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Open AccessArticle

Electrocardiogram Signal Denoising Using Extreme-Point Symmetric Mode Decomposition and Nonlocal Means

School of Life Science and Technology, Huazhong University of Science and Technology, 1037 Luoyu Rd., Wuhan 430074, China; [email protected] (X.T.)
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Academic Editors: Steffen Leonhardt and Daniel Teichmann
Sensors 2016, 16(10), 1584; https://doi.org/10.3390/s16101584
Received: 24 April 2016 / Revised: 8 September 2016 / Accepted: 20 September 2016 / Published: 25 September 2016
(This article belongs to the Special Issue Noninvasive Biomedical Sensors)
Electrocardiogram (ECG) signals contain a great deal of essential information which can be utilized by physicians for the diagnosis of heart diseases. Unfortunately, ECG signals are inevitably corrupted by noise which will severely affect the accuracy of cardiovascular disease diagnosis. Existing ECG signal denoising methods based on wavelet shrinkage, empirical mode decomposition and nonlocal means (NLM) cannot provide sufficient noise reduction or well-detailed preservation, especially with high noise corruption. To address this problem, we have proposed a hybrid ECG signal denoising scheme by combining extreme-point symmetric mode decomposition (ESMD) with NLM. In the proposed method, the noisy ECG signals will first be decomposed into several intrinsic mode functions (IMFs) and adaptive global mean using ESMD. Then, the first several IMFs will be filtered by the NLM method according to the frequency of IMFs while the QRS complex detected from these IMFs as the dominant feature of the ECG signal and the remaining IMFs will be left unprocessed. The denoised IMFs and unprocessed IMFs are combined to produce the final denoised ECG signals. Experiments on both simulated ECG signals and real ECG signals from the MIT-BIH database demonstrate that the proposed method can suppress noise in ECG signals effectively while preserving the details very well, and it outperforms several state-of-the-art ECG signal denoising methods in terms of signal-to-noise ratio (SNR), root mean squared error (RMSE), percent root mean square difference (PRD) and mean opinion score (MOS) error index. View Full-Text
Keywords: electrocardiogram; signal denoising; extreme-point symmetric mode decomposition; nonlocal means electrocardiogram; signal denoising; extreme-point symmetric mode decomposition; nonlocal means
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MDPI and ACS Style

Tian, X.; Li, Y.; Zhou, H.; Li, X.; Chen, L.; Zhang, X. Electrocardiogram Signal Denoising Using Extreme-Point Symmetric Mode Decomposition and Nonlocal Means. Sensors 2016, 16, 1584. https://doi.org/10.3390/s16101584

AMA Style

Tian X, Li Y, Zhou H, Li X, Chen L, Zhang X. Electrocardiogram Signal Denoising Using Extreme-Point Symmetric Mode Decomposition and Nonlocal Means. Sensors. 2016; 16(10):1584. https://doi.org/10.3390/s16101584

Chicago/Turabian Style

Tian, Xiaoying; Li, Yongshuai; Zhou, Huan; Li, Xiang; Chen, Lisha; Zhang, Xuming. 2016. "Electrocardiogram Signal Denoising Using Extreme-Point Symmetric Mode Decomposition and Nonlocal Means" Sensors 16, no. 10: 1584. https://doi.org/10.3390/s16101584

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