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http://dx.doi.org/10.3837/tiis.2018.02.028

Classification of Premature Ventricular Contraction using Error Back-Propagation  

Jeon, Eunkwang (Department of Computer Science & Engineering, Soonchunhyang University)
Jung, Bong-Keun (Department of Occupational Therapy, Soonchunhyang University)
Nam, Yunyoung (Department of Computer Engineering, Soonchunhyang University)
Lee, HwaMin (Department of Computer Software Engineering, Soonchunhyang University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.12, no.2, 2018 , pp. 988-1001 More about this Journal
Abstract
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).
Keywords
ARRHYTHMIA; BACK-PROPAGATION; PREMATURE VENTRICULAR CONTRACTION;
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