A review on ECG filtering techniques for rhythm analysis

Abstract

Electrocardiogram (ECG) signal recording is a challenging task in the field of biomedical engineering. ECG is the cardiac recording of systematic electrical activity arising from the electro-physiological rhythm of the heart muscle. But, during processing, the ECG signal is contaminated with different types of noise in the medical environment. An immense task is the separation of the preferred signal from noises caused by artifacts like muscle noise, power line interference (PLI), baseline wandering (BW), and motion artifacts (MA). Hence, our paper focuses on 50 Hz PLI which is a major artifact/noise affecting the recorded ECG signal. This paper comprehensively reviews fundamental concepts of different denoising techniques. Some of the pioneers’ works are also concisely explained in the paper. Further, in this work, comparative analysis is carried out using notch filter, adaptive filter, discrete wavelet transform (DWT) and empirical mode decomposition (EMD) for filtering 50 Hz PLI noise. A considerable improvement in signal-to-noise ratio (SNR) can be observed from the results when compared with SNR input and SNR output values. Performance comparisons of all the four techniques are also analyzed based on variations in noise frequency. The simulations were carried out in the environment of MATLAB 2019b®. This work epitomizes the significance of our quantitative evaluation, in which adaptive filters are found to perform better with respect to the SNR, whereas DWT performs better with assessment of mean square error (MSE).