Next Article in Journal
A Critical Reassessment of the Hess–Murray Law
Next Article in Special Issue
Contact-Free Detection of Obstructive Sleep Apnea Based on Wavelet Information Entropy Spectrum Using Bio-Radar
Previous Article in Journal
Traceability Analyses between Features and Assets in Software Product Lines
Previous Article in Special Issue
Effect of a Percutaneous Coronary Intervention Procedure on Heart Rate Variability and Pulse Transit Time Variability: A Comparison Study Based on Fuzzy Measure Entropy
Article Menu

Export Article

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

ECG Classification Using Wavelet Packet Entropy and Random Forests

1,2,* and 1,3
School of Economic Information Engineering, Southwestern University of Finance and Economics, 55 Guanghuacun Street, Chengdu 610074, China
Institute of Chinese Payment System, Southwestern University of Finance and Economics, 55 Guanghuacun Street, Chengdu 610074, China
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]   |  


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

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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

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]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top