ECG denoising based on probability of wavelet coefficients

Heart failure and heart diseases are among the main causes of death in the world. Therefore it is necessary to have proper tools to determine the heart condition of the patient. ECG is considered to be the most efficient diagnostic tool to check the functionality of the heart. Hence accurate analysis of ECG signal is important. But it is difficult to analyze an ECG signal if it is corrupted by noise during acquisition. Thus, noise removal becomes an essential part in ECG analysis and characterization. In this paper, we proposed a wavelet based denoising technique. In this technique, denoising is done by thresholding the less significant wavelet coefficients. Here the threshold is based on the probability of the wavelet coefficients at a particular sub-band. Two parameters Signal-to-Error Ratio (SER) and Percent Root Mean Square Difference (PRD) are calculated to evaluate the performance of the proposed denoising method. The database used for testing purpose is taken from MIT-BIH. The results show that the proposed method provides encouraging results for denoising.