Abstract
Electrocardiogram (ECG) plays an important role in diagnostics of cardiac diseases. In general, ECG signals are affected by noises during data acquisition. For accurate treatment, doctors need noise-free ECG signals. This paper presents a detailed analysis of algorithms for denoising ECG signals using different IIR filters like Butterworth filter, Elliptic Filter, Types I and II Chebyshev, and FIR filters like Zero-phase low pass filter, Hamming window and rectangular window. An attempt has been carried out for denoising of ECG signals using ECG data sample from the MIT-BIH database for different noises like random noise, white noise and 50-Hz interference (hum). The performances are evaluated using MATLAB and compared in terms of Signal-to-Noise Ratio (SNR), error and accuracy by calculating the standard deviation using Wavelet toolbox. Simulation study shows that the highest SNR, i.e. 49.03, is obtained with the Butterworth filter, whereas the highest accuracy, i.e. 99.58%, is obtained with the zero-phase low pass filter.
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