in existing electroacupuncture therapy devices.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: In a first aspect, the present invention provides a closed-loop electroacupuncture therapy device based on cardio-brain coupling information feedback, comprising: The frontal EEG signal acquisition module is used to acquire the patient's frontal EEG signals; A single-lead ECG acquisition module is used to acquire the patient's ECG signal; Bluetooth transmission module, based on BLE 5.0 protocol, wirelessly transmits EEG and ECG signals; EEG signal receiving module, used to receive raw EEG signals transmitted via Bluetooth; A single-lead ECG receiver module is used to receive raw ECG signals transmitted via Bluetooth; The signal preprocessing module preprocesses the ECG signal and the EEG signal respectively; Specifically, the ECG signal was sequentially bandpass filtered from 0.5 to 40 Hz to eliminate baseline drift and electromyography interference, and the Pan-Tompkins algorithm was used to locate the R wave peak. The EEG signal was bandpass filtered from 0.5 to 40 Hz and downsampled to 250 Hz. Based on independent component analysis, eye movement, electromyography and ECG artifacts were automatically identified and removed. The ECG feature extraction module extracts the RR interval sequence based on the preprocessed ECG signal and calculates the heart rate variability index sequence, including low-frequency power, high-frequency power and the ratio of low to high-frequency power. The EEG feature extraction module extracts features based on preprocessed EEG signals. , , , , The power spectral density sequence of the frequency band; The dynamic coupling analysis module is used to perform frequency domain coherence analysis on the heart rate variability index sequence and the power spectral density sequence to extract the bidirectional coupling characteristics between the heart and brain. An embedded XGBoost classifier receives the heart-brain bidirectional coupling features as input and outputs electroacupuncture stimulation parameters, including stimulation frequency, intensity, and waveform. The electroacupuncture control module generates bidirectional pulse waves based on the electroacupuncture stimulation parameters and outputs control to the electroacupuncture stimulation electrodes. The output stimulation current is adjustable in the range of 0.1–10mA.

[0006] Preferably, in one possible implementation of the first aspect, the signal preprocessing module employs a second-order Butterworth filter for bandpass filtering.

[0007] Preferably, in one possible implementation of the first aspect, the frequency domain coherence analysis employs multivariate Granger causality analysis to obtain the information flow between heart and brain signals, and constructs a VAR model containing heart and brain signals.

[0008] Preferably, in one possible implementation of the first aspect, the multivariate Granger causality analysis quantifies the nonlinear coupling strength through mutual information:

[0009] in, This represents the mutual information value between electroencephalogram (EEG) and electrocardiogram (ECG) signals, i.e., the bidirectional Granger causality value. This indicates the first step after discretization of the EEG signal. a state, This represents the first step after discretization of the electrocardiogram signal. a state, Indicates the state of brain signals. And the ECG signal is simultaneously in a state The joint probability, Indicates the state of brain signals. The marginal probability, This indicates that the electrocardiogram signal is in a certain state. The marginal probability.

[0010] Preferably, in one possible implementation of the first aspect, the input features of the embedded XGBoost classifier are 30-dimensional, including low-frequency power, high-frequency power, and the ratio of low to high-frequency power, respectively, and... , , , , The two-way Granger causality value between power spectral densities.

[0011] Preferably, in one possible implementation of the first aspect, the duty cycle of the bidirectional pulse wave output by the electroacupuncture control module is adjustable, with an adjustment range of 10%–50%.

[0012] Preferably, in one possible implementation of the first aspect, the stimulation output terminal of the electroacupuncture control module adopts an optocoupler isolation design, and its isolation voltage is not less than 4000 Vrms.

[0013] Preferably, in one possible implementation of the first aspect, the electroacupuncture control module further includes: The stimulation intensity adjustment knob is used to manually adjust the output current intensity within the range of 0.1–10mA; The dynamic impedance detection circuit monitors the impedance of the needle output circuit in real time, with an impedance monitoring range of 200–3000Ω. When the impedance exceeds this range, the shutdown protection mechanism is automatically triggered.

[0014] The beneficial effects of this invention are as follows: by collecting and analyzing the user's electrocardiogram and electroencephalogram signals in real time, extracting heart rate variability indicators and EEG power spectral density sequences, and using dynamic coupling analysis and embedded machine learning models, the device realizes the automatic optimization selection of electroacupuncture stimulation parameters, overcoming the problems of subjective parameter selection, poor individual adaptability and lack of real-time feedback mechanism in the prior art.

[0015] In addition, the device integrates multiple safety protection mechanisms, such as a dynamic impedance detection circuit that monitors electrode contact impedance in real time and automatically shuts down in case of abnormality, and an optocoupler isolation design at the stimulation output end with an isolation voltage of not less than 4000Vrms, which meets medical safety standards and ensures the safety of the treatment process.

[0016] In summary, through heart-brain coupling analysis, this therapeutic device can dynamically optimize treatment parameters, improve personalization and precision, and ensure safety through impedance monitoring and electrical isolation. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This application provides a structural diagram of a closed-loop electroacupuncture therapy device based on heart-brain coupling information feedback. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1: As Figure 1 As shown, the present invention provides a closed-loop electroacupuncture therapy device based on heart-brain coupling information feedback, including a frontal EEG signal acquisition module, a single-lead ECG acquisition module, a Bluetooth transmission module, an EEG signal receiving module, a single-lead ECG receiving module, a signal preprocessing module, an ECG feature extraction module, an EEG feature extraction module, a dynamic coupling analysis module, an embedded XGBoost classifier, and an electroacupuncture control module.

[0021] The frontal EEG signal acquisition module collects the patient's frontal EEG signal, while the single-lead ECG acquisition module collects the user's raw ECG signal through a three-lead electrode patch.

[0022] The Bluetooth transmission module, based on the Bluetooth 5.0 protocol, wirelessly transmits the collected raw EEG and ECG signals to the host for further processing.

[0023] The EEG signal receiving module is used to receive EEG signals sent by the EEG acquisition device via Bluetooth.

[0024] The single-lead ECG receiver module is used to receive the raw ECG signal transmitted via Bluetooth from the ECG acquisition device.

[0025] The signal preprocessing module performs the following preprocessing steps on the ECG and EEG signals respectively: First, resting ECG signals were continuously acquired using a sampling frequency of 250 Hz. Potential interference at the beginning and end of the signal was eliminated by removing the first and last 30 seconds. Then, a 0.5–40 Hz bandpass filter was applied to eliminate baseline drift and EMG interference. Automatic R-wave detection was then performed, and the Pan-Tompkins algorithm was used to locate the R-wave peak, generating a corrected Interbeat Interval (IBI) time series. Next, the IBI series was converted from "heartbeat time" to "real time," generating a vector with one IBI value per millisecond for easy alignment with the EEG time. Finally, autoregressive modeling was performed to calculate the power spectrum.

[0026] The peak HF-HRV frequency for each participant in the range of 0.15–0.4 Hz was identified. A sinusoidal oscillation signal in the vicinity of this frequency (±0.05 Hz) was extracted using an FIR filter. Finally, a Hilbert transform was performed on the filtered HF-HRV signal to obtain the phase-time series.

[0027] For EEG signal processing, after bandpass filtering of 0.5–40 Hz and downsampling to 250 Hz, artifacts from eye movement, electromyography, and electrocardiography were automatically identified and removed based on independent component analysis. The original data structure was preserved, and the data was reconstructed after removing only artifact components. The average signal from the frontal electrodes (Fp1, Fp2) was selected. Five frequency bands were filtered out: Delta (… : 1–4Hz, Theta ( : 4–8Hz, Alpha ( : 8–12Hz, Beta ( : 12–30Hz, Gamma ( (30–50 Hz): Using an FIR filter, the order is set to capture 3 cycles of the lower limit frequency of each band. A Hilbert transform is performed on each filtered EEG signal to extract the amplitude envelope time series.

[0028] The ECG feature extraction module performs R-wave peak detection on the preprocessed high-quality ECG signal and extracts a continuous RR interval sequence. Based on this RR interval sequence, a heart rate variability index sequence is calculated, and the low-frequency power (LF) (0.04-0.15Hz), high-frequency power (HF) (0.15-0.4Hz), and the low- and high-frequency power (LF / HF) ratios are calculated using the Welch method (Hanning window, 50% overlap).

[0029] The EEG feature extraction module calculates the time-frequency energy of each frequency band using Morlet wavelet transform. Morlet wavelet transform is a time-frequency analysis method based on wavelet theory. By convolving the signal with a series of wavelet functions at different scales and positions, it can analyze the signal features simultaneously in the time and frequency domains. In this embodiment, Morlet wavelet transform extracts the signal's features in each frequency band (e.g., ...). Wave 1-4Hz Wavelength 4-8Hz Wave 8-13Hz, Wave 13-30Hz and The time-frequency energy of waves (30-45Hz).

[0030] The dynamic coupling analysis module performs frequency domain coherence analysis on the heart rate variability index sequence and the power spectral density sequence to extract the bidirectional coupling characteristics between the heart and brain.

[0031] Frequency domain coherence analysis employs multivariate Granger causality analysis (MVGC) to explore the information flow between heart and brain signals. MVGC is a statistical method based on vector autoregression (VAR) models, effectively analyzing causal relationships in multivariate time series data. By constructing a VAR model that includes heart and brain signals, MVGC assesses the causal effects between different signals while considering conditional variables to avoid spurious causality, thus providing a more comprehensive understanding of the dynamic characteristics of heart-brain interaction.

[0032] This embodiment uses mutual information to quantify the nonlinear coupling strength:

[0033] in, This represents the mutual information value between electroencephalogram (EEG) and electrocardiogram (ECG) signals, i.e., the bidirectional Granger causality value. This indicates the first step after discretization of the EEG signal. a state, This represents the first step after discretization of the electrocardiogram signal. a state, Indicates the state of brain signals. And the ECG signal is simultaneously in a state The joint probability, Indicates the state of brain signals. The marginal probability, This indicates that the electrocardiogram signal is in a certain state. The marginal probability.

[0034] The embedded XGBoost classifier is a pre-trained gradient boosting decision tree model deployed on the embedded hardware of the therapeutic device. Its input features are 30-dimensional, including low-frequency power, high-frequency power, and the ratio of low to high-frequency power, respectively. , , , , The model establishes a bidirectional Granger causality relationship between power spectral density and electroacupuncture stimulation. Based on the input feature vector, it predicts the most suitable electroacupuncture stimulation parameters for the current user. The model was trained using a large dataset of ECG data from insomnia patients, establishing a correlation between the therapeutic effects of electroacupuncture at different frequencies.

[0035] The electroacupuncture control module receives frequency commands from the XGBoost classifier and generates bidirectional symmetrical pulse waves (pulse width 0.5ms) with corresponding parameters. The duty cycle of the bidirectional pulse waves is adjustable, ranging from 10% to 50%, and is output to specific acupoints on the user's body surface through electrodes. The stimulation current intensity can be manually adjusted from 0.1 to 10mA using a knob on the device to suit the sensitivity of different users.

[0036] The electrocautery control module also integrates safety features: Dynamic impedance detection circuit: Real-time monitoring of the human-electrode contact impedance of the electroacupuncture stimulation output circuit, with a monitoring range of 200-3000Ω. If the detected impedance value exceeds this range, an automatic shutdown will be triggered immediately to prevent ineffective stimulation or potential risks.

[0037] Electrical isolation: The stimulation output terminal adopts a high-performance optocoupler isolator to achieve electrical isolation between the stimulation circuit and the low-voltage control circuit of the equipment. The isolation voltage is not less than 4000Vrms, which complies with the IEC 60601-2-10:2012 safety standard for medical electrical equipment and ensures user safety.

[0038] The closed-loop electroacupuncture therapy device based on heart-brain coupling feedback constructs a complete closed-loop control system: signals are transmitted via Bluetooth to a signal processing module for noise reduction and enhancement; an EEG signal acquisition module receives EEG signals; a single-lead ECG acquisition module continuously or periodically acquires ECG signals; a signal preprocessing module preprocesses both ECG and EEG signals sequentially; an ECG feature extraction module extracts key indicators reflecting the autonomic nervous system state; an EEG feature extraction module extracts power information in the brainwave frequency band; a dynamic coupling analysis module performs frequency domain coherence analysis; an embedded XGBoost model predicts the optimal electroacupuncture frequency in real time based on heart rate variability indicators; and an electroacupuncture control module outputs stimulation at the corresponding frequency. After stimulation, the system can acquire ECGs again and analyze changes in heart rate variability indicators to evaluate the effect and potentially perform a new round of parameter adjustments. This closed-loop control mechanism based on physiological feedback is key to achieving personalized and precise electroacupuncture therapy.

[0039] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A closed-loop electroacupuncture therapy device based on heart-brain coupling information feedback, characterized in that, include: The frontal EEG signal acquisition module is used to acquire the patient's frontal EEG signals; A single-lead ECG acquisition module is used to acquire the patient's ECG signal; Bluetooth transmission module, based on BLE 5.0 protocol, wirelessly transmits EEG and ECG signals; EEG signal receiving module, used to receive raw EEG signals transmitted via Bluetooth; A single-lead ECG receiver module is used to receive raw ECG signals transmitted via Bluetooth; The signal preprocessing module preprocesses the ECG signal and the EEG signal respectively; Specifically, the ECG signal was sequentially bandpass filtered from 0.5 to 40 Hz to eliminate baseline drift and electromyography interference, and the Pan-Tompkins algorithm was used to locate the R wave peak. The EEG signal was bandpass filtered from 0.5 to 40 Hz and downsampled to 250 Hz. Based on independent component analysis, eye movement, electromyography and ECG artifacts were automatically identified and removed. The ECG feature extraction module extracts the RR interval sequence based on the preprocessed ECG signal and calculates the heart rate variability index sequence, including low-frequency power, high-frequency power and the ratio of low to high-frequency power. The EEG feature extraction module extracts features based on preprocessed EEG signals. , , , , The power spectral density sequence of the frequency band; The dynamic coupling analysis module is used to perform frequency domain coherence analysis on the heart rate variability index sequence and the power spectral density sequence to extract the bidirectional coupling characteristics between the heart and brain. An embedded XGBoost classifier receives the heart-brain bidirectional coupling features as input and outputs electroacupuncture stimulation parameters, including stimulation frequency, intensity, and waveform. The electroacupuncture control module generates bidirectional pulse waves based on the electroacupuncture stimulation parameters and outputs control to the electroacupuncture stimulation electrodes. The output stimulation current is adjustable in the range of 0.1–10mA.

2. The closed-loop electroacupuncture therapy device based on heart-brain coupling information feedback according to claim 1, characterized in that, The signal preprocessing module uses a second-order Butterworth filter for bandpass filtering.

3. The closed-loop electroacupuncture therapy device based on heart-brain coupling information feedback according to claim 1, characterized in that, The frequency domain coherence analysis employs multivariate Granger causality analysis to obtain the information flow between cardiac and cerebral signals, and constructs a VAR model that includes cardiac and cerebral signals.

4. A closed-loop electroacupuncture therapy device based on heart-brain coupling information feedback as described in claim 3, characterized in that, The multivariate Granger causality analysis quantifies the strength of nonlinear coupling through mutual information: in, This represents the mutual information value between electroencephalogram (EEG) and electrocardiogram (ECG) signals, i.e., the bidirectional Granger causality value. This indicates the first step after discretization of the EEG signal. a state, This represents the first step after discretization of the electrocardiogram signal. a state, Indicates the state of brain signals. And the ECG signal is simultaneously in a state The joint probability, Indicates the state of brain signals. The marginal probability, Indicates the state of the electrocardiogram signal. The marginal probability.

5. A closed-loop electroacupuncture therapy device based on heart-brain coupling information feedback as described in claim 1, characterized in that, The embedded XGBoost classifier has 30-dimensional input features, including low-frequency power, high-frequency power, and the ratio of low to high-frequency power, respectively. , , , , The two-way Granger causality value between power spectral densities.

6. The closed-loop electroacupuncture therapy device based on heart-brain coupling information feedback according to claim 1, characterized in that, The duty cycle of the bidirectional pulse wave output by the electroacupuncture control module is adjustable, with an adjustment range of 10%–50%.

7. A closed-loop electroacupuncture therapy device based on heart-brain coupling information feedback as described in claim 1, characterized in that, The stimulation output terminal of the electroacupuncture control module adopts an optocoupler isolation design, and its isolation voltage is not less than 4000 Vrms.

8. A closed-loop electroacupuncture therapy device based on heart-brain coupling information feedback as described in claim 1, characterized in that, The electroacupuncture control module also includes: The stimulation intensity adjustment knob is used to manually adjust the output current intensity within the range of 0.1–10mA; The dynamic impedance detection circuit monitors the impedance of the needle output circuit in real time, with an impedance monitoring range of 200–3000Ω. When the impedance exceeds this range, the shutdown protection mechanism is automatically triggered.

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