An Optimal Wavelet Approach for ECG Noise Cancellation

Electrocardiogram (ECG) is a vital biomedical signal for diagnosing heart diseases, but now it has many other applications like stress recognition, biometric recognition, etc. But ECG signal gets noisy from various sources like as muscle noise, electrode artifacts, baseline drift noise and respiration. As wavelet transforms shows a good performance in denoising the ECG signal however, the selection of appropriate mother wavelet functions and number of wavelet decomposition level is still an issue to remove the various kinds of noises from the input signal. It is essential to denoise the ECG signal to get appropriate features of ECG signal.This research work analyze and compare the removal of noise and distortion in ECG signal using five wavelets (Daubechies, Coiflet, Haar, Biorthogonal and Symmlet) with four thresholding rules (SURE, Hybrid, Universal and Minimax) and various decomposition levels of Undecimated Wavelet Transform (UWT) and Discrete Wavelet Transform (DWT).