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http://dx.doi.org/10.6109/jkiice.2018.22.2.233

Premature Ventricular Contraction Classification through R Peak Pattern and RR Interval based on Optimal R Wave Detection  

Cho, Ik-sung (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.22, no.2, 2018 , pp. 233-242 More about this Journal
Abstract
Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require higher computational cost and larger processing time. Therefore it is necessary to design efficient algorithm that classifies PVC(premature ventricular contraction) and decreases computational cost by accurately detecting feature point based on only R peak through optimal R wave. For this purpose, we detected R wave through optimal threshold value and extracted RR interval and R peak pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through RR interval and R peak pattern. The performance of R wave detection and PVC classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30. The achieved scores indicate the average of 99.02% in R wave detection and the rate of 94.85% in PVC classification.
Keywords
R wave; Sampling frequency; R peak pattern; RR interval; Premature ventricular contraction;
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Times Cited By KSCI : 2  (Citation Analysis)
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