An Approach to Identify “ R Peak” of an Ecg Signal Using Shannon Energy to Improve Clinical Diagnosis

Abstract

A novel method for processing Electrocardiogram (ECG) signals, with a focus on interpreting the QRS complex and extracting its characteristics. The algorithm incorporates a variety of techniques, including median filtering, the Shannon energy transform, segmentation, time and amplitude thresholds, and statistical false peak elimination (SFPE). These filters play a crucial role in the preprocessing phase, reducing undesired background noise and interference. With in the algorithm, the Shannon energy (SE) is computed, and an SE envelope is generated by applying a predefined threshold. This envelope serves the purpose of pinpointing R waves in the time domain. False peaks, stemming from residual noise, are eliminated by averaging peak-to-peak intervals. The algorithm's performance is evaluated using MIT-BIH arrhythmia database, demonstrating significant improvements in signal quality, reduced noise, and closer alignment to the original signal. This underscores the efficiency of the proposed signal processing technique.