Principal and independent component-based analysis to enhance adaptive noise canceller for electrocardiogram signals Online publication date: Tue, 15-Feb-2022
by Mangesh Ramaji Kose; Mitul Kumar Ahirwal; Rekh Ram Janghel
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 38, No. 1, 2022
Abstract: In this paper, the proposed methodology has suggested a way to fulfil the need of reference signal for adaptive filtering (AF) of electrocardiogram (ECG) signals. ECG signals are the most important form of representation and observation of different heart conditions. During recording process the ECG signals gets contaminated with different types of noises like, baseline wander (BW), electrode motion artefact (MA), and muscle noise also known as electromyogram (EMG). Noise contamination causes distortion of normal structure of ECG signal. Adaptive filters works fine for ECG noise cancellation. But, the problem is the need of reference signal or estimation of noise signal. To solve this problem principal and independent component (PCA and ICA) of noisy signal has been analysed to extract the noise signal, which is used in adaptive noise cancellation of ECG signals. Fidelity parameters like mean square error (MSE), signal to noise ratio (SNR) and maximum error (ME) has been observed to measure the quality of filtered signals.
Online publication date: Tue, 15-Feb-2022
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