A Comparison Study of Signal Processing Tools for Denoising of Electrocardiogram Signal

Electrocardiogram (ECG) signal is a nonstationary signal which shows the electrical action of the human heart and detects the cardiac abnormalities generated in the heart. But usually this signal is contaminated with different types of noises like baseline wander, power line interference, electromyography noise etc. After contamination the waveform of the signal get corrupted which shows incorrect detection of noises. Here, different types of methodologies like FIR windowing techniques, adaptive filters and wavelet based filter are used for denoising the noisy ECG signal. For analysis of performance estimation different parameters like Signal to Noise Ratio (SNR), Mean Square Error (MSE) and Percent of Root mean square Difference (PRD) are calculated and compared. The simulation results show that wavelet method is better among all these methods.