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http://dx.doi.org/10.6109/jkiice.2019.23.2.117

Feature Extraction based on Auto Regressive Modeling and an Premature Contraction Arrhythmia Classification using Support Vector Machine  

Cho, Ik-sung (Daepartment of Creative Integrated General Studies, Daegu University)
Kwon, Hyeog-soong (Department of IT Engineering, Pusan National University)
Kim, Joo-man (Department of IT Engineering, Pusan National University)
Kim, Seon-jong (Department of IT Engineering, Pusan National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.23, no.2, 2019 , pp. 117-126 More about this Journal
Abstract
Legacy study for detecting arrhythmia have mostly used nonlinear method to increase classification accuracy. Most methods are complex to process and manipulate data and have difficulties in classifying various arrhythmias. Therefore it is necessary to classify various arrhythmia based on short-term data. In this study, we propose a feature extraction based on auto regressive modeling and an premature contraction arrhythmia classification method using SVM., For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. Also, we classified Normal, PVC, PAC through SVM in realtime by extracting four optimal segment length and AR order. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 99.23%, 97.28%, 96.62% in Normal, PVC, PAC classification.
Keywords
AR model; Premature contraction; QRS; R wave; RR interval;
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