A Novel ECG Denoising Scheme Using the Ensemble Kalman Filter

Sarafan, Sadaf; Vuong, Hoang Van; Jilani, Daniel; Malhotra, Samir; Lau, Michael; Vishwanath, Manoj; Ghirmai, Tadesse; Cao, Hung

doi:10.1109/embc48229.2022.9871884

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…While conventional filters can effectively eliminate most of these noise types, extracting a clean heartbeat from an ECG signal corrupted with “Additive White Gaussian Noise” (AWGN) remains a critical issue 12 . From the literature, there are four main methods for de‐noising ECG signals: Wavelet‐based methods, 13–15 PCA‐based methods, 16–18 Kalman filter‐based methods, 19,20 and artificial intelligence‐based methods (AI) 21–23 . The main limitation of the wavelet‐based approaches is the difficulty in selecting the appropriate wavelet basis and decomposition level.…”

Section: Introductionmentioning

confidence: 99%

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An efficient wavelet thresholding strategy and robust shrinkage approach for de‐noising ECG signal

Ahmed

2023

Int J Imaging Syst Tech

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This paper introduces an efficient wavelet thresholding strategy and robust shrinkage approach (WTS) for de‐noising the ECG signals. The optimal mother wavelet for de‐noising the ECG signal is automatically selected based on cross‐correlation and energy‐to‐entropy indices. A dynamic threshold is applied to various levels of decomposition to eliminate different types of noise. A shrinkage approach is suggested to attain seamless on–off transitions. The achievement of the WTS is compared with the state of the art reported in the literature using 180 real ECG signals collected from 60 volunteers. The ECG signals were recorded using three standard ECG machines without applying the filtering option to provide a diverse range of noisy ECG signals. The de‐noising outcomes show that the presented WTS can automatically select the optimal mother wavelet for the particular ECG signal, and it is superior to other techniques in terms of the average predicted signal‐to‐noise ratio (), the average predicted root‐mean‐square error (), and the average execution time (AET).

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Section: Introductionmentioning

confidence: 99%

“…Wavelet-based methods, [13][14][15] PCA-based methods, [16][17][18] Kalman filter-based methods, 19,20 and artificial intelligence-based methods (AI). [21][22][23] The main limitation of the wavelet-based approaches is the difficulty in selecting the appropriate wavelet basis and decomposition level.…”

Section: Introductionmentioning

confidence: 99%