Title : Classification of Premature Ventricular Contraction
Published in : KSII The 12th Asia Pacific International Conference on Information Science and Technology(APIC-IST 2017)
Author : Eunkwang Jeon, Bong-Keun Jung, Yunyoung Nam, HwaMin Lee
Corresponding author : HwaMin Lee
Location : RatiLanna Riverside Spa Resort, ChiangMai, Thailand
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 arrhythmia, such as paroxysmal insomnia, ventricular fibrillation, and coronary artery disease. Therefore, we need for a method that can detect 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 ventricular premature contraction waveform using the features. Based on the error backpropagation algorithm, weights and biases were updated through learning data learning to determine weights and weights that can classify premature ventricular contractions. The final weight and bias values were used to classify normal and premature venticular contraction waveforms in ECG waveforms.
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 the MSIP(Ministry of Science, ICT and Future Planning), Korea, under the ITRC(Information Technology Research Center) support program (IITP-2017-2014-0-00720) supervised by the IITP(Institute for Information & communications Technology Promotion).