Table of Contents
International Scholarly Research Notices
Volume 2014 (2014), Article ID 178436, 12 pages
http://dx.doi.org/10.1155/2014/178436
Research Article

ECG Beats Classification Using Mixture of Features

Department of Electronics and Communication Engineering, National Institute of Technology, Rourkela, Orissa 769008, India

Received 10 March 2014; Revised 20 May 2014; Accepted 7 June 2014; Published 17 September 2014

Academic Editor: Dusmanta K. Mohanta

Copyright © 2014 Manab Kumar Das and Samit Ari. 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.

Abstract

Classification of electrocardiogram (ECG) signals plays an important role in clinical diagnosis of heart disease. This paper proposes the design of an efficient system for classification of the normal beat (N), ventricular ectopic beat (V), supraventricular ectopic beat (S), fusion beat (F), and unknown beat (Q) using a mixture of features. In this paper, two different feature extraction methods are proposed for classification of ECG beats: (i) S-transform based features along with temporal features and (ii) mixture of ST and WT based features along with temporal features. The extracted feature set is independently classified using multilayer perceptron neural network (MLPNN). The performances are evaluated on several normal and abnormal ECG signals from 44 recordings of the MIT-BIH arrhythmia database. In this work, the performances of three feature extraction techniques with MLP-NN classifier are compared using five classes of ECG beat recommended by AAMI (Association for the Advancement of Medical Instrumentation) standards. The average sensitivity performances of the proposed feature extraction technique for N, S, F, V, and Q are 95.70%, 78.05%, 49.60%, 89.68%, and 33.89%, respectively. The experimental results demonstrate that the proposed feature extraction techniques show better performances compared to other existing features extraction techniques.