Cardiac arrhythmia classification using neural networks

Article type: Research Article

Authors: Al-Nashash, H.

Affiliations: School of Engineering, American University of Sharjah, P.O. Box 26666, Sharjah, United Arab Emirates

Abstract: In this paper, ECG arrhythmia classification using principal component analysis is proposed. Hebbian neural networks are used for computing the principal components of an ECG signal. This provides an unsupervised feature extraction, dimension reduction and an improved computing efficiency. Results from 14 pathological records obtained from the MIT ECG database demonstrate the capability of this method in differentiating between five different types of arrhythmia despite the variations in signal morphology. An average value for classification sensitivity and positive predictivity were found to be Se% = 98.1% and +P% = 94.7% respectively.

Keywords: electrocardiograph, ECG, classification, Hebbian neural networks, principal components, data analysis

DOI: 10.3233/THC-2000-8605

Journal: Technology and Health Care, vol. 8, no. 6, pp. 363-372, 2000