Next Article in Journal
Entropic Dynamics
Next Article in Special Issue
Wavelet Entropy Automatically Detects Episodes of Atrial Fibrillation from Single-Lead Electrocardiograms
Previous Article in Journal
Distributing Secret Keys with Quantum Continuous Variables: Principle, Security and Implementations
Previous Article in Special Issue
Combined Power Quality Disturbances Recognition Using Wavelet Packet Entropies and S-Transform
Open AccessArticle

Optimal Base Wavelet Selection for ECG Noise Reduction Using a Comprehensive Entropy Criterion

Department of Electrical Engineering, College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China
Author to whom correspondence should be addressed.
Academic Editor: Raúl Alcaraz Martínez
Entropy 2015, 17(9), 6093-6109;
Received: 29 May 2015 / Revised: 12 August 2015 / Accepted: 17 August 2015 / Published: 1 September 2015
(This article belongs to the Special Issue Wavelet Entropy: Computation and Applications)
The selection of an appropriate wavelet is an essential issue that should be addressed in the wavelet-based filtering of electrocardiogram (ECG) signals. Since entropy can measure the features of uncertainty associated with the ECG signal, a novel comprehensive entropy criterion Ecom based on multiple criteria related to entropy and energy is proposed in this paper to search for an optimal base wavelet for a specific ECG signal. Taking account of the decomposition capability of wavelets and the similarity in information between the decomposed coefficients and the analyzed signal, the proposed Ecom criterion integrates eight criteria, i.e., energy, entropy, energy-to-entropy ratio, joint entropy, conditional entropy, mutual information, relative entropy, as well as comparison information entropy for optimal wavelet selection. The experimental validation is conducted on the basis of ECG signals of sixteen subjects selected from the MIT-BIH Arrhythmia Database. The Ecom is compared with each of these eight criteria through four filtering performance indexes, i.e., output signal to noise ratio (SNRo), root mean square error (RMSE), percent root mean-square difference (PRD) and correlation coefficients. The filtering results of ninety-six ECG signals contaminated by noise have verified that Ecom has outperformed the other eight criteria in the selection of best base wavelets for ECG signal filtering. The wavelet identified by the Ecom has achieved the best filtering performance than the other comparative criteria. A hypothesis test also validates that SNRo, RMSE, PRD and correlation coefficients of Ecom are significantly different from those of the shape-matched approach (α = 0.05 , two-sided t- test). View Full-Text
Keywords: base wavelet; thresholding filtering; entropy; optimal selection base wavelet; thresholding filtering; entropy; optimal selection
Show Figures

Figure 1

MDPI and ACS Style

He, H.; Tan, Y.; Wang, Y. Optimal Base Wavelet Selection for ECG Noise Reduction Using a Comprehensive Entropy Criterion. Entropy 2015, 17, 6093-6109.

Show more citation formats Show less citations formats

Article Access Map by Country/Region

Only visits after 24 November 2015 are recorded.
Back to TopTop