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

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.