ECG signal filtering based on CEEMDAN with hybrid interval thresholding and higher order statistics to select relevant modes

ECG signal filtering based on CEEMDAN with hybrid interval thresholding and higher order... Multimed Tools Appl https://doi.org/10.1007/s11042-018-6143-x ECG signal filtering based on CEEMDAN with hybrid interval thresholding and higher order statistics to select relevant modes 1 1 Lahcen El Bouny · Mohammed Khalil · Abdellah Adib Received: 15 September 2017 / Revised: 9 May 2018 / Accepted: 15 May 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In this paper, we propose a novel ECG signal enhancement method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Higher Order Statistics (HOS). In our scheme, the noisy ECG signal is first decom- posed adaptively into oscillatory components called intrinsic mode functions (IMFs)by using Empirical Mode Decomposition (EMD) or its variants. Therefore, the obtained modes are separated into two groups of noisy signal modes and one group of useful signal modes, by using a novel criterion derived from the HOS namely the fourth order cumulant or kur- tosis. After that, a modified shrinkage scheme based on Interval Thresholding technique is adaptively applied to each selected IMF from the noise-dominant groups in order to reduce the noise and to preserve the QRS complex. The overall filtered ECG signal is then recon- structed by combining the thresholded IMFs and http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

ECG signal filtering based on CEEMDAN with hybrid interval thresholding and higher order statistics to select relevant modes

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Publisher
Springer US
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-018-6143-x
Publisher site
See Article on Publisher Site

Abstract

Multimed Tools Appl https://doi.org/10.1007/s11042-018-6143-x ECG signal filtering based on CEEMDAN with hybrid interval thresholding and higher order statistics to select relevant modes 1 1 Lahcen El Bouny · Mohammed Khalil · Abdellah Adib Received: 15 September 2017 / Revised: 9 May 2018 / Accepted: 15 May 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In this paper, we propose a novel ECG signal enhancement method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Higher Order Statistics (HOS). In our scheme, the noisy ECG signal is first decom- posed adaptively into oscillatory components called intrinsic mode functions (IMFs)by using Empirical Mode Decomposition (EMD) or its variants. Therefore, the obtained modes are separated into two groups of noisy signal modes and one group of useful signal modes, by using a novel criterion derived from the HOS namely the fourth order cumulant or kur- tosis. After that, a modified shrinkage scheme based on Interval Thresholding technique is adaptively applied to each selected IMF from the noise-dominant groups in order to reduce the noise and to preserve the QRS complex. The overall filtered ECG signal is then recon- structed by combining the thresholded IMFs and

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Jun 4, 2018

References

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