Optimal Base Wavelet Selection for ECG Noise Reduction Using

Optimal Base Wavelet Selection for ECG Noise Reduction Using

17 Pages · 2015 · 837 KB · English

Optimal Base Wavelet Selection for ECG Noise Reduction Using a Comprehensive Entropy Criterion. Hong He, Yonghong Tan * and Yuexia Wang. Department of Electrical Engineering, College of Information, Mechanical and Electrical Engineering,. Shanghai Normal University, Shanghai 200234, 

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Entropy 2015, 17, 60936109; doi:103390/e17096093 entropy ISSN 10994300 wwwmdpicom/journal/entropy Article Optimal Base Wavelet Selection for ECG Noise Reduction Using a Comprehensive Entropy Criterion Hong He, Yonghong Tan * and Yuexia Wang Department of Electrical Engineering, College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China; EMail: [email protected] * Author to whom correspondence should be addressed; EMail: [email protected]; Fax: +862137022501 Academic Editor: Raúl Alcaraz Martínez Received: 29 May 2015 / Accepted: 17 August 2015 / Published: 1 September 2015 Abstract: The selection of an appropriate wavelet is an essential issue that should be addressed in the waveletbased filtering of electrocardiogram (ECG) signals Since entropy can measure the features of uncertainty associated with the ECG signal, a novel comprehensive entropy criterion E com 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 E com criterion integrates eight criteria, ie, energy, entropy, energytoentropy 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 MITBIH Arrhythmia Database The E com is compared with each of these eight criteria through four filtering performance indexes, ie, output signal to noise ratio (SNR o), root mean square error (RMSE), percent root meansquare difference (PRD) and correlation coefficients The filtering results of ninetysix ECG signals contaminated by noise have verified that E com has outperformed the other eight criteria in the selection of best base wavelets for ECG signal filtering The wavelet identified by the E com has achieved the best filtering performance than the other comparative criteria A hypothesis test also validates that SNR o, RMSE, PRD and correlation coefficients of E com are significantly different from those of the shapematched approach ( 005   , twosided t test) OPEN ACCESS Entropy 2015, 17 6094 Keywords: base wavelet; thresholding filtering; entropy; optimal selection 1 Introduction Cardiovascular disease is one of the most causes of death in the world With the aging trend of the population, people are paying more and more attention to research into telemedicine systems for the immediate and accurate detection of cardiac diseases [1] As a noninvasive test for recording the electric activity of the heart, electrocardiogram (ECG) plays a vital role in cardiac telemedicine systems The assessment of alterations in the features of ECG signals provides useful information for the detection, diagnosis and treatment of cardiac diseases However, during the ECG signal acquisition and transmission procedures, the sampled ECG signal is inevitably corrupted by various noises, such as baseline wander, electrode motion, power line interference, motion artifact and so on [2] Usually, some specific measures such as median filter and bandstop filter can be implemented to suppress the influence of baseline wander and power line interference existing in ECG signals, respectively However, electromagnetic disturbances such as thermal noise existing in measurement circuits have a significant influence on ECG signals Thus, the noise reduction of ECG signals is a key requirement prior to pathological feature analysis [3] Among a variety of filtering techniques, the wavelet transform has been proven as a useful tool for ECG signal denoising due to its powerful analysis ability in both the time and frequency domains Moreover, the abundance of the base wavelets developed over the past decades is also another prominent advantage for the enhancement of various ECG signals Nevertheless, since ECG signals have diverse wave

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