Denoising of Electrocardiogram Based on Norm Parameter using Wavelet Packet Transform

Abstract

The electrocardiogram is an electrical activity of the human heart and it is mainly used in the diagnosis of different diseases associated with the human heart. In the medical field, it is also known as electrophysiological activity of the human heart. During its recording an unwanted noise known as artifacts is getting contaminated into it, this creates an obstruction in its clinical analysis. As for application of wavelet analysis is concerned, it is then fit to mention that denoising of ECG is effectively done by wavelet tools, so that identification of hidden diseases of the patient becomes possible. In this research article an algorithm is proposed for denoising ECG signal by applying a wavelet transform popularly known as wavelet packet transformation and three wavelet functions: Haar Wavelet, Db-3 Wavelet and Coiflet-3 Wavelet. The performance of this algorithm is studied on the basis of constraints popularly known in the mathematical field as Signal to Noise ratio (SNR), Norm 1 and Norm 2. This algorithm is first applied in deciding the optimum level of decomposition applied to the ECG recording No. S10m.mat, then a comparative study is laid down for a sample of 10 ECG recordings obtained from MIT-BIH database available on www.physio.net.