System and method for dynamic augmentation of in-vitro cardiac contractility with bimodal sensing of r-wave peaks

Abstract

This invention relates to the fields of medical device technology and signal processing algorithm technology. Specifically, it relates to an in vitro cardiac contractility dynamic enhancement system and method based on R-wave peak dual-modal sensing. The problem addressed by this invention is the technical issues of poor safety, high oxygen consumption, insufficient precision, and lack of automated closed-loop control in isolated cardiac mechanical perfusion methods. To solve these problems, this invention provides an in vitro cardiac contractility dynamic enhancement method based on R-wave peak dual-modal sensing, comprising: obtaining an initial threshold and an initial R-wave template library based on real-time cardiac physiological signals within an initial target time period; obtaining the true R-wave peak based on waveform verification of candidate R-wave peaks within the target time period and the initial threshold; calculating the stimulation delay based on species-preset parameters and real-time cardiac physiological signals; and selecting the stimulation mode and adjusting the energy parameters based on changes in the real-time cardiac physiological signals.

Description

Technical Field

[0001] This invention relates to the fields of medical device technology and signal processing algorithm technology, and in particular to an in vitro dynamic enhancement system and method for cardiac contractility based on R-wave peak dual-modal sensing. Background Technology

[0002] Heart transplantation is an effective treatment for end-stage heart failure. With the increasing awareness of organ donation and the improved utilization rate of marginal donor hearts, donor heart quality assessment and repair techniques have become crucial for ensuring transplant success. Ejection capacity, as one of the core functional indicators of the heart, is closely related to myocardial contractility. During donor heart retrieval, transport, and mechanical perfusion, some hearts experience a significant decrease in contractility due to ischemia-reperfusion injury, myocardial stunning, or underlying lesions. If timely and effective intervention is not provided, these hearts will fail the assessment and be discarded, exacerbating the organ shortage crisis.

[0003] Currently, methods to enhance the contractility of isolated mechanically perfused hearts mainly include pharmacological intervention (such as β-adrenergic receptor agonists) and mechanical assistance (such as intra-aortic balloon counterpulsation). However, pharmacological intervention increases myocardial oxygen consumption, exacerbates metabolic burden, and may induce arrhythmias; mechanical assistance devices are complex in structure, expensive, and difficult to deploy in portable organ transport boxes. In addition, existing technologies lack the ability to sense and synchronize the heart's own electrophysiological rhythm in real time, and the stimulation delay is out of sync with the cardiac cycle, which can easily induce malignant arrhythmias.

[0004] The R-wave peak on an electrocardiogram (ECG) represents the onset of ventricular depolarization, followed by an electrophysiological window known as the "absolute refractory period." During this period, cardiomyocytes are in a depolarized state, and no matter how strong the stimulus, it cannot be induced to excite them again; therefore, it is a "safe period" for applying external energy intervention. Studies have shown that applying a high-energy, short-duration electrical stimulation pulse at the end of the absolute refractory period can enhance the mechanical output of the next contraction without increasing myocardial oxygen consumption by affecting the dynamics of calcium ion channels and myofilament sensitivity in cardiomyocytes. This phenomenon is known as the "refractory period stimulation enhancement effect." However, implementing this technology in mechanically perfused hearts faces the following challenges: First, ECG signals from isolated hearts are easily affected by factors such as perfusion fluid flow, mechanical vibration, and electromagnetic interference, resulting in a low signal-to-noise ratio and decreased accuracy of traditional R-peak detection algorithms (such as the Pan-Tompkins algorithm). Second, cardiac electrophysiological parameters (such as heart rate and refractory period duration) vary significantly among different species and individuals, requiring dynamic adjustment of stimulation delay. Third, stimulation energy, pulse width, and electrode position need precise optimization to avoid myocardial damage. Fourth, fully automated closed-loop control is required to ensure that the time delay from signal acquisition to stimulation output is less than the safe window period, which places extremely high demands on the real-time performance of the algorithm. Existing technologies lack an integrated solution and cannot achieve closed-loop enhancement of "sensing-decision-stimulation-feedback" during mechanical perfusion. Therefore, there is an urgent need to develop an electrical stimulation-assisted method based on accurate R-peak identification, using intelligent algorithms and highly reliable hardware systems to dynamically enhance the contractility of marginal donor hearts without increasing oxygen consumption, thereby improving donor heart utilization and transplant prognosis. Summary of the Invention

[0005] The problem solved by this invention is to address the technical issues of poor safety, high oxygen consumption, insufficient precision, and lack of automated closed-loop control in the mechanical perfusion of isolated hearts, which are problems associated with methods for enhancing contractility.

[0006] To address the aforementioned issues, this invention provides an in vitro dynamic enhancement method for cardiac contractility based on dual-modal R-wave peak sensing. The dynamic enhancement method includes: acquiring real-time cardiac physiological signals of the target heart; obtaining an initial threshold and an initial R-wave template library based on the real-time cardiac physiological signals within an initial target time period; filtering the real-time cardiac physiological signals based on the initial threshold and the initial R-wave template library to obtain candidate R-wave peaks; obtaining true R-wave peaks based on waveform verification of the candidate R-wave peaks within the target time period and the initial threshold; calculating the stimulation delay based on species-preset parameters and the real-time cardiac physiological signals when true R-wave peaks appear, and selecting the stimulation mode and adjusting energy parameters based on changes in the real-time cardiac physiological signals; monitoring the number of true R-wave peaks in real-time; and updating the initial threshold and the initial R-wave template library when the number of true R-wave peaks reaches a threshold, obtaining an updated threshold and an updated R-wave template library.

[0007] Compared with existing technologies, the technical effects achieved by this solution are as follows: Obtaining an initial threshold and initial R-wave template library based on real-time cardiac physiological signals within the initial target time period helps establish a detection threshold based on individual signals, making the R-wave peak more realistic. Obtaining candidate R-wave peaks helps to initially filter out a large number of non-R-wave peak signals, narrowing the target detection range. Obtaining the true R-wave peak based on the waveform verification of candidate R-wave peaks within the target time period and the initial threshold helps to eliminate false R-wave peaks (such as false peaks caused by electromyographic interference or baseline drift), significantly improving the accuracy of R-wave peak detection. The calculation of stimulation delay helps to accurately control the timing of stimulation, allowing stimulation to act on the optimal physiological stage of the heart and maximizing the effect of contractile enhancement. Selecting the stimulation mode and adjusting energy parameters based on changes in real-time cardiac physiological signals achieves dynamic matching between the stimulation scheme and the real-time cardiac state, avoiding ineffective regulation or myocardial damage caused by fixed parameters. The setting of the quantity threshold provides a triggering mechanism and data basis for updating the initial threshold and initial R-wave template library, ensuring that the update is based on a sufficient number of new samples, making the updated parameters statistically significant and reliable.

[0008] In one embodiment of the present invention, real-time cardiac physiological signals of the target heart are acquired, and an initial threshold and an initial R-wave template library are obtained based on the real-time cardiac physiological signals within an initial target time period. Specifically, this includes: obtaining a real-time ECG signal based on the real-time cardiac physiological signals; preprocessing the real-time ECG signal using wavelet transform to obtain an initial ECG signal; enhancing the waveform characteristics of the initial ECG signal using an energy operator to obtain an enhanced ECG signal; obtaining a target band based on the enhanced ECG signal within the initial target time period; obtaining an initial peak threshold based on the energy characteristics of the target band; obtaining an average noise level based on the target band and the enhanced ECG signal; obtaining an initial slope threshold based on the average noise level; obtaining an initial threshold based on the initial slope threshold and the initial peak threshold; and filtering the target band based on the initial threshold to obtain an initial R-wave template library.

[0009] Compared with existing technologies, the technical effects achieved by this solution are as follows: wavelet transform can effectively filter out irrelevant signals such as electromyography interference, power frequency noise, or baseline drift, improve the signal-to-noise ratio of ECG signals, prevent noise from masking the true R-wave peak characteristics, enhance waveform characteristics through energy operators, help strengthen the amplitude difference and morphological differentiation between the R-wave peak and surrounding bands, making the R-wave peak characteristics more prominent, obtain the initial peak threshold based on the energy characteristics of the target band, making the initial peak value more consistent with the actual situation of the target heart, calculate the average noise level to help quantify the signal noise intensity, provide an objective basis for the slope threshold, construct the initial slope threshold to further distinguish the steep rising edge characteristics of the R-wave peak from the smooth fluctuations of noise, improve the accuracy of the initial slope threshold determination, and screen and construct the initial R-wave template library to provide a standard morphological reference for subsequent R-wave peak comparison.

[0010] In one embodiment of the present invention, candidate R-wave peaks are obtained by screening real-time cardiac physiological signals based on an initial threshold and an initial R-wave template library. Specifically, this includes: obtaining a signal to be matched based on the enhanced ECG signal of the current acquisition point, calculating the similarity between the signal to be matched and the initial R-wave template library; and when the similarity is greater than the similarity threshold and the signal energy of the current acquisition point exceeds the initial peak value threshold, the current acquisition point is recorded as a candidate R-wave peak.

[0011] Compared with existing technologies, the technical effects achieved by this solution are as follows: using the initial R-wave template library as a standard morphological reference, noise waves and non-R-wave peaks with inconsistent morphology are initially excluded. The dual-condition judgment of similarity and signal energy requires both the waveform to conform to the characteristics of R-wave peaks and the energy to reach the peak threshold, which greatly reduces the probability of false R-wave peaks being selected and further improves the detection speed and accuracy of true R-wave peaks.

[0012] In one embodiment of the present invention, the true R-wave peak is obtained based on the waveform verification of the candidate R-wave peak within the target time period and the initial threshold. Specifically, this includes: recording the enhanced ECG signal of the candidate R-wave peak within the target time period as the target signal, and performing waveform verification on the target signal. When the waveform verification meets the verification standard, the slope of the candidate R-wave peak is calculated. When the slope of the candidate R-wave peak is greater than the initial slope threshold and the candidate R-wave peak is the maximum value of the target signal, the candidate R-wave peak is recorded as the true R-wave peak.

[0013] Compared with existing technologies, the technical effects achieved by adopting this technical solution are as follows: Waveform verification helps to strengthen the morphological specificity screening of R-wave peaks, making the selected real R-wave peaks more consistent with physiological characteristics in terms of waveform structure, thus improving the specificity of R-wave peak detection. The initial slope threshold helps to further distinguish noise (smooth fluctuations, low slope) from real R-wave peaks. By locking the core signal point with the highest amplitude within the target time period, it matches the physiological nature of R-wave peaks as high-amplitude bands in ECG signals.

[0014] In one embodiment of the present invention, when the actual R-wave peak appears, the stimulation delay is calculated based on species-preset parameters and real-time cardiac physiological signals, and the stimulation mode and energy parameters are selected and adjusted according to the changes in real-time cardiac physiological signals. Specifically, this includes: obtaining the average heart rate based on the real-time cardiac physiological signals, and calculating the stimulation delay based on the average heart rate and species-preset parameters. The formula for calculating the stimulation delay is as follows:

[0015] D = k×RR mean + C;

[0016] Where D is the time required for stimulus delay, and RR meanThe mean of the RR interval is given, and k and C are pre-defined parameters for the species, which were optimized and determined after statistical analysis of a large amount of experimental data.

[0017] The stimulation mode and energy parameters are selected and adjusted based on changes in real-time cardiac physiological signals.

[0018] Compared with existing technologies, the technical effects achieved by this solution are as follows: using the mean RR interval as the core physiological indicator and combining it with species-specific preset parameters to lock in the optimal timing of stimulation helps to adapt to the differences in cardiac physiological rhythms among different species. At the same time, it responds to real-time heart rate fluctuations of the same target, avoids stimulation "peak misalignment" caused by fixed delays, ensures that stimulation acts on the key stage of myocardial contraction, maximizes the efficiency of contractility regulation, selects stimulation modes based on real-time signal changes, helps to avoid the inadequacy of a single stimulation mode for different cardiac states, makes the stimulation strategy more in line with physiological needs, and reduces the safety risks of electrical stimulation by adjusting energy parameters based on real-time signals.

[0019] In one embodiment of the present invention, selecting the stimulation mode and adjusting the energy parameters based on changes in real-time cardiac physiological signals specifically includes: real-time monitoring of the myocardial oxygen consumption index of the target heart, and selecting the stimulation mode based on changes in the myocardial oxygen consumption index; when the myocardial oxygen consumption index is greater than the index threshold, an intermittent enhancement mode is used, and when the myocardial oxygen consumption index is less than the index threshold, a single-pulse enhancement mode is used; real-time monitoring of the increase in contractility of the target heart after a number of stimulations, and adjusting the energy parameters based on the increase in contractility; when the increase in contractility is less than a first amplitude threshold, the stimulation voltage is increased, and when the increase in contractility is greater than a second amplitude threshold, the stimulation voltage is decreased.

[0020] Compared with existing technologies, the technical effects achieved by this solution are as follows: using myocardial oxygen consumption index as the core monitoring indicator, it is directly related to cardiac metabolic load, avoiding excessive myocardial oxygen consumption caused by stimulation; using the increase in contractility as feedback basis, it realizes closed-loop regulation of energy parameters, which helps to avoid problems of insufficient energy and energy excess damage.

[0021] In one embodiment of the present invention, the number of true R-wave peaks is monitored in real time. When the number of true R-wave peaks reaches a threshold, the initial threshold and the initial R-wave template library are updated to obtain an updated threshold and an updated R-wave template library. True R-wave peaks are then identified based on the updated threshold and the updated R-wave template library. Specifically, this includes: when the number of true R-wave peaks reaches the threshold, grouping the batch of true R-wave peaks into associated R-wave peak groups; dynamically adjusting the initial peak threshold based on the associated R-wave peak groups to obtain an updated peak threshold; filtering enhanced ECG signals based on the updated peak threshold to obtain an updated R-wave template library; recalculating the mean RR interval based on the associated R-wave peak groups to obtain an updated mean RR interval; and adjusting the subsequent stimulation delay based on the mean RR interval.

[0022] Compared with existing technologies, the technical effects achieved by this solution are as follows: Based on the actual R-wave peak grouping of the qualified number, the peak threshold is dynamically adjusted according to the latest actual signal characteristics, which helps to avoid the initial threshold from becoming invalid due to changes in the heart state. The template is reconstructed based on the latest R-wave peaks selected by updating the peak threshold, so that the template fits the current R-wave peak morphology characteristics of the heart and replaces the template that may have been biased in the initial stage. The mean RR interval is recalculated based on the latest associated R-wave peak group, so that the calculation basis of stimulation delay closely follows the real-time rhythm changes of the heart and avoids the stimulation timing deviation caused by the RR interval mean becoming outdated.

[0023] In one embodiment of the present invention, an in vitro cardiac contractility dynamic enhancement system with R-wave peak dual-modal sensing is also provided. The in vitro cardiac contractility dynamic enhancement method with R-wave peak dual-modal sensing described in the above embodiment is applied to this dynamic enhancement system. The dynamic enhancement system includes: a data acquisition module for acquiring real-time cardiac physiological signals; a data analysis module for analyzing real-time cardiac physiological signals and obtaining the true R-wave peak; and an intelligent decision-making module for selecting stimulation modes and adjusting energy parameters. This dynamic enhancement system has all the technical features of the above-mentioned dynamic enhancement method, which will not be described in detail here. Attached Figure Description

[0024] Figure 1 This is one of the flowcharts for the in vitro dynamic enhancement method of cardiac contractility based on dual-modal sensing of R-wave peaks according to the present invention;

[0025] Figure 2 This is the second flowchart of the in vitro dynamic enhancement method for R-wave peak dual-modal sensing of cardiac contractility according to the present invention.

[0026] Figure 3 This is the third flowchart of the in vitro dynamic enhancement method for R-wave peak dual-modal sensing of cardiac contractility according to the present invention.

[0027] Figure 4This is the fourth flowchart of the in vitro dynamic enhancement method for R-wave peak dual-modal sensing of cardiac contractility according to the present invention.

[0028] Figure 5 This is a schematic diagram of the in vitro cardiac contractility dynamic enhancement system with R-wave peak dual-modal sensing according to the present invention.

[0029] Explanation of reference numerals in the attached figures:

[0030] 100 - Dynamic enhancement system; 110 - Data acquisition module; 120 - Data analysis module; 130 - Intelligent decision-making module. Detailed Implementation

[0031] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0032] [First Embodiment]

[0033] See Figure 1 In one specific embodiment, the present invention provides an in vitro dynamic enhancement method for cardiac contractility based on dual-modal sensing of R-wave peaks, the dynamic enhancement method comprising:

[0034] S100: Collect real-time cardiac physiological signals of the target heart, and obtain the initial threshold and initial R-wave template library based on the real-time cardiac physiological signals within the initial target time period;

[0035] S200. Based on the initial threshold and the initial R-wave template library, candidate R-wave peaks are obtained by screening real-time cardiac physiological signals.

[0036] S300. Obtain the true R-wave peak based on the waveform verification of the candidate R-wave peak within the target time period and the initial threshold.

[0037] S400: When the actual R-wave peak appears, the stimulation delay is calculated based on the species preset parameters and real-time cardiac physiological signals, and the stimulation mode and energy parameters are selected and adjusted according to the changes in real-time cardiac physiological signals.

[0038] S500: Real-time monitoring of the number of real R-wave peaks. When the number of real R-wave peaks reaches the number threshold, the initial threshold and the initial R-wave template library are updated to obtain the updated threshold and the updated R-wave template library.

[0039] In step S100, the target heart typically refers to an isolated heart used for mechanical perfusion, such as the isolated hearts of experimental animals like rats and pigs. Real-time cardiac physiological signals include electrophysiological signals (such as surface electrocardiogram), mechanical function signals (such as intraventricular pressure, systolic / diastolic tension, and heart rate), and metabolic and vitality signals (myocardial oxygen consumption index and lactate concentration). For example, a low-noise, high-input-impedance bioelectric amplifier is used to acquire ECG signals from the surface of the isolated heart in real time at a sampling rate of not less than 10 kHz through Ag / AgCl electrodes or flexible dry electrodes attached to the interventricular septum and apex of the mechanically perfused heart.

[0040] Generally, the initial target time period refers to the period immediately following the start of ECG signal acquisition after the experiment begins. For example, the first 2 seconds of the experiment can be used as the initial target time period. The initial thresholds include the initial peak threshold and the initial slope threshold. The R-wave peak can be identified by the initial threshold and the initial R-wave template library. The initial threshold and the initial R-wave template library can be obtained through the ECG signal on the surface of the heart in real-time cardiac physiological signals. For example, after the ECG signal is hardware filtered (0.5-100Hz bandpass filter) and subjected to a 50 / 60 Hz power frequency notch, it is input into a digital signal processor (DSP) or embedded microcontroller (MCU). The core algorithm runs in the DSP / MCU or host computer to perform real-time R-wave peak detection on the digitized ECG signal.

[0041] In step S200, an enhanced ECG signal segment is extracted from the real-time cardiac physiological signal as the signal to be matched. The signal to be matched is compared one by one with multiple standard R-wave peak templates in the initial R-wave template library. The waveform matching degree is quantified by calculating similarity index (such as correlation coefficient or Euclidean distance). When the similarity exceeds a preset threshold and the energy amplitude of the signal point reaches the initial peak threshold, the signal point is marked as a candidate R-wave peak. For example, an R-wave template library with a length of 200ms is maintained. For each new sampling point, its normalized cross-correlation (NCC) with the template is calculated. When NCC > 0.85 and the signal energy of the current sampling point exceeds the initial peak threshold, it is marked as a candidate R-wave peak.

[0042] In step S300, generally speaking, each R-wave peak has the initial characteristics of a complete QRS complex wave. Waveform verification can filter out single pulse noise that does not have a prior transition and suddenly rises from the baseline. It can also filter out slow drift caused by electrode movement, certain types of power frequency interference, and the situation where the T wave is mistakenly amplified. For example, for each candidate R-wave peak, search forward 80 ms to see if there is a zero crossing point, and verify whether the signal returns to the baseline within the next 150 ms. If both are met, proceed to the next verification stage.

[0043] The R-wave peak is the point with the highest amplitude of the R-wave. Its core features are concentrated in four dimensions: amplitude, slope, position, and timing. Among them, the amplitude feature is the core indicator of the ECG signal strength, and the R-wave peak is the point with the highest amplitude in the ECG signal. The slope feature is the key manifestation of the steep shape. The rising edge from the trough to the R-wave peak is extremely steep, which is the part with the steepest slope in the ECG. The falling edge from the R-wave peak to the subsequent trough is also steep. The absolute value of the slope is close to the slope of the rising branch. After the candidate R-wave peak passes the waveform verification, its slope and amplitude are verified. When its slope is greater than the initial slope threshold and the amplitude is at its maximum value, the true R-wave peak is obtained.

[0044] In step S400, the R-wave peak represents the beginning of ventricular depolarization, followed by an electrophysiological window called the "absolute refractory period" (usually tens to hundreds of milliseconds after the R-wave, varying among different species). During this period, cardiomyocytes are in a depolarized state, and no matter how strong the stimulus, it cannot be induced to excite again. Therefore, it is a "safe period" for applying external energy intervention. Typically, applying a high-energy, short-duration electrical stimulation pulse at the end of the absolute refractory period can enhance the mechanical output of the next contraction without increasing myocardial oxygen consumption by affecting the dynamics of calcium ion channels and myofibril sensitivity in cardiomyocytes. This phenomenon is called the "refractory period stimulation enhancement effect".

[0045] Stimulation delay refers to the time required from the appearance of the R-wave peak to the point where electrical stimulation is needed, ensuring that the stimulation falls precisely within the optimal stimulation window. Generally speaking, the cardiac electrophysiological cycles vary greatly among different species. For example, rats have a fast heart rate (250-350 bpm) and an electrophysiological cycle of only 170-240 ms, while rabbits have a slower heart rate (120-180 bpm) and a cycle of approximately 330-500 ms. Therefore, their stimulation delays are completely different. In addition, the cardiac state can change in different individuals of the same species and at different stages of the same experiment. Therefore, it is necessary to calculate the stimulation delay based on heart rate fluctuations and species-preset parameters.

[0046] In step S500, under normal circumstances, as the experiment continues, the physiological state of the target heart may change, and the initial threshold and initial template library may no longer be fully applicable. For example, after a period of stimulation, the electrophysiological characteristics of the target heart may change, leading to deviations in the morphology, amplitude, and slope of the R-wave peak. In this case, if the initial threshold and initial template library are continued for identification, misjudgment or missed judgment may occur. Therefore, the system can count the number of detected real R-wave peaks in real time. When the cumulative number reaches a preset threshold, an update mechanism can be automatically triggered to dynamically update and adjust the initial threshold and initial R-wave template library. Specifically, the system will recalculate the peak value threshold and slope threshold based on the newly identified real R-wave peak characteristics to obtain the updated threshold. At the same time, a new template library is constructed based on these real R-wave peaks, that is, the R-wave template library is updated. For example, the initial peak value threshold is dynamically readjusted every 5 real R-wave peaks detected.

[0047] The initial threshold and initial R-wave template library are obtained based on real-time cardiac physiological signals within the initial target time period. This helps to establish a detection threshold based on individual signals, making the R-wave peak more realistic. The acquisition of candidate R-wave peaks helps to initially filter out a large number of non-R-wave peak signals, narrowing the target detection range. The waveform verification of candidate R-wave peaks within the target time period and the initial threshold are used to obtain the true R-wave peak, which helps to eliminate false R-wave peaks (such as false peaks caused by electromyographic interference and baseline drift), significantly improving the accuracy of R-wave peak detection. The calculation of stimulation delay helps to accurately control the timing of stimulation, allowing stimulation to act on the optimal physiological stage of the heart and maximizing the effect of contractile enhancement. The selection of stimulation mode and adjustment of energy parameters based on changes in real-time cardiac physiological signals achieves dynamic matching between the stimulation scheme and the real-time cardiac state, avoiding ineffective regulation or myocardial damage caused by fixed parameters. The setting of the quantity threshold provides a triggering mechanism and data basis for updating the initial threshold and the initial R-wave template library, ensuring that the update is based on a sufficient number of new samples, making the updated parameters statistically significant and reliable.

[0048] [Second Embodiment]

[0049] See Figure 2 In one specific embodiment, real-time cardiac physiological signals of the target heart are acquired, and an initial threshold and an initial R-wave template library are obtained based on the real-time cardiac physiological signals within the initial target time period. Specifically, this includes:

[0050] S110. Obtain the real-time ECG signal based on the real-time cardiac physiological signal, and preprocess the real-time ECG signal through wavelet transform to obtain the initial ECG signal.

[0051] S120. Enhance the waveform characteristics of the initial ECG signal by using the energy operator to obtain the enhanced ECG signal, and obtain the target band based on the enhanced ECG signal within the initial target time period;

[0052] S130. Obtain the initial peak threshold based on the energy characteristics of the target band, and obtain the average noise level based on the target band and the enhanced ECG signal;

[0053] S140. Obtain the initial slope threshold based on the average noise level, and obtain the initial threshold based on the initial slope threshold and the initial peak threshold.

[0054] S150. Filter the target bands according to the initial threshold to obtain the initial R-wave template library.

[0055] In step S110, wavelet transform, as a time-frequency analysis method, can effectively decompose a signal into different frequency components. When processing ECG signals, appropriate wavelet basis functions and decomposition levels can be selected based on the frequency characteristics of different bands (such as P-wave, QRS complex, T-wave, etc.) in the ECG signal. For example, the Daubechies 4th order wavelet transform is used to perform multi-scale decomposition on the original ECG signal, reconstruct the detail coefficients of the 3rd to 5th levels to suppress baseline drift and high-frequency noise, and retain the core frequency band of the QRS complex wave to obtain the initial ECG signal.

[0056] In step S120, the waveform characteristics of the initial ECG signal are enhanced by an energy operator to obtain an enhanced ECG signal. The energy operator (such as the Teager energy operator) can highlight the instantaneous energy changes of the signal, and is especially suitable for enhancing the steep rising and falling edges of the R-wave peak in the ECG signal. For example, the Teager energy operator is applied to each sampling point of the initial ECG signal to calculate its instantaneous energy value and generate an energy-enhanced ECG signal. In the enhanced signal, the amplitude and slope characteristics of the R-wave peak are more obvious, which facilitates the subsequent target band extraction and threshold calculation. Based on the enhanced ECG signal within the initial target time period, the target band is obtained. The target band refers to the main frequency range containing the R-wave peak characteristics, which usually corresponds to the frequency band of the QRS complex wave. For example, by analyzing the power spectral density of the enhanced ECG signal, the main frequency range of the QRS group (such as 10-30Hz) is determined, and the signal within this frequency band is extracted as the target band, providing a focused signal range for subsequent threshold calculation and template library construction.

[0057] In steps S130 and S140, an initial peak threshold is obtained based on the energy characteristics of the target band. The energy characteristics of the target band reflect the amplitude distribution of the R-wave peak. For example, the 95th percentile of the energy envelope is calculated as the initial peak threshold. This initial peak threshold is used for amplitude judgment when screening candidate R-wave peaks. The average noise level is the slope statistical mean of pure interference without physiologically effective waveforms in the enhanced ECG signal. It reflects the typical fluctuation intensity of the enhanced ECG signal other than the target band and can be obtained through statistical analysis of the enhanced ECG signal.

[0058] The slope threshold is used to determine whether the rising and falling edges of the R-wave peak are steep enough. The average noise level is multiplied by an empirical coefficient to obtain the initial slope threshold. This initial slope threshold ensures that only signal points with a slope significantly higher than the noise level are considered as candidate points of the R-wave peak. For example, the average noise level is multiplied by a coefficient of 1.5 as the initial slope threshold. The initial threshold is obtained based on the initial slope threshold and the initial peak threshold. The initial threshold is a judgment criterion that combines amplitude and slope characteristics.

[0059] In step S150, the initial R-wave template library is a set of standard waveforms used for subsequent R-wave peak identification. For example, all ECG signal segments that meet the initial threshold conditions are extracted from the target band. These segments are aligned (e.g., based on the R-wave peak) and normalized (e.g., amplitude normalized to the [0,1] range). Then, a set of representative R-wave peak templates are generated by clustering algorithms (e.g., K-means clustering) or averaging operations and stored in the initial R-wave template library. This template library provides a benchmark reference for subsequent real-time R-wave peak identification.

[0060] Wavelet transform can effectively filter out irrelevant signals such as electromyography interference, power frequency noise, or baseline drift, improve the signal-to-noise ratio of ECG signals, prevent noise from masking the true R-wave peak characteristics, enhance waveform characteristics through energy operators, help strengthen the amplitude difference and morphological distinction between the R-wave peak and surrounding bands, making the R-wave peak characteristics more prominent, obtain the initial peak threshold based on the energy characteristics of the target band, making the initial peak value more consistent with the actual situation of the target heart, calculate the average noise level to help quantify the signal noise intensity, provide an objective basis for the slope threshold, construct the initial slope threshold to further distinguish the steep rising edge characteristics of the R-wave peak from the smooth fluctuations of noise, improve the accuracy of the initial slope threshold determination, and screen and construct the initial R-wave template library to provide a standard morphological reference for subsequent R-wave peak comparison.

[0061] [Third Embodiment]

[0062] In one specific embodiment, candidate R-wave peaks are obtained by screening real-time cardiac physiological signals based on an initial threshold and an initial R-wave template library, specifically including:

[0063] S210. Obtain the signal to be matched based on the enhanced ECG signal at the current acquisition point, and calculate the similarity between the signal to be matched and the initial R-wave template library;

[0064] S220. When the similarity is greater than the similarity threshold and the signal energy of the current acquisition point exceeds the initial peak threshold, the current acquisition point is recorded as a candidate R-wave peak.

[0065] In step S210, the signal to be matched is usually a segment of signal with a fixed length that includes the current acquisition point, extracted from the enhanced ECG signal. For example, the signal 50ms before and after the current acquisition point is extracted as the signal to be matched. Similarity calculation is a key step in measuring the degree of morphological similarity between the signal to be matched and each template in the initial R-wave template library. Various similarity measurement methods can be used, such as correlation coefficient, Euclidean distance, or dynamic time warping distance. Taking the correlation coefficient as an example, the correlation coefficient between the signal to be matched and each template is calculated. The value of the correlation coefficient is between [-1, 1]. The closer the value is to 1, the more similar the two are in shape. By calculating the correlation coefficient between the signal to be matched and all templates in the initial R-wave template library, a set of similarity values ​​is obtained.

[0066] In step S220, the similarity threshold is a pre-set standard value used to determine whether the signal to be matched is sufficiently similar to the template. For example, the similarity threshold is set to 0.8. Only when the correlation coefficient between the signal to be matched and a certain template is greater than 0.8 is the signal to be matched considered to be similar to the template in shape. The signal energy reflects the intensity of the signal and can be obtained by calculating the energy value of the signal to be matched. The initial peak threshold is determined based on the energy characteristics of the target band within the initial target time period and is used to determine whether the signal amplitude reaches the level of the R-wave peak. Only when the signal energy of the current acquisition point exceeds the initial peak threshold is the acquisition point considered to be a possible R-wave peak. By simultaneously satisfying the two conditions of similarity greater than the similarity threshold and signal energy exceeding the initial peak threshold, candidate R-wave peaks can be effectively screened out, reducing the possibility of misjudgment. For example, if the correlation coefficient between a signal to be matched and a template in the initial R-wave template library is 0.85, and the signal energy of the current acquisition point is 1.2 times the initial peak threshold, then the current acquisition point is recorded as a candidate R-wave peak. Similarity calculation can initially screen out signal segments that are similar to R-wave peaks in terms of morphology, avoiding misjudging noise or interference signals with large morphological differences as R-wave peaks. Signal energy judgment further verifies whether candidate signals have the characteristics of R-wave peaks from the perspective of amplitude. The combination of the two improves the accuracy and reliability of candidate R-wave peak screening.

[0067] Using the initial R-wave template library as a standard morphological reference, noise waves and non-R-wave peaks with inconsistent morphology were initially excluded. The dual-condition judgment of similarity and signal energy requires both that the waveform conforms to the characteristics of R-wave peaks and that the energy reaches the peak threshold, which greatly reduces the probability of false R-wave peaks being selected and further improves the detection speed and accuracy of true R-wave peaks.

[0068] [Fourth Embodiment]

[0069] See Figure 3 In one specific embodiment, the true R-wave peak is obtained based on the waveform verification of the candidate R-wave peak within the target time period and the initial threshold, specifically including:

[0070] S310. Record the enhanced ECG signal of the candidate R-wave peak within the target time period as the target signal, and verify the waveform of the target signal. When the waveform verification meets the verification standard, calculate the slope of the candidate R-wave peak.

[0071] S320. When the slope of the candidate R-wave peak is greater than the initial slope threshold and the candidate R-wave peak is the maximum value of the target signal, the candidate R-wave peak is recorded as the true R-wave peak.

[0072] In step S310, the target time period refers to the time period that contains both candidate R-wave peaks and waveform characteristics of real R-wave peaks. For example, the time period within 80 ms before and 150 ms after the candidate R-wave peak is recorded as the target time period. The target signal is an enhanced ECG signal within a specific time range surrounding the candidate R-wave peak. Waveform verification is performed to ensure that the waveform of the candidate R-wave peak conforms to the characteristics of a real R-wave peak, avoiding misjudgment of signals with similar shapes but not real R-wave peaks. For example, for each candidate R-wave peak, a forward search is performed for 80 ms to determine whether the target signal has a zero-crossing point within these 80 ms, and the subsequent 150 ms are verified to determine whether the target signal returns to the baseline within these 150 ms. When these morphological characteristics meet the preset verification criteria, the slope of the candidate R-wave peak is further calculated. The slope calculation can be obtained through the first difference of the candidate R-wave peak.

[0073] In step S320, a candidate R-wave peak is more likely to be a true R-wave peak only when its slope is significantly higher than that formed by interference factors such as noise levels. At the same time, the candidate R-wave peak must be the maximum value of the target signal. This is a key condition for further confirming whether it is a true R-wave peak from the perspective of amplitude, because the true R-wave peak is usually the point with the largest amplitude in the target signal. For example, if the slope of a candidate R-wave peak is calculated to be 1.3 times the initial slope threshold, and its amplitude is the largest in the target signal, which is the maximum value of the target signal, then the candidate R-wave peak can be determined as a true R-wave peak.

[0074] Waveform verification helps to enhance the morphological specificity screening of R-wave peaks, making the selected real R-wave peaks more consistent with physiological characteristics in terms of waveform structure, thus improving the specificity of R-wave peak detection. The initial slope threshold helps to further distinguish noise (smooth fluctuations, low slope) from real R-wave peaks. By locking the core signal point with the highest amplitude within the target time period, it aligns with the physiological nature of R-wave peaks as high-amplitude bands in ECG signals.

[0075] [Fifth Embodiment]

[0076] In one specific embodiment, when the actual R-wave peak appears, the stimulation delay is calculated based on species-preset parameters and real-time cardiac physiological signals, and the stimulation mode and energy parameters are selected and adjusted according to changes in the real-time cardiac physiological signals, specifically including:

[0077] S410. Obtain the average heart rate based on real-time cardiac physiological signals, and calculate the stimulation delay based on the average heart rate and species preset parameters. The formula for calculating the stimulation delay is as follows:

[0078] D = k×RR mean + C;

[0079] Where D is the time required for stimulus delay, and RR mean The mean of the RR interval is given, and k and C are pre-defined parameters for the species, which were optimized and determined after statistical analysis of a large amount of experimental data.

[0080] S420: Select the stimulation mode and adjust the energy parameters based on real-time changes in cardiac physiological signals.

[0081] In step S420, the changes in real-time cardiac physiological signals include multiple aspects, such as the trend of heart rate changes, changes in the amplitude and morphology of the R wave peak, etc. The stimulation mode is selected based on these changes. For example, if the heart rate continues to increase and the amplitude of the R wave peak decreases, a milder stimulation mode may be selected to avoid excessive stimulation and burden on the heart. If the heart rate is too slow and the morphology of the R wave peak is abnormal, a stronger stimulation mode is selected to enhance the heart's contractility.

[0082] Using the mean RR interval as the core physiological indicator and combining it with species-specific preset parameters to lock in the optimal timing of stimulation helps to adapt to the differences in cardiac physiological rhythms among different species. At the same time, it responds to real-time heart rate fluctuations of the same target, avoids stimulation "peak misalignment" caused by fixed delays, ensures that stimulation acts on the key stage of myocardial contraction, maximizes the efficiency of contractility regulation, and selects stimulation modes based on real-time signal changes to avoid insufficient adaptation of a single stimulation mode to different cardiac states, making the stimulation strategy more in line with physiological needs. Adjusting energy parameters based on real-time signals reduces the safety risks of electrical stimulation.

[0083] [Sixth Embodiment]

[0084] In one specific embodiment, selecting the stimulation mode and adjusting the energy parameters based on changes in real-time cardiac physiological signals includes:

[0085] S421. Monitor the myocardial oxygen consumption index of the target heart in real time and select the stimulation mode according to the changes in the myocardial oxygen consumption index.

[0086] S422. When the myocardial oxygen consumption index is greater than the index threshold, the intermittent enhancement mode is used; when the myocardial oxygen consumption index is less than the index threshold, the single-pulse enhancement mode is used.

[0087] S423. Monitor the increase in contractility of the target heart after a number of stimulations and adjust the energy parameters according to the increase in contractility.

[0088] S424. When the increase in contractile force is less than the first amplitude threshold, the stimulation voltage is increased; when the increase in contractile force is greater than the second amplitude threshold, the stimulation voltage is decreased.

[0089] In step S421, the myocardial oxygen consumption index is an important indicator reflecting cardiac energy metabolism and oxygen supply and demand balance. By monitoring this index in real time, the physiological state of the heart can be dynamically understood. For example, when the myocardial oxygen consumption index rises, it indicates that the heart is under high load. If a strong stimulation mode is used at this time, it may further increase the burden on the heart. Therefore, a milder stimulation mode, such as the intermittent enhancement mode, should be selected to avoid overstimulating the heart.

[0090] In step S422, the intermittent enhancement mode allows the heart to rest during the stimulation interval through intermittent stimulation, thereby reducing overall myocardial oxygen consumption and protecting cardiac function. The single-pulse enhancement mode stimulates the heart with a single strong pulse, which is suitable for situations where the heart is in good condition and oxygen consumption is low, and can more directly enhance cardiac contractility.

[0091] In step S423, the magnitude of the increase in contractility is a key indicator for evaluating the effect of stimulation. By monitoring this indicator in real time, the impact of stimulation on cardiac contractility can be understood in a timely manner. For example, if the magnitude of the increase in contractility does not meet expectations, it may be necessary to increase the stimulation energy. If the magnitude of the increase in contractility is too large, it may be necessary to reduce the stimulation energy to avoid damage to the heart.

[0092] In step S424, the first amplitude threshold is a preset safety lower limit. When the amplitude of the contractile force enhancement is lower than this threshold, it indicates that the current stimulation energy is insufficient and the stimulation effect needs to be enhanced by increasing the stimulation voltage. The second amplitude threshold is a preset safety upper limit. When the amplitude of the contractile force enhancement exceeds this threshold, it indicates that the current stimulation energy is too high and may pose a potential risk to the heart. The stimulation intensity needs to be reduced by decreasing the stimulation voltage to ensure the safety and effectiveness of the treatment.

[0093] Using myocardial oxygen consumption index as the core monitoring indicator, it is directly related to cardiac metabolic load, avoiding excessive myocardial oxygen consumption caused by stimulation. Using the increase in contractility as feedback basis, it realizes closed-loop regulation of energy parameters, which helps to avoid problems of insufficient energy and energy excess damage.

[0094] [Seventh Embodiment]

[0095] See Figure 4 In one specific embodiment, the number of real R-wave peaks is monitored in real time. When the number of real R-wave peaks reaches a threshold, the initial threshold and the initial R-wave template library are updated to obtain an updated threshold and an updated R-wave template library. Real R-wave peaks are then identified based on the updated threshold and the updated R-wave template library. Specifically, this includes:

[0096] S510. When the number of real R-wave peaks reaches the quantity threshold, the batch of real R-wave peaks is grouped to form associated R-wave peak groups.

[0097] S520. Dynamically adjust the initial peak threshold according to the associated R-wave peak group to obtain the updated peak threshold;

[0098] S530. Based on the updated peak threshold, the enhanced ECG signal is filtered to obtain the updated R-wave template library;

[0099] S540. Recalculate the mean RR interval based on the associated R-wave peak group to obtain the updated mean RR interval, and adjust the subsequent stimulus delay based on the mean RR interval.

[0100] In step S510, the quantity threshold is a pre-set standard value. When the number of real R-wave peaks accumulates to this threshold, it indicates that enough real R-wave peak samples have been collected. At this time, grouping operations help to better analyze the characteristics and patterns of these R-wave peaks. The grouping method can be determined according to actual needs. For example, if 5 real R-wave peaks are detected, an associated R-wave peak group is formed.

[0101] In step S520, the initial peak threshold is set based on limited data in the initial stage, which may have certain limitations. By analyzing the amplitude characteristics of each R-wave peak in the associated R-wave peak group, the amplitude range of the R-wave peak in the current cardiac state can be grasped more accurately, thereby dynamically adjusting the initial peak threshold to better reflect the actual situation. The formula for updating the peak threshold is as follows:

[0102] T peak = a×T peak + (1-a)×E max ;

[0103] Where 'a' is the forgetting factor, which can be determined empirically, and E max To determine the maximum energy value of the R-wave peak within the associated R-wave peak group.

[0104] It should be noted that the initial slope threshold usually does not need to be updated at the same frequency and in the same manner, because the average noise level and fluctuation range of the enhanced ECG are usually relatively stable.

[0105] In step S530, after the peak threshold is determined, the R-wave peaks that meet the requirements are re-screened from the enhanced ECG signal based on the threshold. The enhanced ECG signals containing these R-wave peaks are then included in the R-wave template library as new templates to form an updated R-wave template library. This makes the templates in the template library closer to the current actual state of the heart and improves the accuracy of subsequent R-wave peak identification. For example, templates in the initial R-wave template library that have large differences in morphology from the currently associated R-wave peak group are removed, and newly screened templates that are more in line with the current situation are added.

[0106] In step S540, the RR interval refers to the time interval between two consecutive true R wave peaks, which is an important indicator of heart rhythm. By recalculating the mean RR interval between each R wave peak in the associated R wave peak group, the current heart rhythm can be understood more accurately.

[0107] Based on the actual R-wave peak grouping of the number of qualified individuals, the peak threshold is dynamically adjusted according to the latest actual signal characteristics. This helps to avoid the initial threshold becoming invalid due to changes in cardiac state. The template is reconstructed based on the latest R-wave peaks selected by updating the peak threshold, so that the template fits the current R-wave peak morphology characteristics of the heart and replaces the template that may have been biased in the initial stage. The mean RR interval is recalculated based on the latest associated R-wave peak group, so that the calculation basis of stimulation delay closely follows the real-time changes in cardiac rhythm, avoiding stimulation timing deviation caused by outdated mean RR intervals.

[0108] [Eighth Embodiment]

[0109] See Figure 5In one specific embodiment, the present invention also provides an in vitro cardiac contractility dynamic enhancement system 100 with R-wave peak dual-modal sensing. The in vitro cardiac contractility dynamic enhancement method with R-wave peak dual-modal sensing described in the above embodiment is applied to the dynamic enhancement system 100. The dynamic enhancement system 100 includes: a data acquisition module 110 for acquiring real-time cardiac physiological signals; a data analysis module 120 for analyzing real-time cardiac physiological signals and obtaining the true R-wave peak; and an intelligent decision-making module 130 for selecting stimulation modes and adjusting energy parameters. This dynamic enhancement system has all the technical features of the above-mentioned dynamic enhancement method, which will not be described in detail here.

[0110] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A method for dynamically enhancing in vitro cardiac contractility using dual-modal sensing of R-wave peaks, characterized in that, The dynamic enhancement method includes: Real-time cardiac physiological signals of the target heart are collected, and an initial threshold and an initial R-wave template library are obtained based on the real-time cardiac physiological signals within the initial target time period. Candidate R-wave peaks are obtained by filtering the real-time cardiac physiological signals based on the initial threshold and the initial R-wave template library. The true R-wave peak is obtained by verifying the waveform of the candidate R-wave peak within the target time period and using the initial threshold. When the actual R-wave peak appears, the stimulation delay is calculated based on the species preset parameters and the real-time cardiac physiological signal, and the stimulation mode and energy parameters are selected and adjusted according to the changes in the real-time cardiac physiological signal. The number of real R-wave peaks is monitored in real time. When the number of real R-wave peaks reaches a threshold, the initial threshold and the initial R-wave template library are updated to obtain an updated threshold and an updated R-wave template library. The real R-wave peaks are then identified based on the updated threshold and the updated R-wave template library.

2. The dynamic enhancement method according to claim 1, characterized in that, The process of acquiring real-time cardiac physiological signals from the target heart and obtaining an initial threshold and an initial R-wave template library based on these signals within the initial target time period specifically includes: The real-time ECG signal is obtained based on the real-time cardiac physiological signal, and the real-time ECG signal is preprocessed by wavelet transform to obtain the initial ECG signal; The waveform characteristics of the initial ECG signal are enhanced by an energy operator to obtain an enhanced ECG signal. Based on the enhanced ECG signal within the initial target time period, the target band is obtained. An initial peak threshold is obtained based on the energy characteristics of the target band, and an average noise level is obtained based on the target band and the enhanced ECG signal. An initial slope threshold is obtained based on the average noise level, and an initial threshold is obtained based on the initial slope threshold and the initial peak threshold. The initial R-wave template library is obtained by filtering the target bands according to the initial threshold.

3. The dynamic enhancement method according to claim 2, characterized in that, The step of filtering the real-time cardiac physiological signal to obtain candidate R-wave peaks based on the initial threshold and the initial R-wave template library specifically includes: Based on the enhanced ECG signal at the current acquisition point, a signal to be matched is obtained, and the similarity between the signal to be matched and the initial R-wave template library is calculated. When the similarity is greater than the similarity threshold and the signal energy of the current acquisition point exceeds the initial peak threshold, the current acquisition point is recorded as the candidate R-wave peak.

4. The dynamic enhancement method according to claim 3, characterized in that, The step of obtaining the true R-wave peak based on the waveform verification of the candidate R-wave peak within the target time period and the initial threshold specifically includes: The enhanced ECG signal of the candidate R-wave peak within the target time period is recorded as the target signal, and the waveform of the target signal is verified. When the waveform verification meets the verification criteria, the slope of the candidate R-wave peak is calculated. When the slope of the candidate R-peak is greater than the initial slope threshold and the candidate R-peak is the maximum value of the target signal, the candidate R-peak is recorded as the true R-peak.

5. The dynamic enhancement method according to claim 4, characterized in that, When the actual R-wave peak appears, the stimulation delay is calculated based on the species-preset parameters and the real-time cardiac physiological signal, and the stimulation mode and energy parameters are selected and adjusted according to the changes in the real-time cardiac physiological signal. Specifically, this includes: The average heart rate is obtained based on the real-time cardiac physiological signal. The stimulation delay is calculated based on the average heart rate and the species preset parameters. The calculation formula for the stimulation delay is as follows: D = k×RR mean + C; Where D is the time required for the stimulus delay, and RR mean The mean RR interval is given, and k and C are preset parameters for the species, which were optimized and determined after statistical analysis of a large amount of experimental data. The stimulation mode and energy parameters are selected and adjusted based on the changes in the real-time cardiac physiological signals.

6. The dynamic enhancement method according to claim 5, characterized in that, The selection of stimulation mode and adjustment of energy parameters based on changes in the real-time cardiac physiological signals specifically includes: The myocardial oxygen consumption index of the target heart is monitored in real time, and the stimulation mode is selected according to the changes in the myocardial oxygen consumption index. When the myocardial oxygen consumption index is greater than the exponential threshold, the intermittent enhancement mode is used; when the myocardial oxygen consumption index is less than the exponential threshold, the single-pulse enhancement mode is used. The energy parameters are adjusted based on the increase in contractility of the target heart after a number of stimulations, as the target heart's contractility increases in real time. When the increase in contractile force is less than the first amplitude threshold, the stimulation voltage is increased; when the increase in contractile force is greater than the second amplitude threshold, the stimulation voltage is decreased.

7. The dynamic enhancement method according to claim 6, characterized in that, The real-time monitoring of the number of real R-wave peaks, when the number of real R-wave peaks reaches a threshold, updates the initial threshold and the initial R-wave template library to obtain an updated threshold and an updated R-wave template library, and continues to identify real R-wave peaks based on the updated threshold and the updated R-wave template library, specifically including: When the number of real R-wave peaks reaches the number threshold, the batch of real R-wave peaks is grouped to form associated R-wave peak groups. The initial peak threshold is dynamically adjusted based on the associated R-wave peak group to obtain the updated peak threshold; The enhanced ECG signal is filtered according to the updated peak threshold to obtain the updated R-wave template library; The mean RR interval is recalculated based on the associated R-wave peak group to obtain an updated mean RR interval, and the subsequent stimulus delay is adjusted based on the mean RR interval.

8. An in vitro cardiac contractility dynamic enhancement system based on R-wave peak dual-modal sensing, characterized in that, The dynamic enhancement method according to any one of claims 1 to 7 is applied to the dynamic enhancement system, the dynamic enhancement system comprising: The data acquisition module is used to acquire the real-time cardiac physiological signals; The data analysis module is used to analyze the real-time cardiac physiological signals and obtain the true R wave peak; The intelligent decision-making module is used to select stimulation modes and adjust energy parameters.

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