ECG Signal De-noising based on Adaptive Filters

Abstract

Denoising a signal is one of the most important tasks in signal processing. Electrocardiogram (ECG) test gives more efficient result to analyze the heart diseases. The amplitude and frequency of the ECG signals are added with various noises and that may lead to a wrong analysis of ECG or it is difficult to interpret and quality is degraded. In this paper three different noises are added to raw ECG signal, Power-line Interference noise (PLI), Baseline Wandering (BW) noise and Composite Noise (CN). The noisy signal is pre-processed using bandpass filter, low-frequency ECG signal is selected by applying DWT, CEEMD (Complementary Ensemble Empirical Mode Decomposition), LMS (Least Mean Square) and NLMS (Normalized Least Mean Square) are the different filtering techniques used to denoised. To increase the signal quality, the denoised ECG is applied to Kalman Smoother. Inverse wavelet transforms, which reconstruct signal without destructing features of ECG signal. The simulation result shows that the proposed system with better performance compared to another traditional system in terms of Signal to Noise Ratio (SNR), Correlation Coefficient (CCR), Percentage Root mean square Difference (PRD) and the Mean Square Error (MSE).