Analysis of Different Denoising Techniques of ECG Signals

An electrocardiogram (ECG) is a recording of the electrical activity of the heart in dependence on time. The mechanical activity of the heart is linked with its electrical activity. Therefore ECG is an important diagnostic tool for assessing heart function. It becomes necessary to make ECG signals free from noise for proper analysis and detection of the diseases. Various noise removal techniques are available and can be implemented in MATLAB. Wavelets have been found to be a powerful tool for removing noise from a variety of signals (denoising).The methods that are discussed in this paper are wavelet filter-wiener filter, pilot estimation, KALMAN filter. All the above methods can be implemented for ECG signal denoising, various methods of denoising are studied and considering advantages and disadvantages of all the methods it is concluded that wavelet method of denoising and its enhancement wavelet filtering method is best. Wavelet analysis produces a time-scale view of the signal .A wavelet is a waveform of effectively limited duration that has an average value of zero. Our goal was to find a suitable filter bank using at wavelet transform and to choose other parameters with respect to the signal-to-noise ratio (SNR) obtained. Testing was performed on artificially noised signals from the standard CSE and MIT-BIH database. Keywords— Electrocardiography (ECG), Wavelet transform, CSE and MIT-BIH database, Wiener filtering, Pilot estimation, KALMAN filter.