Key result
A dictionary learning-based sparse representation framework outperformed existing ECG denoising approaches in enhancing signal quality by removing both low- and high-frequency noises.
Why the study?
ECG signal quality enhancement is a crucial task in computer-based automated processing systems, but traditional filtering techniques do not completely remove both low- and high-frequency noises.
Does a dictionary learning-based sparse representation framework improve ECG signal quality compared to existing denoising approaches?
Does a dictionary learning-based sparse representation framework improve ECG signal quality compared to existing denoising approaches?
A novel dictionary learning-based sparse representation framework effectively removes both low- and high-frequency noises from ECG signals, outperforming traditional filtering techniques.
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.
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Rakshit et al. (2018) studied ECG signal noise. Dictionary learning (DL)-based sparse representation framework vs. Existing ECG denoising approaches was evaluated on Signal-to-noise ratio, root-mean-square error and percentage RMS difference. A dictionary learning-based sparse representation framework outperformed existing ECG denoising approaches in enhancing signal quality by removing both low- and high-frequency noises.
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