Open AccessHybrid approach for ECG signal enhancement using dictionary learning‐based sparse representation
Author(s) -
Rakshit Manas,
Das Susmita
Publication year - 2019
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2018.5060
Subject(s) - sparse approximation , computer science , pattern recognition (psychology) , noise reduction , artificial intelligence , signal (programming language) , additive white gaussian noise , signal processing , noise (video) , speech recognition , mean squared error , interference (communication) , signal to noise ratio (imaging) , white noise , mathematics , channel (broadcasting) , digital signal processing , statistics , telecommunications , programming language , computer hardware , image (mathematics)
Electrocardiogram (ECG) signal quality enhancement is a crucial task in the computer‐based automated processing system. In this study, a dictionary learning (DL)‐based sparse representation framework is presented for ECG signal enhancement through denoising. Unlike, traditional filtering techniques, the proposed method removes both low‐ and high‐frequency noises for complete enhancement of the signal quality. The frequency localised DL‐based sparse representation approach is applied to remove both baseline wander and power‐line interference. The time‐localised and signal characteristics based DL‐based sparse representation scheme is employed for extraction of clean ECG components from white Gaussian noise and muscle artefact added noisy ECG record. Both qualitative and quantitative performance analyses are carried out using the Massachusetts Institute of Technology ‐ Beth Israel Hospital (MIT‐BIH) database, QT database and synthetic ECG signals. The efficacy of the proposed method is compared with the existing ECG denoising approaches using standard performance metrics: signal‐to‐noise ratio, root‐mean‐square error and percentage RMS difference. It is observed through the detailed study and analysis that the proposed approach outperforms the existing ones and can be served to further enhance the overall quality of ECG in a computer‐based automated medical system.