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Entropy 2016, 18(8), 285; doi:10.3390/e18080285

ECG Classification Using Wavelet Packet Entropy and Random Forests

1,2,* and 1,3
1
School of Economic Information Engineering, Southwestern University of Finance and Economics, 55 Guanghuacun Street, Chengdu 610074, China
2
Institute of Chinese Payment System, Southwestern University of Finance and Economics, 55 Guanghuacun Street, Chengdu 610074, China
3
School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, China
*
Author to whom correspondence should be addressed.
Academic Editors: Carlos M. Travieso-González and Jesús B. Alonso-Hernández
Received: 20 June 2016 / Revised: 25 July 2016 / Accepted: 2 August 2016 / Published: 5 August 2016
(This article belongs to the Special Issue Entropy on Biosignals and Intelligent Systems)
View Full-Text   |   Download PDF [800 KB, uploaded 12 August 2016]   |  

Abstract

The electrocardiogram (ECG) is one of the most important techniques for heart disease diagnosis. Many traditional methodologies of feature extraction and classification have been widely applied to ECG analysis. However, the effectiveness and efficiency of such methodologies remain to be improved, and much existing research did not consider the separation of training and testing samples from the same set of patients (so called inter-patient scheme). To cope with these issues, in this paper, we propose a method to classify ECG signals using wavelet packet entropy (WPE) and random forests (RF) following the Association for the Advancement of Medical Instrumentation (AAMI) recommendations and the inter-patient scheme. Specifically, we firstly decompose the ECG signals by wavelet packet decomposition (WPD), and then calculate entropy from the decomposed coefficients as representative features, and finally use RF to build an ECG classification model. To the best of our knowledge, it is the first time that WPE and RF are used to classify ECG following the AAMI recommendations and the inter-patient scheme. Extensive experiments are conducted on the publicly available MIT–BIH Arrhythmia database and influence of mother wavelets and level of decomposition for WPD, type of entropy and the number of base learners in RF on the performance are also discussed. The experimental results are superior to those by several state-of-the-art competing methods, showing that WPE and RF is promising for ECG classification. View Full-Text
Keywords: ECG classification; wavelet packet entropy; feature extraction; random forests; AAMI ECG classification; wavelet packet entropy; feature extraction; random forests; AAMI
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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).

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Li, T.; Zhou, M. ECG Classification Using Wavelet Packet Entropy and Random Forests. Entropy 2016, 18, 285.

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