Denoising of ECG Signal using Soft Thresholding and Empirical Mode Decomposition

Abstract

Electrocardiogram (ECG) is used to record the electrical activity of the heart. Electrocardiogram (ECG), a noninvasive technique which is used generally as a primary diagnostic tool for cardiovascular diseases. A cleaned ECG signal provides necessary information about the electrophysiology of the heart diseases and ischemic changes that may occur. The electrocardiographic signals are often contaminated by noise from diverse sources. Different noises of high frequencies and low frequencies are contaminated with ECG signal that may lead wrong interpretations. The noises that commonly disturb the basic electrocardiogram are power line interference, electrode contact noise, motion artifacts, electromyography (EMG) noise, and instrumentation noise. These noises can be classified according to their frequency content. It becomes very important to minimise these disturbances in ECG signal so that accuracy and the reliability can be improve. In this paper, denoising of the ECG signal is the major objective and technique used for this purpose is based on the Empirical Mode Decomposition (EMD) followed by wavelet based soft thresholding (Rigrsure). The experiments are carried out on MIT-BIH (Massachusetts Institute of Technology Beth Israel Hospital) database.