Selection of Optimal Parameters for ECG Signal Smoothing and Baseline Drift Removal

Abstract

Electrocardiogram (ECG) contains crucial clinical information about the cardiac activities of the heart, however, such signal a part of being in large volume is often characterised by a low quality due to the noise and other artifacts. In order to correctly extract the important features from the ECG signal, first it needs to be preprocessed, denoised and normilised. Significant attention in the literature has been directed toward the ECG preprocessing, though there are ambiguity to which wavelet performs the best for ECG signal processing as well as which decomposition level should be used and how the baseline wander can be removed. Parameters of wavelets have been investigated but the lack of evidence for recommendations is not found. This research conducts a comprehensive study to identify some characteristics of optimal decomposition level and to identify the span that should be used. We have taken into consideration all available wavelets within the Matlab environment and tested it on a number of randomly chosen ECG signals. Results indicate that the decomposition level of 4 should be used and that the Biorthogonal wavelet bior3.9 performs the best for smoothing and baseline drift removal. Also, we concluded that the optimal value for span is 100, which guarantees the best baseline wander removal.