ECG Signal Denoising Using Wavelet Transform in MATLAB

This document presents a comprehensive approach to electrocardiogram (ECG) signal denoising using wavelet transform methodology. ECG signals are electrical patterns generated during cardiac electrophysiological activities, whose analysis provides critical insights into heart health conditions. However, these signals are frequently contaminated by various noise sources (baseline wander, powerline interference, muscle artifacts), complicating accurate interpretation and analysis. Wavelet transform serves as a powerful mathematical tool for signal processing that decomposes signals into multi-resolution frequency components. The denoising process typically involves three key MATLAB implementation steps: 1. Wavelet decomposition using functions like wavedec() with optimal wavelet selection (Daubechies, Symlets) 2. Threshold application to detail coefficients via threshold() or wthresh() functions 3. Signal reconstruction employing waverec() for noise-free output The algorithm preserves critical signal features while effectively suppressing noise through: - Soft/hard thresholding techniques for coefficient processing - Level-dependent threshold calculations using rigorous criteria - Multi-scale analysis capturing both temporal and frequency characteristics This technical discussion details wavelet transform fundamentals, practical implementation workflows, and parameter optimization strategies for enhancing ECG signal quality and analytical accuracy. The methodology enables researchers to achieve superior noise reduction without compromising clinically significant signal components.