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http://dx.doi.org/10.6109/jkiice.2016.20.2.427

Detection of QRS Feature Based on Phase Transition Tracking for Premature Ventricular Contraction Classification  

Cho, Ik-sung (Department of Information and Communication Engineering, Kyungwoon University)
Yoon, Jeong-oh (Department of Information and Communication Engineering, Kyungwoon University)
Kwon, Hyeog-soong (Department of IT Engineering, Pusan National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.20, no.2, 2016 , pp. 427-436 More about this Journal
Abstract
In general, QRS duration represent a distance of Q start and S end point. However, since criteria of QRS duration are vague and Q, S point is not detected accurately, arrhythmia classification performance can be reduced. In this paper, we propose extraction of Q, S start and end point RS feature based on phase transition tracking method after we detected R wave that is large peak of electrocardiogram(ECG) signal. For this purpose, we detected R wave, from noise-free ECG signal through the preprocessing method. Also, we classified QRS pattern through differentiation value of ECG signal and extracted Q, S start and end point by tracking direction and count of phase based on R wave. The performance of R wave detection is evaluated by using 48 record of MIT-BIH arrhythmia database. The achieved scores indicate the average detection rate of 99.60%. PVC classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30 premature ventricular contraction(PVC). The achieved scores indicate the average detection rate of 94.12% in PVC.
Keywords
phase transition tracking; QRS pattern; QRS duration; Q, S start and end point; PVC;
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