Deep learning-based arrhythmia detection using RR-Interval framed electrocardiograms

Kim, S.-K. and Yeun, C.Y. and Yoo, Paul and Lo, N.-W. and Damiani, E. (2022) Deep learning-based arrhythmia detection using RR-Interval framed electrocardiograms. In: UNSPECIFIED (ed.) The 8th International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems. Springer. (In Press)

Abstract

Deep learning applied to electrocardiogram (ECG) data can be used to achieve personal authentication in biometric security applications, but it has not been widely used to diagnose cardiovascular disorders. We devel-oped a deep learning model for the detection of arrhythmia in which time-sliced ECG data representing the distance between successive R-peaks are used as the input for a convolutional neural network (CNN). The main ob-jective is developing the compact deep learning based detect system which minimally uses the dataset but delivers the confident accuracy rate of the Arrhythmia detection. This compact system can be implemented in weara-ble devices or real-time monitoring equipment because the feature extrac-tion step is not required for complex ECG waveforms, only the R-peak data is needed. The 10 hidden layers of the CNN detect arrhythmias using a nov-el RR-interval framing (RRIF) approach. Two testing processes were imple-mented, the first during the training and validation of the CNN algorithm and the second using different datasets for testing under realistic condi-tions. The results of both tests indicated that the Compact Arrhythmia De-tection System (CADS) matched the performance of conventional systems for the detection of arrhythmia in two consecutive test runs.

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