TITLE:
Noise-Specific Hybrid Wavelet-Kalman Framework for Electrocardiogram Signal Denoising
AUTHORS:
Madjiko Luc Madalngué, Mbainaibeye Jérôme, Ali Ouchar Cherif, Ngoidita Natebaye, Mawilina Jonas
KEYWORDS:
Electrocardiogram, Signal Denoising, Wavelet Transform, Kalman Filter, Hybrid Filtering
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.16 No.10,
September
30,
2026
ABSTRACT: Electrocardiogram (ECG) signals are frequently contaminated by noise, which degrades signal quality and may compromise the clinical interpretation of the P-QRS-T morphology. This paper proposes a noise-specific hybrid Wavelet-Kalman framework in which the processing strategy is configured according to the considered disturbance characteristics. The proposed approach was evaluated using ECG records 103, 105, and 121 from the MIT-BIH Arrhythmia Database under representative noise conditions. Its performance was assessed using signal-to-noise ratio (SNR), mean squared error (MSE), and percent root-mean-square difference (PRD), and compared with conventional Wiener, Kalman, and Wavelet-based filtering methods. Experimental results show that denoising performance depends on the disturbance characteristics: Wavelet-based processing is particularly effective for high-frequency and non-stationary interference, whereas Kalman-based estimation provides additional benefits for low-frequency and narrowband disturbances. Under the evaluated conditions, the proposed framework achieved SNR improvements of up to 26.97 dB and PRD values as low as 2.45%, while preserving ECG morphology. These findings support the use of noise-specific processing configurations for ECG denoising, while computational complexity and real-time suitability require further quantitative evaluation.