Removal of power-line interference from ECG using decomposition methodologies and kalman filter framework: A comparative study

Abstract

Electrocardiogram (ECG) is a primary signal utilized in the medical field for the identification and interpretation of pathological and physiological phenomenon. In different real conditions, the ECG is corrupted by many artifacts out of them is power-line interference (PLI). The PLI sternly limits the effectiveness of ECG recordings, and therefore, it is vital to remove PLI for better clinical judgment. In this paper, we have compared discrete wavelet transform (DWT), empirical mode decomposition (EMD), Kalman filter (KF), and KF smoother (KFS) for the elimination of PLI from ECG. These methodologies have experimented on different ECG recordings taken from the MIT-BIH arrhythmia database in the input signal to noise ratio (SNR) range of -10 to 10dB. The simulation results calculated using reconstructed ECG, magnitude spectrum, output SNR, and computational cost indicate that the KFS framework gives better denoising performance compared to KF, DWT, and EMD.