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http://dx.doi.org/10.6109/jkiice.2013.17.8.1947

Arrhythmia Classification based on Binary Coding using QRS Feature Variability  

Cho, Ik-Sung (Department of IT Engineering, Pusan National University)
Kwon, Hyeog-Soong (Department of IT Engineering, Pusan National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.17, no.8, 2013 , pp. 1947-1954 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 accurate detection of P-QRS-T point, higher computational cost and larger processing time. But it is difficult to detect the P and T wave signal because of person's individual difference. Therefore it is necessary to design efficient algorithm that classifies different arrhythmia in realtime and decreases computational cost by extrating minimal feature. In this paper, we propose arrhythmia detection based on binary coding using QRS feature varibility. For this purpose, we detected R wave, RR interval, QRS width from noise-free ECG signal through the preprocessing method. Also, we classified arrhythmia in realtime by converting threshold variability of feature to binary code. PVC, PAC, Normal, BBB, Paced beat classification is evaluated by using 39 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 97.18%, 94.14%, 99.83%, 92.77%, 97.48% in PVC, PAC, Normal, BBB, Paced beat classification.
Keywords
R wave; RR interval; QRS width; Binary Coding; PVC; PAC; BBB;
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Times Cited By KSCI : 2  (Citation Analysis)
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