Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/5827
Title: Baseline wander and power line interference removal from ECG signals using eigenvalue decomposition
Authors: Pachori, Ram Bilas
Keywords: Eigenvalues and eigenfunctions;Electrocardiography;Matrix algebra;Signal interference;Signal to noise ratio;Baseline wander;ECG Denoising;Eigenvalue decomposition;Hankel matrix;Powerline interference;Biomedical signal processing;acoustic stress;algorithm;amplitude modulation;Article;baseline wander noise;cardiovascular parameters;comparative study;data base;decomposition;eigenvalue decomposition of Hankel matrix;electrocardiogram;empirical mode decomposition;extended kalman filter algorithm;frequency modulation;heart ventricle extrasystole;Hilbert vibration decomposition algorithm;modified recursive least square algorithm;noise reduction;normalized sign least mean square algorithm;power line interference noise;priority journal;QRS complex;recording;signal noise ratio;signal processing;simulation;wavelet transformation
Issue Date: 2018
Publisher: Elsevier Ltd
Citation: Sharma, R. R., & Pachori, R. B. (2018). Baseline wander and power line interference removal from ECG signals using eigenvalue decomposition. Biomedical Signal Processing and Control, 45, 33-49. doi:10.1016/j.bspc.2018.05.002
Abstract: In this paper, a novel method is proposed for baseline wander (BW) and power line interference (PLI) removal from electrocardiogram (ECG) signals. The proposed methodology is based on the eigenvalue decomposition of the Hankel matrix. It has been observed that the end-point eigenvalues of the Hankel matrix formed using noisy ECG signals are correlated with BW and PLI components. We have proposed a methodology to remove BW and PLI noise by eliminating eigenvalues corresponding to noisy components. The proposed concept uses one-step process for removing both BW and PLI noise simultaneously. The proposed method has been compared with other existing methods using performance measure parameters namely output signal to noise ratio (SNRout), and percent root mean square difference (PRD). Simulation results validate the better performance of the proposed method than compared methods at different noise levels. The proposed method is suitable for preprocessing of ECG signals. © 2018 Elsevier Ltd
URI: https://doi.org/10.1016/j.bspc.2018.05.002
https://dspace.iiti.ac.in/handle/123456789/5827
ISSN: 1746-8094
Type of Material: Journal Article
Appears in Collections:Department of Electrical Engineering

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