A Generalized ECG Dynamic Model with Asymmetric Gaussians and its Application in Model-Based ECG Denoising

Constrained by the symmetries of its Gaussian elements, traditional ECG dynamic model has difficulty in accurately representing complicated ECG waveforms. In order to overcome to this limitation, this paper proposes a generalized EDM by introducing asymmetric Gaussians into the model instead of symmetric ones. The generalized EDM is then applied to the model-based ECG denoising framework using an extended Kalman filter (EKF). Experiments are conducted based on the MIT-BIH Arrhythmia database, and the results show that the proposed EDM is able to model a wider range of ECG morphologies than the traditional one, and consequently improves the denoising performance.