Enhanced external counterpulsation adaptive control system

Abstract

The application provides an enhanced external counterpulsation (EECP) adaptive control system, which comprises a multi-modal physiological signal synchronous acquisition module and a signal analysis processing module; the multi-modal physiological signal synchronous acquisition module is used for acquiring synchronous multi-modal physiological signals of a subject; the synchronous multi-modal physiological signals comprise single-lead or multi-lead electroencephalogram signals and single-lead electrocardiogram signals; the signal analysis processing module is used for pre-processing the synchronous multi-modal physiological signals, then performing feature point identification and joint analysis to obtain a physiological coupling index, and controlling inflation and deflation of an air bag of an enhanced external counterpulsation device through the physiological coupling index.

Description

Technical Field

[0001] This application relates to enhanced external counterpulsation systems, and more particularly to an enhanced external counterpulsation adaptive control system. Background Technology

[0002] Enhanced external counterpulsation (EECP) is a non-invasive circulatory support technique used to treat ischemic diseases. It involves sequentially inflating the cuffs—one in the lower leg, one in the thigh, and one in the buttocks—from bottom to top during diastole, in response to synchronous R-wave signals from an electrocardiogram. This compresses the arterial system in the lower body, driving blood flow back to the upper body during diastole, improving perfusion to vital organs such as the heart and brain. Simultaneously, the compression of the venous system increases venous return from the right side of the heart, increasing stroke volume and cardiac output. During systole, the three cuffs deflate, reducing the resistance load on the heart's ejection. Currently, EECP is included in domestic and international guidelines for the diagnosis and treatment of ischemic heart disease and stroke, stable and unstable angina, acute myocardial infarction, cardiogenic shock, congestive heart failure, and stable coronary artery disease.

[0003] However, in current clinically used EECP devices, the inflation and deflation of the balloon are triggered by the R wave of the electrocardiogram signal. Specifically, the inflation and deflation processes are controlled at specific time intervals after the R wave peak. For different patients and conditions, the specific inflation pressure is generally controlled within the range of 0.020-0.045 MPa. Once these parameters are fixed, they remain unchanged throughout the entire EECP treatment. However, due to the variability of the heart rate and the changes in muscle and vascular characteristics caused by repeated inflation and deflation, the initial parameter settings cannot dynamically and adaptively adjust to track these physiological changes, thus affecting the treatment effect. Summary of the Invention

[0004] In view of the above problems, this application aims to propose an enhanced external counterpulsation (EECP) adaptive control system.

[0005] The enhanced external counterpulsation (EECP) adaptive control system of this application includes a multimodal physiological signal synchronous acquisition module and a signal analysis and processing module;

[0006] The multimodal physiological signal synchronous acquisition module is used to acquire synchronous multimodal physiological signals of the subject; the synchronous multimodal physiological signals include: single-lead or multi-lead electroencephalogram (EEG) signals and single-lead electrocardiogram (ECG) signals;

[0007] The signal analysis and processing module preprocesses the synchronous multimodal physiological signals, then performs feature point identification and joint feature point analysis to obtain physiological coupling indexes. These physiological coupling indexes are used to control the inflation and deflation of the airbags in the enhanced external counterpulsation device.

[0008] Preferably, the multimodal physiological signal synchronous acquisition module includes EEG electrodes and ECG electrodes; the EEG electrodes are used to acquire single-lead or multi-lead EEG signals; and the ECG electrodes are used to acquire single-lead ECG signals.

[0009] Preferably, the sampling frequency of the EEG electrodes and ECG electrodes is 500Hz.

[0010] Preferably, the signal analysis and processing module first applies a 50Hz notch filter to all acquired physiological signals to reduce the impact of power frequency interference.

[0011] The EEG signal was further processed through a 0.05-100Hz bandpass filter to obtain a bandpass-filtered EEG signal; the ECG signal was processed through a 0.05-100Hz bandpass filter, and the R wave and T wave were identified based on ECG characteristic points; taking the T wave peak position as the reference point, ECG and EEG segments 200ms before and 800ms after the reference point were taken; the 1000ms windowed EEG corresponding to the T wave was used as the cardiac evoked potential; the mean_P of the HEP curve amplitude within the time window of 250-450ms after EECP inflation and pressurization was taken;

[0012] mean_P is the physiological coupling index.

[0013] This application also proposes an enhanced external counterpulsation (EECP) adaptive control system, which includes a multimodal physiological signal synchronous acquisition module and a signal analysis and processing module;

[0014] The multimodal physiological signal synchronous acquisition module is used to acquire synchronous multimodal physiological signals of the subject; these synchronous multimodal physiological signals include: single-lead electrocardiogram signal, chest impedance signal, and volume pulse wave;

[0015] The signal analysis and processing module preprocesses the synchronous multimodal physiological signals, then performs feature point identification and joint feature point analysis to obtain physiological coupling indexes. These physiological coupling indexes are used to control the inflation and deflation of the airbags in the enhanced external counterpulsation device.

[0016] Preferably, the multimodal physiological signal synchronous acquisition module includes an electrocardiogram electrode, an impedance electrode, and a fingertip photoelectric sensor; the electrocardiogram electrode is used to acquire single-lead electrocardiogram signals; the impedance electrode is used to acquire chest impedance signals; and the fingertip photoelectric sensor is used to acquire volumetric pulse waves.

[0017] Preferably, the sampling frequency of the ECG electrode, impedance electrode, and fingertip photoelectric sensor is 500Hz.

[0018] Preferably, the signal analysis and processing module first applies a 50Hz notch filter to all acquired physiological signals to reduce the impact of power frequency interference.

[0019] After the electrocardiogram signal is processed by bandpass filtering from 0.05 to 100 Hz, the peak positions of the R wave and T wave in each cycle are determined by wavelet transform and the principle of maximum and minimum values. The interval between two adjacent R wave peaks is IBI. The chest impedance signal is processed by bandpass filtering from 0.1 to 0.8 Hz to obtain the respiratory RES. The volumetric pulse wave signal is further processed by bandpass filtering from 0.5 to 3.5 Hz, and the peak point of the volumetric pulse wave signal in each cycle is determined by the maximum point of its second-order difference signal.

[0020] Preferably, the time interval between the peak of the ECG R wave and the peak of the volumetric pulse wave within the same cycle is used as the pulse wave conduction time (PTT); the IBI, RES, and PTT time series are resampled at 5 Hz.

[0021] Preferably, the resampled IBI, RES, and PTT time series are processed using the empirical mode decomposition method to obtain the physiological coupling index PSI. total ,

[0022] PSI total =PSI RES-IBI +PSI RES-PTT +PSI IBI-PTT ;

[0023] Among them, PSI RES-IBI PSI is the phase synchronization index between RES and IBI. RES-PTT The phase coupling synchronization index between RES and PTT; PSI IBI-PTT This is the phase synchronization index between IBI and PTT.

[0024] The enhanced external counterpulsation (EECP) adaptive control system of this application can control EECP according to the multimodal physiological signal characteristics collected by the patient in real time, so that EECP adapts to physiological changes and achieves better therapeutic effect. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the adaptive control system and method for the EECP device in this application;

[0026] Figure 2 A schematic diagram illustrating multimodal physiological signal preprocessing and feature point recognition;

[0027] Figure 3 A physiological coupling analysis diagram of the cardiac, respiratory, and blood pressure systems;

[0028] Figure 4 This is a diagram illustrating the heart-brain coupling analysis.

[0029] Figure 5 This is a graph showing the relationship between EECP pressure parameters and physiological coupling parameters. Detailed Implementation

[0030] The enhanced external counterpulsation adaptive control system and method of this application will now be described in detail with reference to the accompanying drawings.

[0031] like Figure 1 As shown, the EECP parameter adaptive control system mainly consists of two parts: a multimodal physiological signal synchronous acquisition module and a signal analysis and processing module. During clinical EECP treatment, the multimodal physiological signal synchronous acquisition module utilizes photoelectric sensors (optional sensors for monitoring airbag pressure) placed on the head (EEG electrodes), chest and abdomen (ECG electrodes), and fingertips to non-invasively acquire single-lead or multi-lead EEG (one or more electrode positions can be located according to the 10-20 international standard lead system), single-lead ECG (standard limb lead II), chest impedance (4 electrodes), and volumetric pulse wave (index finger), among other multimodal physiological signals. The sampling rate is set to 500Hz.

[0032] like Figure 2 As shown, in the signal analysis and processing module, the physiological signal preprocessing module first applies a 50Hz notch filter to all acquired physiological signals to reduce the impact of power frequency interference. The EEG signal is further processed through a 0.05-100Hz bandpass filter to extract usable EEG signals; the ECG signal, after being processed through a 0.05-100Hz bandpass filter, uses wavelet transform and the principles of maxima and minima to determine the peak positions of the R-wave and T-wave in each cycle, with two adjacent R-wave peaks representing the intercardia-bi (IBI); the chest impedance signal, after being processed through a 0.1-0.8Hz bandpass filter, yields the respiratory signal RES; the volumetric pulse wave signal is further processed through a 0.5-3.5Hz bandpass filter, and the peak point of the volumetric pulse wave signal in each cycle is determined through the maxima of its second-order difference signal.

[0033] In the simultaneous multimodal physiological signal feature point joint analysis module, the time interval between the peak of the ECG R wave and the peak of the volumetric pulse wave within the same cycle is used as the pulse transit time (PTT). Based on the linear relationship between blood pressure and PTT, it can be used to characterize beat-by-beat blood pressure. The time series of intercardiac interval (IBI), respiratory respiration (RES), and pulse transit time (PTT) are resampled at 5 Hz.

[0034] For the resampled IBI, RES, and PTT time series, the empirical mode decomposition method is used, and each series can be represented as follows: The form where c i(t) is the intrinsic mode function, r n (t) represents the residual. With a resampling frequency of 5Hz, the r-th intrinsic mode function c of the IBI, RES, and PTT time series is selected. r (t) is used as the main component for extracting the above three time series. Figure 3 (A, B, C)

[0035] Based on the selection of the intrinsic mode functions of IBI, RES, and PTT time series as their respective principal rhythms, c is constructed using Hilbert transform. r Hilbert transform of (t) Where p is the Cauchy principal value. The c-value is obtained through the complex conjugate transformation pair. r (t) and Define the analytic signal z r (t), then in Thus, the Hilbert transform provides a unique instantaneous phase function Φ(t). The instantaneous phases of the primary rhythms of the selected intercardiac interval (IBI), respiratory respiration (RES), and pulse wave conduction time (PTT) sequences are Φ(t), respectively. IBI (t), Φ RES (t) and Φ PTT (t). The instantaneous phase difference between respiratory RES and cardiac interbeat IBI is The instantaneous phase difference between the respiratory RES and pulse wave arrival time PTT is The instantaneous phase difference between the intercardiac interval (IBI) and the pulse wave arrival time (PTT) is: like Figure 3 As shown in (D), (E), and (F), the coupling strength between the heart, respiration, and blood pressure can be quantified using the phase synchronization index (PSI), which is calculated as follows: in These represent the instantaneous phase difference between two time series of specific lengths. The mean of the cosine and sine values. Figure 3 The numbers (G), (H), and (I) are shown in order. The distribution of respiratory-cardiac, respiratory-blood pressure, and cardiac-blood pressure PSI values ​​are respectively PSI RES-IBI =0.63, PSI RES-PTT =0.53, PSI IBI-PTT =0.66. For example... Figure 3 As shown, the stronger the coupling and the better the synchronization between two signals, the larger their corresponding PSI value, and the more pronounced the peak will be in their distribution plot. Here, we define PSI... total =PSI RES-IBI +PSIRES-PTT +PSI IBI-PTT As an optional signal output for EECP parameter adaptive control.

[0036] The cardio-brain coupling strength was quantified by jointly analyzing preprocessed synchronized ECG and EEG data. Based on the identification of R and T waves as characteristic points on the ECG, the T wave peak position was located for each cycle, with the T wave peak position serving as the starting point for EECP inflation. Using the T wave peak position as a reference point, windowing was performed, extracting ECG and EEG segments 200ms before and 800ms after the reference point. Figure 2 As shown in (E) and (F), the 1000ms windowed EEG corresponding to the T wave is defined as heartbeat evoked potentials (HEP). The 1000ms windowed T wave and HEP of each cycle of a specific length of ECG and EEG signals are sequentially superimposed and averaged with a reference point to obtain the average values ​​of ECG and HEP, as shown in (E) and (F). Figure 4 As shown in Figures A and B. For HEP, a time window of 250-450ms is taken after EECP inflation and pressurization. The mean value of the HEP curve amplitude within the time window is calculated (the maximum amplitude of the positive inflection point can also be used as a potential quantifiable feature) to quantify the heart-brain coupling strength, which serves as an optional signal output for EECP parameter adaptive control.

[0037] Example

[0038] Using a clinically applicable EECP device, specific subjects underwent intervention. During the intervention, synchronous EEG, ECG, volumetric pulse wave, and electrical impedance signals were collected under pressure parameters of baseline (0 MPa without inflated cuff), 0.005 MPa, 0.010 MPa, 0.015 MPa, 0.020 MPa, 0.025 MPa, 0.030 MPa, 0.035 MPa, 0.040 MPa, 0.045 MPa, and 0.050 MPa. The immediate effect of EECP on physiological coupling strength was analyzed. Signal preprocessing was performed using the above method to obtain preprocessed EEG, ECG, volumetric pulse wave, and respiratory signals. Feature points were identified, and coupling analysis was performed using the same method to obtain the physiological coupling index PSI. total And mean_P. Analyze PSI under different EECP pressures. total And mean_P, such as Figure 5 As shown, at an EECP pressure of 0.020 MPa, the physiological coupling index PSI of the subjects was... totalBoth mean P and mean P reached their maximum values, indicating that EECP at 0.020 MPa can have a positive intervention effect on the subjects. In clinical practice, the physiological coupling index PSI is extracted by real-time acquisition and analysis of multimodal physiological signals such as simultaneous EEG, ECG, volume pulse wave, and respiration during EECP. total The mean_P parameter can be used for adaptive feedback control of EECP pressure parameters to optimize and improve the current clinical EECP treatment mode with fixed pressure parameters.

[0039] Unless otherwise defined, all technical and / or scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this invention relates. The materials, methods, and embodiments mentioned in this application are illustrative only and not restrictive.

[0040] Although the present invention has been described in conjunction with specific embodiments, those skilled in the art can make appropriate substitutions, modifications and changes within the inventive spirit of this application, and such substitutions, modifications and changes still fall within the protection scope of this application.

Claims

1. An enhanced external counterpulsation (EECP) adaptive control system, comprising a multimodal physiological signal synchronous acquisition module and a signal analysis and processing module; The multimodal physiological signal synchronous acquisition module is used to acquire synchronous multimodal physiological signals from the subject. The synchronous multimodal physiological signals include: single-lead or multi-lead electroencephalogram (EEG) signals and single-lead electrocardiogram (ECG) signals; The signal analysis and processing module preprocesses the synchronous multimodal physiological signals, then performs feature point identification and joint feature point analysis to obtain physiological coupling indexes. The inflation and deflation pressure parameters of the airbag of the enhanced external counterpulsation device are controlled by the adaptive feedback of these physiological coupling indexes. The multimodal physiological signal synchronous acquisition module includes EEG electrodes and ECG electrodes; the EEG electrodes are used to acquire single-lead or multi-lead EEG signals; the ECG electrodes are used to acquire single-lead ECG signals. The sampling frequency of the EEG electrodes and ECG electrodes is 500Hz; The signal analysis and processing module first applies a 50Hz notch filter to all acquired physiological signals to reduce the impact of power frequency interference. The EEG signal was further processed through a 0.05-100Hz bandpass filter to obtain a bandpass-filtered EEG signal. The ECG signal, after being processed through a 0.05-100Hz bandpass filter, was used to identify the R and T waves based on ECG characteristic points. Using the T wave peak position as a reference point, ECG and EEG segments were taken 200ms before and 800ms after the reference point. The 1000ms EEG segment corresponding to the T wave was used as the cardiac evoked potential. The mean amplitude of the cardiac evoked potential curve was taken within a time window of 250-450ms after the balloon was inflated and pressurized. mean_P ; mean_P This refers to the physiological coupling index.

2. An enhanced external counterpulsation (EECP) adaptive control system, comprising a multimodal physiological signal synchronous acquisition module and a signal analysis and processing module; The multimodal physiological signal synchronous acquisition module is used to acquire synchronous multimodal physiological signals from the subject. The synchronized multimodal physiological signals include: single-lead electrocardiogram signal, chest impedance signal, and volumetric pulse wave; The signal analysis and processing module preprocesses the synchronous multimodal physiological signals, then performs feature point identification and joint feature point analysis to obtain physiological coupling indexes. The inflation and deflation pressure parameters of the airbag of the enhanced external counterpulsation device are controlled by the adaptive feedback of these physiological coupling indexes. The multimodal physiological signal synchronous acquisition module includes ECG electrodes, impedance electrodes, and fingertip photoelectric sensors; the ECG electrodes are used to acquire single-lead ECG signals; the impedance electrodes are used to acquire chest impedance signals; and the fingertip photoelectric sensors are used to acquire volumetric pulse waves. The sampling frequency of the ECG electrodes, impedance electrodes, and fingertip photoelectric sensors is 500Hz; The signal analysis and processing module first applies a 50Hz notch filter to all acquired physiological signals to reduce the impact of power frequency interference. After the electrocardiogram signal is processed by bandpass filtering from 0.05 to 100 Hz, the peak positions of the R wave and T wave in each cycle are determined by wavelet transform and the principle of maximum and minimum values. The interval between two adjacent R wave peaks is IBI. The chest impedance signal is processed by bandpass filtering from 0.1 to 0.8 Hz to obtain the respiratory RES. The volumetric pulse wave signal is further processed by bandpass filtering from 0.5 to 3.5 Hz, and the peak point of the volumetric pulse wave signal in each cycle is determined by the maximum point of its second-order difference signal. The time interval between the peak of the ECG R wave and the peak of the volumetric pulse wave within the same cycle is used as the pulse wave conduction time (PTT); the IBI, RES, and PTT time series are resampled at 5 Hz. The resampled IBI, RES, and PTT time series were processed using the empirical mode decomposition method to obtain physiological coupling indices. PSI total , PSI total =PSI RES-IBI +PSI RES-PTT +PSI IBI-PTT ； in, PSI RES-IBI This is the phase synchronization index between RES and IBI. PSI RES-PTT The phase coupling synchronization index between RES and PTT; PSI IBI-PTT This is the phase synchronization index between IBI and PTT.

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