ECG Denoising Using Wavelet Transform and Wiener Filter

Abstract

Electrocardiogram (ECG) signals are commonly utilized to diagnose heart problems. Electromyographic (EMG) noise, baseline wander, and power line interference are some of the frequent noises that can alter ECG data and obscure dynamic medical information. This research examines a number of methods for reducing the various types of noise seen in ECG readings. Using the MIT-BIH Arrhythmia Database, a thorough comparison of the wavelet transforms, and Wiener filter's performances is carried out. Standard metrics, such as Signal-to-Noise Ratio (SNR), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Percentage Root Difference (PRD), Peak Signal-to-Noise Ratio (PSNR), and Correlation Coefficient (CC), are used to assess the performance. The results show how effective the Wiener filtering approach is in reducing noise in the ECG data.