A Nonlinear Bayesian Filtering Framework for ECG Denoising

TLDR

ECG dynamics are modeled using a modified nonlinear dynamic model originally developed for realistic synthetic ECG generation. The study proposes a nonlinear Bayesian filtering framework for denoising single‑channel ECG recordings. The framework applies the modified model within Extended Kalman Filter, Extended Kalman Smoother, and Unscented Kalman Filter, with an automatic parameter‑selection scheme, and is tested on synthetic noisy ECGs with varying SNRs. The method outperforms conventional band‑pass, adaptive, and wavelet denoising across a wide SNR range and successfully removes real nonstationary muscle artifacts, demonstrating its effectiveness for model‑based ECG filtering.

Abstract

In this paper, a nonlinear Bayesian filtering framework is proposed for the filtering of single channel noisy electrocardiogram (ECG) recordings. The necessary dynamic models of the ECG are based on a modified nonlinear dynamic model, previously suggested for the generation of a highly realistic synthetic ECG. A modified version of this model is used in several Bayesian filters, including the Extended Kalman Filter, Extended Kalman Smoother, and Unscented Kalman Filter. An automatic parameter selection method is also introduced, to facilitate the adaptation of the model parameters to a vast variety of ECGs. This approach is evaluated on several normal ECGs, by artificially adding white and colored Gaussian noises to visually inspected clean ECG recordings, and studying the SNR and morphology of the filter outputs. The results of the study demonstrate superior results compared with conventional ECG denoising approaches such as bandpass filtering, adaptive filtering, and wavelet denoising, over a wide range of ECG SNRs. The method is also successfully evaluated on real nonstationary muscle artifact. This method may therefore serve as an effective framework for the model-based filtering of noisy ECG recordings.