Classification of Premature Ventricular Contraction using Error Back-Propagation

Title : Classification of Premature Ventricular Contraction using Error Back-Propagation
Journal : KSII Transactions on Internet and Information Systems
Authors : Eunkwang Jeon, Bong-Keun Jung, Yunyoung Nam, HwaMin Lee
Corresponding author : HwaMin Lee

Arrhythmia has recently emerged as one of the major causes of death in Koreans. Premature Ventricular Contraction (PVC) is the most common arrhythmia that can be found in clinical practice, and it may be a precursor to dangerous arrhythmias, such as paroxysmal insomnia, ventricular fibrillation, and coronary artery disease. Therefore, we need for a method that can detect an abnormal heart beat and diagnose arrhythmia early. We extracted the features corresponding to the QRS pattern from the subject’s ECG signal and classify the premature ventricular contraction waveform using the features. We modified the weighting and bias values based on the error back-propagation algorithm through learning data. We classify the normal signal and the premature ventricular contraction signal through the modified weights and deflection values. MIT-BIH arrhythmia data sets were used for performance tests. We used RR interval, QS interval, QR amplitude and RS amplitude features. And the hidden layer with two nodes is composed of two layers to form a total three layers (input layer 0, output layer 3).

A preliminary version of this paper was presented at APIC-IST 2017, and was selected as an outstanding paper. This research was financially supported by the “ICT Convergence Smart Rehabilitation Industrial Education Program” through the Ministry of Trade, Industry & Energy (MOTIE) and Korea Institute for Advancement of Technology (KIAT) and by the Soonchunhyang University Research Fund.