An electromyography and impedance dual-mode feedback-based electric stimulation adaptive adjustment method

Abstract

The application discloses a kind of based on myoelectricity and impedance bimodal feedback's electric stimulation self-adapting regulation method, it is related to biomedical signal processing and intelligent medical equipment technical field, the system includes central control unit, myoelectric signal processing module, myoelectric probe matrix, communication interface and power interface, method includes: synchronous acquisition myoelectric signal and tissue impedance signal and pre-processing;Extract myoelectric feature and impedance feature;Based on characteristic calculation stimulation efficiency index K;Through self-learning process, establish individualized stimulation model;According to K value, real-time adjustment electric stimulation current, realizes closed-loop control.The application converts subjective feeling into objective physiological index, realizes accurate, individualized, self-adapting electric stimulation regulation, improves treatment effect, safety and user experience, applicable to electric acupuncture, neuromuscular rehabilitation and pain management equipment etc..

Description

Technical Field

[0001] This invention relates to the field of biomedical signal processing and intelligent medical device technology, specifically to an adaptive adjustment method for electrical stimulation based on dual-modal feedback of electromyography and impedance. Background Technology

[0002] Electrical stimulation therapy (such as electroacupuncture and transcutaneous electrical nerve stimulation) is a common physical therapy method. In existing technologies, the adjustment of stimulation intensity largely relies on subjective user feedback (such as sensations of numbness, distension, or heaviness) or the operator's experience. This open-loop control method has significant drawbacks:

[0003] 1. Subjectivity and imprecision: The tolerance and perceptual sensitivity of different individuals vary greatly, and adjustments based on subjective feedback cannot achieve precise and reproducible dose control.

[0004] 2. Lack of real-time adaptability: During treatment, the body's physiological state (such as distraction and changes in skin resistance due to sweating) will change dynamically, and a fixed stimulation intensity may not be able to maintain the best therapeutic effect.

[0005] 3. Safety risks exist: For patients with limited expressive abilities (such as children, the elderly, or comatose patients), the operator cannot obtain effective feedback, which can easily lead to overstimulation or understimulation.

[0006] 4. Poor user experience: Users need to constantly adjust the intensity manually, which is cumbersome and provides a poor experience.

[0007] Therefore, there is an urgent need for a technical solution that can achieve adaptive adjustment of electrical stimulation based on objective physiological signals in order to improve the accuracy, safety and comfort of treatment. Summary of the Invention

[0008] (a) Technical problems to be solved

[0009] To overcome the shortcomings of existing technologies, we propose an adaptive adjustment method for electrical stimulation based on dual-modal feedback of electromyography and impedance, which can solve the problems of reliance on subjective feedback, low control accuracy, and poor safety in existing technologies.

[0010] (II) Technical Solution

[0011] The present invention is achieved through the following technical solution: The present invention proposes an adaptive adjustment method for electrical stimulation based on dual-modal feedback of electromyography and impedance, including a central control unit, an electromyography signal processing module, an electromyography probe matrix, a communication interface for data interaction with external devices, and a power supply interface for powering the system. The system includes an electromyography signal channel and an impedance measurement channel. The electromyography probe matrix includes at least two pairs of electrodes capable of applying electrical stimulation and acquiring bioelectrical signals.

[0012] Furthermore, the electromyography signal channel is capable of acquiring surface electromyography signals at the microvolt level, and includes a high-pass filter, a low-pass filter, and a programmable gain amplifier. The impedance measurement channel is capable of calculating tissue impedance by injecting a safe alternating current and measuring the voltage.

[0013] Furthermore, the electromyography probe matrix is ​​arranged in a matrix or a specific geometric shape, and the electrodes are coupled to the skin via a conductive gel.

[0014] The modulation method of an adaptive electrical stimulation system based on dual-modal feedback of electromyography and impedance includes the following steps:

[0015] Step 1: Simultaneously acquire electromyographic signals and tissue impedance signals, and perform preprocessing;

[0016] Step 2: Extract electromyographic and impedance features from the preprocessed signals;

[0017] Step 3: Calculate the stimulus efficacy index K based on the extracted features;

[0018] Step 4: Establish a personalized stimulus model through a self-learning process;

[0019] Step 5: Adjust the electrical stimulation current in real time according to the stimulation efficacy index K to achieve closed-loop control.

[0020] Furthermore, the electromyographic characteristics include integrated electromyographic value, root mean square value, and median frequency, and the impedance characteristics include fundamental frequency impedance and impedance change rate.

[0021] Furthermore, the formula for calculating the stimulus efficacy index K is as follows:

[0022] K=(a*iEMG+b*RMS)*(1+c*|ΔZ|);

[0023] Where iEMG is the integrated electromyography value, RMS is the root mean square value of electromyography, |ΔZ| is the absolute value of impedance change, and a, b, and c are weighting coefficients.

[0024] Furthermore, the self-learning process includes controlling the electrical stimulation current to increase stepwise from zero; calculating the corresponding stimulation efficacy index K at each current step; performing curve fitting based on the data of current and K values ​​to establish a relationship model; and determining the optimal stimulation current range according to the model.

[0025] Furthermore, the closed-loop control includes setting a target K value range; calculating the current K value in real time; increasing the stimulation current if the K value is lower than the target range, and decreasing the stimulation current if the K value is higher than the target range.

[0026] (III) Beneficial Effects

[0027] Compared with the prior art, the present invention has the following advantages:

[0028] 1. It achieves objectification and quantification: The traditionally subjective feeling of "deqi" is transformed into a measurable "stimulation efficacy index" K, giving electrical stimulation therapy an objective dosage standard and evaluation basis.

[0029] 2. Significantly improved control precision and therapeutic effect: Through dual-modal feedback of electromyography and impedance, the system can perceive the stimulation effect more comprehensively from both the levels of neural activation and tissue response, thereby making more precise adjustments to ensure that the stimulation is always within the optimal therapeutic window.

[0030] 3. High degree of personalization and adaptability: The built-in self-learning algorithm can establish a unique stimulation model for each user and each acupoint, truly realizing "treatment according to the individual and treatment according to the acupoint", and solving the problem of unstable efficacy of general equipment.

[0031] 4. Significantly enhanced safety and user experience: Closed-loop control effectively prevents discomfort or risks caused by excessive stimulation, especially suitable for users who cannot provide active feedback. Automated adjustment also eliminates the hassle of frequent manual operation, making the experience more convenient and comfortable.

[0032] 5. Supports telemedicine and data analysis: All objective data can be recorded and uploaded, providing a valuable data foundation for doctors to remotely monitor treatment effectiveness, adjust treatment plans, and conduct large-scale clinical research. Attached Figure Description

[0033] Figure 1 This is a hardware logic diagram of the system of the present invention.

[0034] Figure 2 This is a schematic diagram of a specific embodiment of the electromyography probe matrix in the system of the present invention.

[0035] Figure 3 This is a schematic diagram of the method steps of the present invention. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0037] like Figure 1 The illustrated adaptive modulation system for electrical stimulation based on dual-modal feedback of electromyography and impedance specifically includes:

[0038] 1) Central Control Unit: As the core of edge computing, it is built into the MCU (Microcontroller Unit) and is responsible for signal processing, algorithm operation and control scheduling of the entire system. Its built-in MCU or dedicated DSP (Digital Signal Processing) is responsible for running the core algorithm described later and controlling signal acquisition and output.

[0039] 2) Electromyography signal processing module: Signal conditioning and acquisition circuit, connected to the electrodes, including electromyography signal channels: including a high-pass filter (to filter out motion artifacts), a low-pass filter (to filter out high-frequency noise), and a programmable gain amplifier, used to acquire surface electromyography signals at the microvolt level.

[0040] Impedance measurement channel: Employing bioelectrical impedance analysis technology, it calculates the impedance value of local tissue by injecting a weak, safe alternating current at a specific frequency (e.g., 50kHz) and measuring the resulting voltage. Data storage module: Used to store the collected raw data and processed stimulus intensity values.

[0041] 3) Electromyographic probe matrix: comprising at least two pairs of electrodes, preferably arranged in a matrix or specific geometry. These electrodes are used for both applying therapeutic electrical pulses and acquiring bioelectrical signals. The electrodes are coupled to the skin via a conductive gel. The arrangement of the electromyographic probe matrix (taking two sets of four probes as an example) is shown in the attached figure. Figure 2 As shown, each pair of opposite probes forms a current loop.

[0042] 4) Communication interface: such as I2C, SPI or UART, used for data interaction with the main controller (such as the MCU of the acupuncture patch); power interface: accepts power according to the power supply parameter protocol.

[0043] like Figure 3 As shown, the adjustment method of the electrical stimulation adaptive adjustment system based on dual-modal feedback of electromyography and impedance is as follows:

[0044] Synchronous signal acquisition and preprocessing

[0045] 1) During the intervals between electrical stimulation pulses (to avoid stimulation artifacts), or by using a signal at a different frequency than the treatment pulse, simultaneously acquire electromyographic signals and tissue impedance.

[0046] 2) Bandpass filtering (e.g., 20-500Hz) is applied to the electromyographic signal to remove noise, and the impedance signal is demodulated to obtain the real and imaginary parts (resistance and reactance).

[0047] Multi-dimensional feature extraction: Extracting key features (rather than simple voltage and current values) from the preprocessed signal.

[0048] From electromyographic signals:

[0049] 1) Integrated electromyography (EMG) value: The absolute value of the EMG signal is integrated within a specific time window to reflect the overall electrical activity level of the muscle.

[0050] 2) Root mean square value: reflects the effective amplitude of the electromyographic signal.

[0051] 3) Median frequency: The power spectrum is calculated by fast Fourier transform, reflecting the state of muscle fatigue.

[0052] From impedance signal:

[0053] 1) Fundamental frequency impedance: reflects the static characteristics of the tissue.

[0054] 2) Impedance change rate: The dynamic change before, during or after stimulation, reflecting changes in local blood flow, fluid accumulation, etc.

[0055] Stimulus efficacy index (stimulus intensity) calculation

[0056] K=(a*iEMG+b*RMS)*(1+c*|ΔZ|);

[0057] Where: K is the stimulation efficacy index, which is a quantitative value reflecting the effectiveness of stimulation; iEMG is the integrated electromyography value; and RMS is the root mean square value of electromyography.

[0058] |ΔZ|: The absolute value of the change in current impedance relative to the resting state.

[0059] a, b, and c are weighting coefficients that can be pre-calibrated through experiments or adjusted individually through a self-learning process.

[0060] The physiological explanation of this formula is as follows: (a*iEMG + b*RMS) represents the degree of neuromuscular excitation (an objective indicator of "deqi"), while (1+c*|ΔZ|) is a modulating factor that reflects the physiological changes in the tissue caused by stimulation (such as local congestion). Multiplying the two together can more comprehensively assess the overall effect of the stimulation.

[0061] Individualized model self-learning

[0062] 1) When the system is used on a user's acupoint for the first time, it enters "learning mode".

[0063] 2) Control the output of the treatment electrode to produce a step current I_i that starts from zero (zero current represents a resting state without stimulation) and increases slowly.

[0064] 3) After stabilization, the corresponding stimulus efficacy index K_i is calculated for each current step I_i.

[0065] 4) After collecting a set of data pairs (I_i, K_i), perform polynomial curve fitting to establish the IK relationship model for the user, that is, obtain the values ​​of a, b, and c in the aforementioned formula.

[0066] 5) Based on the fitted curve, set a target K value range (such as the value range near the inflection point of the fitted curve). The current range corresponding to this range is the personalized optimal stimulation intensity range for the user.

[0067] In closed-loop control, during treatment mode, the main controller uses the target K value as the setpoint (anchor point) and calculates the current K value in real time. If it is lower than the target range, the stimulation current is increased in a certain step; if it is higher than the target range, the current is decreased, thereby achieving closed-loop precise control based on objective physiological signals as feedback.

[0068] The system of this invention can be integrated into intelligent electroacupuncture devices. After the user wears the device, the system automatically performs personalized acupoint learning, monitors changes in electromyography and impedance in real time during the treatment process, and dynamically adjusts the stimulation intensity to keep the "deqi" sensation stable within the optimal range, thereby improving the treatment effect and comfort.

[0069] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the concept and scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the inventive concept should fall within the protection scope of the present invention. All technical contents for which protection is sought in this invention are fully described in the claims.

Claims

1. An adaptive adjustment system for electrical stimulation based on dual-modal feedback of electromyography and impedance, characterized in that, It includes a central control unit, an electromyography (EMG) signal processing module, an EMG probe matrix, a communication interface for data interaction with external devices, and a power interface for powering the system. It includes an EMG signal channel and an impedance measurement channel. The EMG probe matrix includes at least two pairs of electrodes capable of applying electrical stimulation and acquiring bioelectrical signals.

2. The adaptive adjustment system for electrical stimulation based on dual-modal feedback of electromyography and impedance according to claim 1, characterized in that: The electromyography signal channel is capable of acquiring surface electromyography signals at the microvolt level, and includes a high-pass filter, a low-pass filter, and a programmable gain amplifier. The impedance measurement channel is capable of calculating tissue impedance by injecting a safe alternating current and measuring the voltage.

3. The adaptive adjustment system for electrical stimulation based on dual-modal feedback of electromyography and impedance according to claim 1, characterized in that: The electromyography probe matrix is ​​arranged in a matrix or a specific geometric shape, and the electrodes are coupled to the skin through a conductive gel.

4. The adjustment method of the adaptive electrical stimulation system based on dual-modal feedback of electromyography and impedance according to any one of claims 1-3, characterized in that, Includes the following steps: Step 1: Simultaneously acquire electromyographic signals and tissue impedance signals, and perform preprocessing; Step 2: Extract electromyographic and impedance features from the preprocessed signals; Step 3: Calculate the stimulus efficacy index K based on the extracted features; Step 4: Establish a personalized stimulus model through a self-learning process; Step 5: Adjust the electrical stimulation current in real time according to the stimulation efficacy index K to achieve closed-loop control.

5. The adjustment method of the adaptive adjustment system for electrical stimulation based on dual-modal feedback of electromyography and impedance according to claim 4, characterized in that: The electromyographic characteristics include integrated electromyographic value, root mean square value, and median frequency, and the impedance characteristics include fundamental frequency impedance and impedance change rate.

6. The adjustment method of the adaptive adjustment system for electrical stimulation based on dual-modal feedback of electromyography and impedance according to claim 4, characterized in that... The formula for calculating the stimulus efficacy index K is as follows: K=(a*iEMG+b*RMS)*(1+c*|ΔZ|); Where iEMG is the integrated electromyography value, RMS is the root mean square value of electromyography, |ΔZ| is the absolute value of impedance change, and a, b, and c are weighting coefficients.

7. The adjustment method of the adaptive adjustment system for electrical stimulation based on dual-modal feedback of electromyography and impedance according to claim 4, characterized in that: The self-learning process includes controlling the electrical stimulation current to increase stepwise from zero; calculating the corresponding stimulation efficacy index K at each current step; performing curve fitting based on the data of current and K values ​​to establish a relationship model; and determining the optimal stimulation current range based on the model.

8. The adjustment method of the adaptive adjustment system for electrical stimulation based on dual-modal feedback of electromyography and impedance according to claim 4, characterized in that: The closed-loop control includes setting a target K value range; calculating the current K value in real time; increasing the stimulation current if the K value is lower than the target range, and decreasing the stimulation current if the K value is higher than the target range.

Images