A new multi-stage combined kernel filtering approach for ECG noise removal.

Electrocardiogram (ECG) signals are contaminated with different artifacts and noise sources which increase the difficulty in analyzing the ECG signals and obtaining accurate diagnosis of heart diseases. In this paper, a new multi-stage combined adaptive filtering design based on Kernel Recursive Least Squares Tracker (KRLST) and Kernel Recursive Least Squares with Approximate Linear Dependency (ALDKRLS) algorithms is proposed for removing artifacts and noise sources, while preserving the low frequency components and the tiny features of the ECG signal. The capability of the proposed approach is demonstrated by investigating several ECG signals from the MIT-BIH database and comparing the results with other adaptive filtering techniques. The results show that the combined ALDKRLS-KRLST approach is much superior in terms of attenuating artifacts components, sensitivity of ECG peak detection, and heart diseases diagnosis. This reveals the effectiveness of the proposed technique as an effective framework for achieving high-resolution ECG from noisy ECG recordings.