Performance Investigation of Marginalized Particle‑Extended Kalman Filter under Different Particle Weighting Strategies in the Field of Electrocardiogram Denoising

Maryam Mohebbi, Hamed Danandeh Hesar


Background: Recently, a marginalized particle‑extended Kalman flter (MP‑EKF)
has been proposed for electrocardiogram (ECG) signal denoising. Similar to particle
flters, the performance of MP‑EKF relies heavily on the defnition of proper particle
weighting strategy. In this paper, we aim to investigate the performance of MP‑EKF
under different particle weighting strategies in both stationary and nonstationary noises.
Some of these particle weighting strategies are introduced for the frst time for ECG
denoising. Methods: In this paper, the proposed particle weighting strategies use different
mathematical functions to regulate the behaviors of particles based on noisy measurements
and a synthetic ECG signal built using feature parameters of ECG dynamic model. One
of these strategies is a fuzzy‑based particle weighting method that is defned to adapt its
function based on different input signal‑to‑noise ratios (SNRs). To evaluate the proposed
particle weighting strategies, the denoising performance of MP‑EKF was evaluated on
MIT‑BIH normal sinus rhythm database at 11 different input SNRs and in four different
types of artifcial and real noises. For quantitative comparison, the SNR improvement
measure was used, and for qualitative comparison, the multi‑scale entropy‑based
weighted distortion measure was used. Results: The experimental results revealed that the
fuzzy‑based particle weighting strategy exhibited a very well and reliable performance in
both stationary and nonstationary noisy environments. Conclusion: We concluded that the
fuzzy‑based particle weighting strategy is the best‑suited strategy for MP‑EKF framework
because it adaptively and automatically regulates the behaviors of particles in different
noisy environments.

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