Abstract
Extended Kalman filter (EKF) is a well-known nonlinear Bayesian framework deployed in various electrocardiogram (ECG) processing fields. However, it is not very effective in removing non-stationary noises, such as muscle artifacts (MA) common in ECG recordings. This paper addresses this issue by proposing a new ECG dynamic model (EDM) and a novel formulation for EKF that improves its performance in non-stationary environments. In order to show the effectiveness of the proposed EKF algorithm, its denoising performance is evaluated on the MIT-BIH Normal Sinus Rhythm Database (NSRDB) in the presence real muscle artifact noise. The results showed that the proposed EKF framework significantly outperformed the standard EKF framework in non-stationary environments from SNR improvement viewpoint.
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