Novel cascade filter design of improved sparse low-rank matrix estimation and kernel adaptive filtering for ECG denoising and artifacts cancellation. (August 2022)

• Record Type: Journal Article
• Authors: Eltrass, Ahmed S.
• Is Part Of: Biomedical signal processing and control. Volume 77(2022)
• Publication Date: 2022-08
• Subjects: Electrocardiogram (ECG) -- Improved sparse low-rank (ISLR) -- Arrhythmia (ARR) -- Congestive heart failure (CHF); Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103750
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A new multi-stage cascade system for ECG denoising and artifacts removal. Combining the ALDKRLS for ECG artifacts removal and the ISLR for ECG denoising. Superior performance in removing white and colored noises, and different artifact components. Accurate ECG multi-class diagnosis from the filtered high-quality ECG signals. Results show superior performance of the proposed approach over other techniques. Abstract: ElectroCardioGram (ECG) signals are highly vulnerable to disturbances caused by noise and artifact sources which can degrade the ECG signal quality and increase the difficulty in obtaining reliable and accurate clinical interpretations for heart conditions. This paper introduces, for the first time, the Improved Sparse Low-Rank (ISLR) algorithm for suppressing white/colored noises, and the Kernel Recursive Least Squares with Approximate Linear Dependency (ALDKRLS) algorithm for eliminating various artifact sources. A novel automated multi-stage filter is introduced for suppressing artifact components in the first stage using ALDKRLS and eliminating noise sources in the subsequent stage using ISLR. The robustness of the suggested multi-stage filter is demonstrated by eliminating noise and artifact components individually and when both present concurrently using real ECG data. Experimental results elucidate the outstanding accuracy of the suggested framework in eliminating interference sources and keeping the essential and important characteristics of the original ECG data. Also, the application of the suggested framework in practical systems is examined by investigating a new efficient ECG multi-class classification system before and after suppressing noise and artifact interferences. Results show that the suggested framework manages not only to eliminate effectively noise and artifact components, but also to achieve very accurate ECG diagnosis results by maintaining the essential characteristics of the ECG signal that differentiate different heart disorders. This elucidates the usefulness of the proposed multi-stage filter as a promising preprocessing tool for obtaining high-resolution ECG data and consequently enhancing the diagnosis performance of several heart diseases.