Deep neural networks versus support vector machines for ECG arrhythmia classification

Sean Shensheng Xu, Man Wai Mak, Chi Chung Cheung

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

15 Citations (Scopus)

Abstract

Heart arrhythmia is a condition in which the heartbeat is too fast, too slow, or irregular. As Electrocardiography (ECG) is an efficient measurement of heart arrhythmia, lots of research efforts have been spent on the identification of heart arrhythmia by classifying ECG signals for health care. Among them, support vector machines (SVMs) and artificial neural networks (ANNs) are the most popular. However, most of the previous studies reported the performance of either the SVMs or the ANNs without in-depth comparisons between these two methods. Also, a large number of features can be extracted from ECG signals, and some may be more relevant to heart arrhythmia than the others. This paper is to enhance the performance of heart arrhythmia classification by selecting relevant features from ECG signals, applying dimension reduction on the feature vectors, and applying deep neural networks (DNNs) for classification. A holistic comparison among DNNs, SVMs, and ANNs will be provided. Experimental results suggest that DNNs outperform both SVMs and ANNs, provided that relevant features have been selected.
Original languageEnglish
Title of host publication2017 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2017
PublisherIEEE
Pages127-132
Number of pages6
ISBN (Electronic)9781538605608
DOIs
Publication statusPublished - 5 Sep 2017
Event2017 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2017 - Hong Kong, Hong Kong
Duration: 10 Jul 201714 Jul 2017

Conference

Conference2017 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2017
Country/TerritoryHong Kong
CityHong Kong
Period10/07/1714/07/17

Keywords

  • deep neural networks
  • ECG
  • Fisher discriminant ratio
  • Heart arrhythmia classification
  • SVM

ASJC Scopus subject areas

  • Computer Science Applications
  • Media Technology

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