Comparative study of LVQ and BPN ECG classifier Online publication date: Mon, 30-Apr-2018
by Ashish Nainwal; Yatindra Kumar; Bhola Jha
International Journal of Computational Systems Engineering (IJCSYSE), Vol. 4, No. 2/3, 2018
Abstract: ECG is the electrical waveform of heart activity. It contains much information on heart disease. It is very important to diagnosis the heart disease as soon as possible otherwise it can be harmful to patient. This paper presents to classify ECG signal using learning vector quantisation and back propagation neural network and feature of ECG (morphology and frequency domain) features. In this paper, the 45 ECG signals from MIT-BIH arrhythmia database are used to classify in to two classes, one is normal and another one is abnormal using above mentioned classifier. Out of 45 signals 25 are normal and 20 are abnormal according to MIT-BIH. Twenty-eight morphological features and four frequency domain features are set as an input to the classifier. The performance of classifier measures in the terms of sensitivity (SE), positive predictivity (PP) and specificity (SP). The system performance is achieved with 82.35% accuracy using LVQ and 94.11% using BPN.
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