Next Article in Journal
A Novel Petri Nets-Based Modeling Method for the Interaction between the Sensor and the Geographic Environment in Emerging Sensor Networks
Next Article in Special Issue
Classification of Anticipatory Signals for Grasp and Release from Surface Electromyography
Previous Article in Journal
Strain Modal Analysis of Small and Light Pipes Using Distributed Fibre Bragg Grating Sensors
Previous Article in Special Issue
System Description and First Application of an FPGA-Based Simultaneous Multi-Frequency Electrical Impedance Tomography
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(10), 1584;

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.)
Author to whom correspondence should be addressed.
Academic Editors: Steffen Leonhardt and Daniel Teichmann
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)
Full-Text   |   PDF [3499 KB, uploaded 27 September 2016]   |  


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

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top