Enhancing Clinical Efficiency: Autonomous Determination of Cardiac Effective Refractory Period Using ECG Signals

Abstract

Electrophysiological procedures for managing arrhythmias remain time-consuming, causing patients to wait for critical interventions. To address this pressing need, we introduce a revolutionary algorithm that autonomously calculates the effective refractory period (ERP) of cardiac tissue from electrocardiogram (ECG). The algorithm principally comprises signal filtering techniques and the detection of local extrema within the signal waveform. This algorithm underwent rigorous assessment using an in-house database of ECG signals acquired from ten patients who underwent electrophysiological examinations. Fundamental digital signal processing methods, such as linear filtering and thresholding, were employed in the determination of ERP. The algorithm yielded results congruent with the ERP values established by electrophysiologists in nine out of ten cases, with a standard deviation of 18.97 milliseconds. By being accurate and easy to integrate., this algorithm holds promise for real-time deployment in clinical settings, where it could potentially streamline and automate stimulation protocols, thereby expediting the examination process.Electrophysiological procedures for managing arrhythmias remain time-consuming, causing patients to wait for critical interventions. To address this pressing need, we introduce a revolutionary algorithm that autonomously calculates the effective refractory period (ERP) of cardiac tissue from electrocardiogram (ECG). The algorithm principally comprises signal filtering techniques and the detection of local extrema within the signal waveform. This algorithm underwent rigorous assessment using an in-house database of ECG signals acquired from ten patients who underwent electrophysiological examinations. Fundamental digital signal processing methods, such as linear filtering and thresholding, were employed in the determination of ERP. The algorithm yielded results congruent with the ERP values established by electrophysiologists in nine out of ten cases, with a standard deviation of 18.97 milliseconds. By being accurate and easy to integrate., this algorithm holds promise for real-time deployment in clinical settings, where it could potentially streamline and automate stimulation protocols, thereby expediting the examination process.