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http://dx.doi.org/10.6109/jkiice.2019.23.12.1542

Optimal Parameter Extraction based on Deep Learning for Premature Ventricular Contraction Detection  

Cho, Ik-sung (Department of Creative Integrated General 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.23, no.12, 2019 , pp. 1542-1550 More about this Journal
Abstract
Legacy studies for classifying arrhythmia have been studied to improve the accuracy of classification, Neural Network, Fuzzy, etc. 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 optimal parameter extraction method based on a deep learning. For this purpose, R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment 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 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 97.84% in PVC classification.
Keywords
Optimal parameter; Deep learning; Premature ventricular contraction; QRS; RR interval;
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Times Cited By KSCI : 1  (Citation Analysis)
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