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http://dx.doi.org/10.6109/jkiice.2020.24.10.1341

Parameter Extraction for Based on AR and Arrhythmia Classification through Deep Learning  

Cho, Ik-sung (School of Interdisciplinary Studies, Daegu University)
Kwon, Hyeog-soong (Department of IT Engineering, Pusan National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.10, 2020 , pp. 1341-1347 More about this Journal
Abstract
Legacy studies for classifying arrhythmia have been studied in order to improve the accuracy of classification, Neural Network, Fuzzy, Machine Learning, etc. In particular, deep learning is most frequently used for arrhythmia classification using error backpropagation algorithm by solving the limit of hidden layer number, which is a problem of neural network. In order to apply a deep learning model to an ECG signal, it is necessary to select an optimal model and parameters. In this paper, we propose parameter extraction based on AR and arrhythmia classification through a deep learning. For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval is modelled. And then, the weights were learned by supervised learning method through deep learning and the model was evaluated by the verification data. The classification rate of PVC is evaluated through MIT-BIH arrhythmia database. The achieved scores indicate arrhythmia classification rate of over 97%.
Keywords
AR; Deep learning; PVC; QRS; RR;
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Times Cited By KSCI : 2  (Citation Analysis)
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