Low-fatigue multi-mode external diaphragm pacemaker based on electrocardiosignal feedback

Abstract

The invention discloses a low-fatigue multi-mode external diaphragm pacemaker based on electrocardiosignal feedback, and belongs to the field of respiratory function rehabilitation electrical stimulation treatment instruments. By integrating electrocardiosignal acquisition and electrical stimulation functions, high-frequency (kHz) current stimulation pulses can be output to reduce diaphragm fatigue, the physiological state of a patient is monitored in real time, stimulation parameters are dynamically adjusted according to information such as respiratory rate and heart rate, and a low-fatigue, personalized and low-cost respiratory assistance solution is provided. The method is expected to generate positive influence in the aspects of adjuvant therapy of respiratory failure, respiratory muscle function recovery training, rehabilitation management of chronic obstructive pulmonary disease (COPD), treatment of respiratory disorders related to neuromuscular diseases and the like, and provides better support and help for patients and doctors. And new ways and possibilities are provided for research and treatment of respiratory system diseases and related neuromuscular diseases in the future.

Description

Technical Field

[0001] The present invention belongs to the field of respiratory function rehabilitation electrostimulation treatment devices, and particularly relates to the design and implementation of a low-fatigue multi-mode extracorporeal diaphragm pacemaker based on electrocardiogram signal feedback. Background Art

[0002] Common respiratory system diseases include chronic obstructive pulmonary disease (COPD), central respiratory failure, neuromuscular diseases, etc. Their causes are diverse, including respiratory muscle dysfunction, respiratory center damage, airway obstruction, and infection and inflammation. These diseases lead to impaired diaphragm function, resulting in problems such as diaphragmatic paralysis, respiratory muscle weakness, and respiratory muscle fatigue, causing symptoms such as irregular breathing and dyspnea. Currently, the respiratory improvement methods used clinically include abdominal breathing training, non-invasive or invasive respiratory muscle mechanical ventilation (such as CPAP, BIPAP), surgical repair of the diaphragm, postural and position therapy, drug therapy, etc. These methods provide respiratory support for patients with respiratory disorders by enhancing diaphragm strength, repairing diaphragmatic nerve problems, improving diaphragm activity, and anti-inflammatory treatment of complications. The treatment effect is relatively significant, but there are also many disadvantages, such as: respiratory muscle training requires time and patience, and the effect varies from person to person; long-term use of mechanical ventilation may lead to dependence, infection risk, and complications (such as tracheal injury); surgical treatment has a high risk, a long recovery period, and may require multiple surgeries; postural and position therapy requires long-term adherence, has a slow effect, and may not be applicable to some patients; drug therapy has side effects, and long-term use may lead to drug resistance or side reactions.

[0003] In addition to the traditional methods mentioned above, an extracorporeal diaphragm pacemaker (EDP) can enhance diaphragm function by electrically stimulating the phrenic nerve. As an innovative non-invasive technique, this method does not require surgery, avoiding surgery-related risks and making up for the deficiencies of traditional methods. The specific implementation process is as follows:

[0004] 1) Place the positive electrode at the lower 1 / 3 of the outer edge of the sternocleidomastoid muscle, and place the negative electrode at a position near the diaphragm in the chest area.

[0005] 2) Transmit the electrical stimulation current to the phrenic nerve through the skin surface electrode, activate the excitability of the nerve, trigger an action potential, cause the diaphragm to contract and the dome to descend, and along with the synergistic contraction of the external intercostal muscles, the thoracic cavity volume increases, the intrathoracic pressure decreases, and the transpulmonary pressure gradient drives gas to flow into the alveoli through the airway, completing the inspiration action.

[0006] 3) In the absence of current stimulation, patients with impaired respiratory centers or nerve conduction pathways are unable to generate spontaneous respiratory action potentials. The excitability of the diaphragmatic fibers ceases, and the muscles are in a relaxed state. The relaxed diaphragm gradually returns to its initial position due to its own elastic recoil force, that is, it returns upward into the thoracic cavity. The thoracic cavity volume decreases, the intrathoracic pressure increases, and the combined action of the alveolar elastic recoil force and the chest wall elastic recoil force generates an expiratory driving force to complete the expiratory action.

[0007] The EDP is simple to operate and portable, and can be used for long-term assisted use in a home environment. It is not only suitable for patients who are unable to actively perform respiratory muscle training, but also helps to promote the functional recovery of the diaphragmatic muscles. It is a safe, economical and efficient respiratory assistance method.

[0008] Although the EDP has achieved remarkable results in clinical applications at home and abroad at the present stage, it also faces some problems and challenges. For example:

[0009] 1) Prone to muscle fatigue: Under long-term electrical stimulation, the muscles will experience fatigue problems, manifested as a decrease in contractility.

[0010] 2) Lack of real-time physiological signal monitoring function: The currently used electrical stimulator lacks the function of real-time monitoring of human physiological signals, and it is impossible to detect in time the discomfort of patients or the inappropriate stimulation intensity.

[0011] 3) Lack of electrical stimulation parameter optimization function: Due to differences in the diaphragmatic structure, nerve sensitivity, disease progression, etc. among different patients, the responses to electrical stimulation are different. How to expand the parameter range and accurately adjust parameters such as the intensity, frequency, and waveform of electrical stimulation to maximize the activation of the diaphragm and avoid side effects is still a difficult problem.

[0012] The above three problems will bring the following disadvantages during the use of the device: The lack of a function to solve muscle fatigue will lead to a decline in the use effect, diaphragmatic injury and patient discomfort, and in severe cases, even cause the functional decline of the diaphragm; The lack of real-time detection function will result in the inability to dynamically adjust the stimulation parameters according to the changes in real-time physiological parameters, thus causing excessive or insufficient stimulation; At the same time, in the absence of a monitoring function, if the patient has other physiological abnormalities, the device cannot respond in time, thus increasing the safety risk; The lack of a parameter optimization function will result in improper parameter settings affecting the curative effect. For example, too low stimulation parameters will lead to insufficient diaphragmatic contraction, and too high parameters will increase muscle fatigue; At the same time, it cannot meet the personalized use plan. Different patients have different sensitivities and tolerances to stimulation, and fixed parameter settings cannot meet the needs of each patient, which will reduce the use effect. Therefore, in order to make the EDP more effective for different patients with respiratory disorders, it is urgent to solve the above three problems. Summary of the Invention

[0013] In response to the needs of patients with respiratory disorders for low fatigue and safety when using EDP, the present invention provides an effective low-fatigue feedback device. By dispersing the activation sequence of different motor nerve fibers through high-frequency (kHz) electrical stimulation, it makes them follow an asynchronous activation pattern, improves the utilization efficiency of the diaphragm, solves the problem of stimulation fatigue caused by long-term use, monitors the patient's real-time usage status and physiological status through respiratory data obtained from electrocardiogram signal processing, and adapts to the personalized needs of different patients by pre-designing multiple stimulation modes and a wide range of dynamically adjustable stimulation parameters.

[0014] The technical solution of the present invention:

[0015] A low-fatigue multi-mode extracorporeal diaphragm pacemaker based on electrocardiogram signal feedback, comprising a microcontroller, an electrical stimulation system, an electrocardiogram signal acquisition system, and a power supply system.

[0016] The electrical stimulation system and the electrocardiogram signal acquisition system share a microcontroller, and the power supply system supplies power to the microcontroller, the electrical stimulation system, and the electrocardiogram signal acquisition system. The microcontroller simultaneously performs data analysis and processing on the electrocardiogram acquisition system and the electrical stimulation system.

[0017] The electrical stimulation system includes a stimulation electrode, a constant current source module, a current detection module, an H-bridge module, and a power supply module. First, the stimulation signal is generated by the DAC pin of the microcontroller and is in the form of a voltage pulse. Secondly, this voltage pulse signal is transmitted to the constant current source module and converted into a current pulse signal. At the same time, the current detection module is used to monitor the magnitude of the current pulse in the constant current source module and feedback the detection result to the microcontroller to ensure that the magnitude of this current pulse is within a safe range. Then, the current pulse output by the constant current source is transmitted to the H-bridge module, and by controlling the conduction state of the optocoupler diode in this module, the output direction of this current pulse is determined. Finally, the current pulse output by the H-bridge module is output to the human body through the stimulation electrode. The power supply module supplies power to the microcontroller and the H-bridge module.

[0018] Furthermore, the stimulation electrode: is in the form of two-channel four electrode patches, with a positive electrode placed at the lower 1 / 3 of the outer edge of the sternocleidomastoid muscle and a negative electrode placed in the chest area near the diaphragm.

[0019] The electrocardiogram (ECG) signal acquisition system includes acquisition electrodes, a primary low-pass filter and input protection module, a right leg drive module, a first-stage differential amplifier module, a first-order active high-pass filter module, a fifth-order Bessel low-pass filter module, a secondary amplification and level elevation module, and an AD analog-to-digital conversion module. During ECG acquisition, the signal is input by three acquisition electrodes on the left leg, right arm, and left arm. After being subjected to primary low-pass filtering and input protection, the signal is further processed by first-stage differential amplification and first-order active high-pass filtering, and then passes through a fifth-order Bessel low-pass filter to retain the signal characteristics. Then the signal is subjected to secondary amplification and level elevation, and is converted into a digital signal by the AD analog-to-digital conversion module and transmitted to a microcontroller for data processing, and finally displayed on an LCD. The right leg drive module is used to reduce the common-mode noise during the first-stage differential amplification process.

[0020] Further, the acquisition electrodes: adopt the standard limb lead form, use three bipolar limb leads (I, II, III), and the electrode positions are the left leg electrode (LL), the right arm electrode (RA), and the left arm electrode (LA). The electrode material is selected as silver / silver chloride (Ag / AgCl) with good biocompatibility and electrochemical stability.

[0021] The converted ECG digital signal is transmitted to the microcontroller through the ADC2 pin of the hardware interface for processing and analysis, specifically including:

[0022] (1) The specific steps for extracting the respiratory signal based on ECG are as follows:

[0023] 2.1 ECG signal acquisition: The ECG signal of the human body is acquired through the standard three-lead ECG acquisition electrodes. The ECG signal reflects the electrical activity of the heart and mainly includes the P wave, the R wave in the QRS complex, and the T wave.

[0024] 2.2 Signal preprocessing: After being processed by the ECG signal acquisition system, the finally acquired ECG digital signal after AD conversion is subjected to preliminary low-pass filtering to remove high-frequency noise and low-frequency drift.

[0025] 2.3 Detection and analysis of the R wave: The Pan & Tompkins algorithm is used for R wave detection.

[0026] Steps of the Pan & Tompkins algorithm:

[0027] 1) Band-pass filtering: The ECG signal after preliminary low-pass filtering is passed through a band-pass filter to remove high-frequency noise and low-frequency interference.

[0028] 2) Differential operation: The ECG signal processed by the band-pass filter is subjected to first-order or second-order differential operation to enhance the characteristics of the QRS complex.

[0029] 3) Squaring operation: Square the differentiated signal to further highlight the peak value of the QRS complex.

[0030] 4) Moving window integration: Smooth the squared signal through moving window integration to extract the contour of the QRS complex.

[0031] 5) Threshold determination: Set a dynamic threshold. When the signal exceeds this threshold, it is determined that an R wave appears. By adaptively adjusting the threshold, false detection or missed detection caused by signal amplitude changes can be avoided.

[0032] 2.4 Baseline drift removal: Use natural cubic spline interpolation to estimate the baseline of the electrocardiogram signal after R wave detection. By selecting the non-R wave region in the electrocardiogram signal as the baseline reference point, use spline interpolation to fit the baseline curve, and then subtract this baseline from the original signal to remove the baseline drift.

[0033] 2.5 Respiratory signal extraction:

[0034] 1) R wave amplitude sequence extraction: Extract the amplitude sequence of the R wave from the R waves detected in step 2.3 to form an amplitude array sequence related to time.

[0035] 2) Interpolation processing: Use natural cubic spline interpolation to perform interpolation processing on the R wave amplitude array sequence to generate a continuous respiratory signal.

[0036] 3) Respiratory frequency calculation: By performing Fourier transform or wavelet transform on the continuous respiratory signal, analyze its spectral characteristics, and thus calculate the respiratory frequency.

[0037] (2) Parameter optimization of the anti-fatigue electrical stimulator for different modes

[0038] Customize different modes to correspond to the respiratory demands under different physiological states. Collect sufficient data in advance and conduct comprehensive analysis to set the initial current stimulation parameter values for each mode.

[0039] When the user uses the low-fatigue multi-mode external diaphragm pacemaker and selects the corresponding mode, the electrical stimulator directly outputs the current stimulation parameter values. By collecting and analyzing the user's electrocardiogram signal, fatigue detection and adjustment of the current stimulation parameter values are carried out, or safety monitoring and abnormal handling are performed, and then the initial current stimulation parameter values are adjusted according to the cause and frequency of the abnormality.

[0040] Furthermore, after optimization and adjustment, the user can also perform real-time manual dynamic adjustment of the current stimulation parameter values output by the electrical stimulator.

[0041] Advantages of the present invention: By adding a high-frequency (kHz) stimulation output function and cross-stimulation with low-frequency output, the present invention solves the problem of muscle fatigue caused by long-term use of electrical stimulation. By adding an electrocardiogram signal acquisition system and an EDR algorithm, the present invention realizes the real-time monitoring function of physiological conditions and the optimized setting function of stimulation parameters during the stimulation process. The above methods effectively improve the effect and safety of electrical stimulation of the diaphragm for assisting breathing. The EDP designed by the present invention has the advantages of low fatigue, high safety, and strong adaptability. Brief Description of the Drawings

[0042] Figure 1 It is a schematic diagram of the appearance of the EDP device.

[0043] Figure 2 It is a block diagram of the overall system architecture.

[0044] Figure 3 It is a block diagram of the electrical stimulation system.

[0045] Figure 4 It is a flowchart of the main program.

[0046] Figure 5 It is a block diagram of the electrocardiogram signal acquisition system.

[0047] Figure 6 It is the system power tree.

[0048] Figure 7 It is a flowchart of the EDR algorithm.

[0049] Figure 8 It is the modulation effect of respiration on the R-wave amplitude of the electrocardiogram signal.

[0050] Figure 9 It is a flowchart of the three-layer parameter optimization.

[0051] Figure 10 It is a schematic diagram of the use of the present invention. Detailed Embodiments

[0052] The technical solution of the present invention will be further described below according to the drawings and embodiments.

[0053] A low-fatigue multi-mode extracorporeal diaphragm pacemaker based on electrocardiogram signal feedback, including a microcontroller, an electrical stimulation system, an electrocardiogram signal acquisition system, and a power supply system, as Figure 2 shown. The electrical stimulation system and the electrocardiogram signal acquisition system share a microcontroller, and the power supply system supplies power to the microcontroller, the electrical stimulation system, and the electrocardiogram signal acquisition system.

[0054] The described microcontroller simultaneously performs data analysis and processing on the electrocardiogram acquisition system and the electrical stimulation system. This design reduces the independence requirements of system modules, avoids communication delays and interface complexities between multiple processors, simplifies the circuit design, reduces the hardware space occupation, and makes the device more miniaturized and portable.

[0055] The described electrical stimulation system includes a stimulation electrode, a constant current source module, a current detection module, an H-bridge module, and a power supply module, as Figure 2 , Figure 3 shown. First, the stimulation signal is generated by the output of the DAC (Digital-to-Analog Converter) pin of the microcontroller in the form of a voltage pulse. Second, this voltage pulse signal is transmitted to the constant current source module and converted into a current pulse signal. At the same time, the current detection module is used to monitor the magnitude of the current pulse in the constant current source module and feedback the detection result to the microcontroller to ensure that the magnitude of this current pulse is within a safe range. Then, the current pulse output by the constant current source is transmitted to the H-bridge module, and by controlling the conduction state of the optocoupler diode in this module, the output direction of this current pulse is determined. Finally, the current pulse output by the H-bridge module is output to the human body through the stimulation electrode. The power supply module supplies power to the microcontroller and the H-bridge module.

[0056] Furthermore, the described stimulation electrode: is in the form of two-channel four electrode patches. The positive electrode is placed at the lower 1 / 3 of the outer edge of the sternocleidomastoid muscle, and the negative electrode is placed in the chest area near the diaphragm. This design can not only provide more accurate stimulation positioning, but also enhance the overall efficacy of the pacemaker, improve the comfort of use, and bring a better rehabilitation experience for patients by simultaneously stimulating the phrenic nerves on both sides.

[0057] Furthermore, the described constant current source module: The constant current source module utilizes the "virtual short" principle of an ideal integrated operational amplifier and combines an NPN transistor and a high-precision sampling resistor device to form a voltage series negative feedback circuit. This design significantly improves the stability and accuracy of the circuit. The voltage V DAC (ranging from 0 to 3.3V) output by the DAC pin of the microcontroller is converted into a current form. By configuring the resistance value of the emitter of the NPN transistor to be 300Ω, and according to the "virtual short" principle of the ideal integrated operational amplifier and Ohm's law, the current pulse magnitude range is calculated to be 0.1 to 11 mA.

[0058] Further, the current detection module: To ensure that the stimulation parameters output by the electrical stimulator are within the safe range for the human body during use, the present invention incorporates a current detection module. The current detection module uses an in-phase proportional amplification circuit to amplify the voltage generated by the current pulse flowing through a 10Ω sampling resistor on the emitter of an NPN transistor and transmits it to pin 1 of the ADC (Analog-to-Digital Converter) of the microcontroller for detection and judgment. If this voltage exceeds the set threshold, the output is immediately stopped and an alarm is given.

[0059] Further, the H-bridge module: The H-bridge module selects optocoupler diodes to replace the original transistor design, which not only effectively isolates the control signal from the load circuit but also ensures the safety and stability of the system. The function of the H-bridge module is to control the conduction state of the optocoupler through the high and low levels output by the I / O port pins of the microcontroller, thereby controlling the current direction on the load and generating the bidirectional stimulation square wave required for electrical stimulation, so as to achieve positive and negative phase alternating stimulation. The purpose is to prevent damage to the human body caused by charge accumulation due to continuous unidirectional stimulation during the stimulation process.

[0060] The range of stimulation parameters that the electrical stimulation can output is as follows:

[0061]

[0062] After the electrical stimulation hardware circuit is built, by designing and loading the software program into the microcontroller, it is possible to directly select the stimulation parameters for four human motion modes (sleep, quiet, walking, jogging) and dynamically adjust various stimulation parameters (pulse amplitude, pulse frequency, stimulation pulse width). The specific process is as Figure 4 shown. In this embodiment, buttons are set on the device, as Figure 1 shown, and the voltage value is adjusted and displayed in real time through button input. At the beginning, first initialize the HAL (Hardware Abstraction Layer) library, system clock, delay, and serial port functions, as well as initialize the LCD screen and DAC. Then, set the initial values of the stimulation parameters for the four modes and display them on the LCD screen. Continuously detect button input. For example, according to the pressing of the "stimulation amplitude increase" or "stimulation amplitude decrease" buttons, the voltage value is increased or decreased accordingly, while ensuring that the voltage value is within the range of 0V to 3.3V. The adjusted voltage value is output and displayed on the LCD screen. In addition, delay processing is also included to ensure the stability of the operation. The entire process is executed in a loop to achieve dynamic control and real-time display of the stimulation parameters.

[0063] As Figure 5As shown in the figure, the electrocardiogram (ECG) signal acquisition system includes acquisition electrodes, a primary low-pass filter and input protection module, a right leg drive module, a first-stage differential amplifier module, a first-order active high-pass filter module, a fifth-order Bessel low-pass filter module, a secondary amplification and level boosting module, and an AD analog-to-digital conversion module. During ECG acquisition, signals are input through three acquisition electrodes on the left leg, right arm, and left arm. After passing through the primary low-pass filter and input protection, the signals are further processed by the first-stage differential amplifier and the first-order active high-pass filter, and then passed through the fifth-order Bessel low-pass filter to retain signal characteristics. Then the signals are secondarily amplified and level boosted, and then converted into digital signals by the AD analog-to-digital conversion module and transmitted to the microcontroller for data processing, and finally displayed on the LCD. The right leg drive module is used to reduce common-mode noise during the first-stage differential amplification process.

[0064] Furthermore, for the acquisition electrodes: The standard limb lead form is adopted, using three bipolar limb leads (I, II, III), and the electrode positions are the left leg electrode (LL), the right arm electrode (RA), and the left arm electrode (LA). The electrode material is selected as silver / silver chloride (Ag / AgCl) with good biocompatibility and electrochemical stability.

[0065] Furthermore, for the primary low-pass filter and input protection module: This design not only has a low-pass filtering effect but also has an input protection function. The input protection module can effectively prevent damage to the input end caused by high voltage or current. This design ensures the reliability of the system in extreme working environments, making the entire signal acquisition system more robust.

[0066] Furthermore, for the right leg drive module: During ECG signal acquisition, common-mode noise is the main interference source. To effectively suppress common-mode noise, this module design adopts reverse voltage control technology, which can significantly reduce the impact of common-mode noise on ECG signals. This not only improves the accuracy of the signals but also ensures signal quality in complex environments and reduces the risk of external electromagnetic interference.

[0067] Furthermore, for the first-stage differential amplifier module: This module design uses a high-precision differential amplifier, which can effectively amplify weak ECG signals by 14 times while suppressing external noise. Its design feature is that it can maintain the clarity of the signals in a high-noise environment, ensuring the accuracy and reliability of the amplified signals.

[0068] Furthermore, for the first-order active high-pass filter module: During acquisition, limb movement caused by the human body will cause baseline drift, and its frequency is mainly concentrated in the range of 0.03 - 2 Hz. Therefore, this system is designed with a first-order high-pass filter for filtering. The cut-off frequency of the high-pass filter is set to 0.0339 Hz. The reason for setting the cut-off frequency relatively low is to avoid attenuation of low-frequency ECG signals.

[0069] Furthermore, for the fifth-order Bessel low-pass filtering module: Since the spectral width of the ECG is generally between 0.05 - 200 Hz, low-pass filtering must be used to filter out the frequency bands beyond the ECG frequency band. Selecting a fifth-order Bessel filter as the high-order filter can efficiently filter out high-frequency noise while retaining the shape of the signal. This filter has a flat amplitude response and a linear phase response, ensuring the fidelity of the electrocardiogram signal and minimizing distortion to the greatest extent possible.

[0070] Furthermore, for the secondary amplification and level elevation module: The electrocardiogram signal is still relatively weak after the first-stage differential amplification. To meet the requirements of subsequent analog-to-digital conversion accuracy, the present invention designs a secondary amplification circuit. By amplifying the input signal by approximately 70 times, the requirement for the high precision of the analog-to-digital conversion chip is reduced. At the same time, to ensure that the signal input to the AD conversion chip is positive, a level elevation circuit is designed to ensure that the minimum input voltage condition of the ADC is met.

[0071] Furthermore, for the AD analog-to-digital conversion module: Since the ADC built into the microcontroller is close to the crystal oscillator circuit and other digital pins of the microcontroller, the analog electrocardiogram signal is more susceptible to interference. To keep the analog signal as far away from the digital signal as possible, the present invention selects the ADC8866 of Texas Instruments (TI) to design an external ADC chip, which converts the continuous analog electrocardiogram signal into a discrete digital signal for efficient processing, analysis, and transmission by the subsequent microcontroller system.

[0072] The converted electrocardiogram digital signal is transmitted to the microcontroller through the ADC2 pin of the hardware interface for processing and analysis, including:

[0073] (1) The processing and analysis method: The extraction of respiratory signals based on electrocardiogram is a technology that indirectly obtains respiratory information by analyzing the electrocardiogram (ECG) signal, called the electrocardiogram-derived respiration (ECG-Derived Respiration, EDR) technology. As Figure 7 shown, this technology does not require dedicated sensors and hardware modules to detect respiratory signals. It only needs to obtain the electrocardiogram signal using an electrocardiogram monitor, avoiding the restraint on the human body of the above two detection methods and making dynamic respiratory detection possible.

[0074] The specific steps for the extraction of respiratory signals based on electrocardiogram are as follows:

[0075] 2.1 Electrocardiogram signal acquisition: The electrocardiogram signal of the human body is collected through standard three-lead electrocardiogram acquisition electrodes (left arm, right arm, left leg). The electrocardiogram signal reflects the electrical activity of the heart and mainly includes the P wave, the R wave in the QRS complex, and the T wave.

[0076] 2.2 Signal preprocessing: After being processed by the electrocardiogram signal acquisition system, the electrocardiogram digital signal after AD conversion is finally subjected to preliminary low-pass filtering to remove high-frequency noise and low-frequency drift.

[0077] 2.3 Detection and analysis of R waves: The Pan & Tompkins algorithm is used for R wave detection. This algorithm is a classic QRS complex detection algorithm, which has the characteristics of high calculation efficiency and good detection accuracy.

[0078] Steps of the Pan & Tompkins algorithm:

[0079] 1) Band-pass filtering: The electrocardiogram signal after preliminary low-pass filtering is passed through a band-pass filter (usually 5 Hz to 15 Hz) to remove high-frequency noise and low-frequency interference.

[0080] 2) Differential operation: The electrocardiogram signal processed by the band-pass filter is subjected to first-order or second-order differential operation to enhance the characteristics of the QRS complex.

[0081] 3) Squaring operation: The differentiated signal is squared to further highlight the peak value of the QRS complex.

[0082] 4) Moving window integration: The squared signal is smoothed by moving window integration (usually the window length is 200 ms) to extract the contour of the QRS complex.

[0083] 5) Threshold determination: A dynamic threshold is set. When the signal exceeds this threshold, it is determined that an R wave appears. By adaptively adjusting the threshold, false detection or missed detection caused by signal amplitude changes can be avoided.

[0084] 2.4 Removal of baseline drift: The baseline drift in the electrocardiogram signal is mainly caused by factors such as poor electrode contact, respiratory movement, and patient body position changes. Baseline drift will mask the true amplitude change of the R wave and affect the extraction of the respiratory signal, so baseline drift needs to be removed. Specific removal method: The natural cubic spline interpolation method is used to estimate the baseline of the electrocardiogram signal after R wave detection. By selecting the non-R wave region (such as the latter part of the T wave or the former part of the P wave) in the electrocardiogram signal as the baseline reference point, the baseline curve is fitted by spline interpolation, and then this baseline is subtracted from the original signal to remove the baseline drift.

[0085] 2.5 Extraction of respiratory signal: According to the fact that respiratory movement will affect the amplitude of the R wave in the electrocardiogram, which is manifested as a decrease in the amplitude of the R wave during inspiration and an increase in the amplitude of the R wave during expiration. This phenomenon is due to the change in thoracic pressure caused by respiratory movement, which in turn affects the electrical activity of the heart. The specific steps are as follows:

[0086] 1) Extraction of R wave amplitude sequence: The amplitude sequence of the R wave is extracted from the R waves detected in step 2.3 to form an amplitude array sequence related to time.

[0087] 2) Interpolation processing: Use natural cubic spline interpolation to interpolate the R-wave amplitude array sequence to generate a continuous respiration signal. Spline interpolation can smoothly connect discrete R-wave amplitude points while maintaining the periodicity and amplitude variation of the respiration signal.

[0088] 3) Respiration frequency calculation: By performing Fourier transform or wavelet transform on the continuous respiration signal and analyzing its spectral characteristics, the respiration frequency is calculated.

[0089] The power supply system described includes a +5V to ±3.3V module, a ±5V module, and a +60V module.

[0090] 1.3 The design of the power supply system is crucial for the stability of the entire system, and a reliable, stable, and efficient power supply is a prerequisite for ensuring the safe and stable operation of the system. During the overall design process, the required supply voltages and currents of each module are different. Based on the voltage magnitudes and current requirements of each branch, the required voltage and current magnitudes of each module are evaluated, and a power supply tree is drawn for the entire system. As Figure 6 shown:

[0091] 1) +5V to ±3.3V module: Designed using a Low Dropout Regulator (LDO), through its internal feedback mechanism, the input +5V voltage is converted into a stable ±3.3V output.

[0092] 2) +5V to ±5V module: Use the Isolated Power Supply Module of the JSY A05_S-1WR3&B05_LS-1WR3 series, and add a capacitive filter network at the input and output ends of the module to reduce ripple and noise, and through precise feedback control, a stable ±5V output is obtained.

[0093] 3) +5V to +60V module: Select a BOOST boost circuit. Through the high-frequency switching action of a switching transistor (such as a MOSFET), the input voltage is stored in the inductor and then released to the output capacitor through a diode, thereby achieving the boost function. By controlling the duty cycle of the switching transistor in the circuit design, the required +60V output voltage can be accurately obtained.

[0094] (2) Parameter optimization of the anti-fatigue electrical stimulator for different modes

[0095] Customize different modes to correspond to the respiration requirements under different physiological states. Collect sufficient data in advance and conduct comprehensive analysis to set the initial current stimulation parameter values (stimulation frequency, amplitude, and pulse width) for each mode.

[0096] When the user uses the low-fatigue multi-mode external diaphragm pacemaker and selects the corresponding mode, the electrical stimulator directly outputs the current stimulation parameter value. By collecting and analyzing the user's electrocardiogram signal, fatigue detection and stimulation parameter adjustment are carried out, or safety monitoring and abnormal handling are carried out, and then the initial stimulation parameters are adjusted according to the cause and frequency of the abnormality.

[0097] For the current different exercise states of the human body and the real-time respiratory data information of the human body obtained from the electrocardiogram signal, the current stimulation pulse parameters output by the electrical stimulator are dynamically adjusted. This is an intelligent technology based on real-time physiological feedback. This method dynamically optimizes the electrical stimulation parameters by accurately monitoring and analyzing the respiratory needs of the human body to ensure safety, comfort and effectiveness during use.

[0098] For step 3, the specific steps for optimizing the parameters of the anti-fatigue electrical stimulator for different modes are as follows:

[0099] 3.1 First-layer parameter optimization - mode selection and initial parameter setting

[0100] Four predefined output modes are set for manual selection, corresponding to the respiratory needs under different physiological states: sleep mode, quiet mode, walking mode, jogging mode. In each mode, the user can fine-tune the stimulation parameters (such as stimulation frequency, amplitude, pulse width, etc.) according to their own needs.

[0101] The basis for parameter setting in the four different modes:

[0102] 1) Volunteer data collection: Collect the electrocardiogram data of 20 healthy volunteers in the above four modes, analyze their respiratory information (such as respiratory frequency, heart rate variability, etc.), and calculate the average value.

[0103] 2) Analysis of public data sets: Analyze and process the publicly available electrocardiogram signal data sets on the Internet, extract the respiratory characteristics under different activity states as the reference standard for the normal population.

[0104] 3) Comprehensive reference: Combine the analysis results of volunteer data and public data sets to set the initial stimulation parameters for each mode to ensure that they are applicable to most users.

[0105] According to the comprehensive analysis results, set the initial stimulation frequency, amplitude and pulse width for each mode. When the button corresponding to the selected mode is pressed, the electrical stimulator directly outputs the current stimulation parameter value.

[0106] 3.2 Second-layer parameter optimization - fatigue detection and stimulation parameter adjustment based on respiratory frequency

[0107] Analyze the obtained breathing rate: First, set the normal range of the breathing rate according to the patient's age, health status, and exercise state. For example, the breathing rate of an adult at rest is usually 12 - 20 breaths per minute, while it increases to 20 - 30 breaths per minute during exercise. If the breathing rate significantly decreases (such as below 20% of the normal range), it indicates that the patient has breathing fatigue. For example, if the breathing rate drops from 15 breaths per minute to below 12 breaths per minute at rest, it indicates diaphragmatic fatigue.

[0108] When a fatigue problem is detected, select the variable frequency mode. By regulating the usage duration of high - frequency and low - frequency stimuli, with high - frequency stimuli used for a long time and low - frequency stimuli used for a short time, to solve the muscle fatigue problem caused during long - term use.

[0109] 3.3 Optimization of the third - layer parameters - Safety monitoring and exception handling

[0110] 1) Real - time heart rate and breathing rate monitoring: Based on the real - time collected electrocardiogram (ECG) signals, continuously monitor the heart rate changes. If the heart rate exceeds the normal range (such as more than 120 beats per minute or less than 40 beats per minute), it indicates that the patient has discomfort or other safety problems. In addition, the breathing rate can be continuously monitored through heart rate variability (HRV) analysis. If the breathing rate is abnormal (such as less than 6 breaths per minute or more than 30 breaths per minute), it indicates breathing problems or inappropriate stimulation intensity.

[0111] 2) Abnormality judgment and handling:

[0112] Based on the changes in heart rate and breathing rate, judge whether a safety abnormality occurs. For example, if both the heart rate and breathing rate show abnormal changes, it indicates that the patient is in a state of discomfort. If a safety abnormality is detected, immediately stop the electrical stimulation to avoid further discomfort or risk to the patient.

[0113] 3) Alarm prompt: Notify the patient or medical staff through the device's alarm system (sound alarm prompt).

[0114] 4) Data recording: Record the ECG signals and breathing signals when an abnormality occurs for subsequent analysis and parameter adjustment.

[0115] 5) Subsequent adjustment: Adjust the initial stimulation parameters according to the cause and frequency of the abnormality. For example, if heart rate abnormalities frequently occur, it is necessary to reduce the stimulation amplitude or adjust the stimulation frequency.

[0116] Through continuous monitoring and optimization, ensure that the electrical stimulator can operate safely and effectively in different modes.

[0117] Analyze and process the electrocardiogram signals through the above method, and dynamically adjust the stimulation parameters output by the electrical stimulator in real time manually based on the obtained breathing frequency as feedback information to achieve better usage effects.

[0118] This device is applicable to various patients with respiratory disorders, such as respiratory failure, chronic obstructive pulmonary disease (COPD), neuromuscular diseases, etc., and can significantly improve the respiratory function of patients and improve the quality of life. At the same time, this technology also provides new ideas and methods for the research and development of future intelligent medical devices.

Claims

1. A low fatigue multi-mode external diaphragm pacemaker based on ECG signal feedback, characterized in that: Including microcontroller, electrical stimulation system, ECG signal acquisition system, power supply system; The electrical stimulation system and the ECG signal acquisition system share a microcontroller, and the power supply system supplies power to the microcontroller, the electrical stimulation system, and the ECG signal acquisition system; the microcontroller simultaneously performs data analysis and processing on the ECG acquisition system and the electrical stimulation system; The electrical stimulation system includes stimulation electrodes, a constant current source module, a current detection module, an H-bridge module, and a power supply module; firstly, the stimulation signal is generated by the DAC pin output of the microcontroller in the form of a voltage pulse; secondly, the voltage pulse signal is transmitted to the constant current source module and converted into a current pulse signal; At the same time, the current detection module is used to monitor the size of the current pulse in the constant current source module and feed back the detection result to the microcontroller to ensure that the current pulse size is within a safe range; then, the current pulse output by the constant current source is transmitted to the H-bridge module, and the output direction of the current pulse is determined by controlling the conduction state of the optocoupler diode in the module; finally, the current pulse output by the H-bridge module is output to the human body through the stimulation electrode; The power module supplies power to the microcontroller and H-bridge module; The ECG signal acquisition system includes acquisition electrodes, a primary low-pass filter and input protection module, a right leg drive module, a primary differential amplifier module, a first-order active high-pass filter module, a fifth-order Bessel low-pass filter module, a secondary amplifier and level raising module, and an AD analog-to-digital conversion module; during the ECG acquisition process, the signal is input by three acquisition electrodes of the left leg, right arm and left arm, and after the signal is subjected to primary low-pass filtering and input protection, it is further processed by a primary differential amplifier and a first-order active high-pass filter, and then subjected to a fifth-order Bessel low-pass filter to retain the signal characteristics; The signal is then amplified and level-raised for a second time, converted into a digital signal through an AD analog-to-digital conversion module, transmitted to a microcontroller for data processing, and finally displayed on an LCD; while performing the first-stage differential amplification processing, the right leg drive module is used to reduce common-mode noise.

2. A low-fatigue multi-mode external diaphragm pacemaker based on ECG signal feedback according to claim 1, characterized in that: The converted ECG digital signal is transmitted from the hardware interface ADC2 pin to the microcontroller for processing and analysis, including: (I) The specific steps of ECG-based respiratory signal extraction are as follows: 2.1 ECG signal acquisition: The ECG signal of the human body is collected through the standard three-lead ECG acquisition electrode; the ECG signal reflects the electrical activity of the heart, mainly including the P wave, the R wave and the T wave in the QRS complex; 2.2 Signal preprocessing: After being processed by the ECG signal acquisition system, the AD-converted ECG digital signal is finally subjected to preliminary low-pass filtering to remove high-frequency noise and low-frequency drift; 2.3 Detection and analysis of R wave: Pan & Tompkins algorithm is used to detect R wave; Pan&Tompkins algorithm steps: 1) Bandpass filtering: The ECG signal after preliminary low-pass filtering is passed through a bandpass filter to remove high-frequency noise and low-frequency interference; 2) Differential operation: Perform first-order or second-order differential operation on the ECG signal after bandpass filter processing to enhance the characteristics of the QRS complex; 3) Square operation: Square the differential signal to further highlight the peak of the QRS complex; 4) Moving window integration: The squared signal is smoothed by moving window integration to extract the contour of the QRS complex; 5) Threshold determination: Set a dynamic threshold. When the signal exceeds the threshold, it is determined as the appearance of the R wave. By adaptively adjusting the threshold, false detection or missed detection caused by changes in signal amplitude can be avoided. 2.4 Baseline drift removal: The baseline of the ECG signal after R wave detection is estimated using the natural cubic spline interpolation method; the non-R wave region in the ECG signal is selected as the baseline reference point, the baseline curve is fitted using spline interpolation, and then the baseline is subtracted from the original signal to remove the baseline drift; 2.5 Respiratory signal extraction: 1) Extraction of R wave amplitude sequence: Extract the R wave amplitude sequence from the R wave detected in step 2.3 to form a time-dependent amplitude array sequence; 2) Interpolation processing: Use the natural cubic spline interpolation method to interpolate the R wave amplitude array sequence to generate a continuous respiratory signal; 3) Respiratory frequency calculation: The respiratory frequency is calculated by performing Fourier transform or wavelet transform on the continuous respiratory signal and analyzing its spectrum characteristics; (II) Parameter optimization of anti-fatigue electrical stimulator for different modes Customize different modes to correspond to breathing needs under different physiological states, collect sufficient data in advance, conduct comprehensive analysis, and set the initial current stimulation parameter value for each mode; When the user uses a low-fatigue multi-mode external diaphragm pacemaker, after selecting the corresponding mode, the electric stimulator directly outputs the corresponding current stimulation parameter value; by collecting the user's electrocardiogram signal and analyzing it, fatigue detection and current stimulation parameter value adjustment are performed, or safety monitoring and abnormality processing are performed, and then the initial current stimulation parameter value is adjusted according to the cause and frequency of the abnormality.

3. A low-fatigue multi-mode external diaphragm pacemaker based on ECG signal feedback according to claim 1 or 2, characterized in that: The stimulation electrode is in the form of two-channel four-electrode patch, with a positive electrode placed at the lower 1 / 3 of the outer edge of the sternocleidomastoid muscle and a negative electrode placed near the diaphragm in the chest area.

4. A low-fatigue multi-mode external diaphragm pacemaker based on ECG signal feedback according to claim 1 or 2, characterized in that: The collection electrodes: adopt the standard limb lead form, use three bipolar limb leads, the electrode positions are left leg electrode, right arm electrode, left arm electrode; the electrode material is silver / silver chloride with good biocompatibility and electrochemical stability.

5. The low-fatigue multi-mode external diaphragm pacemaker based on ECG signal feedback according to claim 2, characterized in that: After optimization and adjustment, the user can also manually and dynamically adjust the current stimulation parameter values ​​output by the electrical stimulator in real time.

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