Deep Learning-Based Arrhythmia Detection Using RR-Interval Framed Electrocardiograms

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 developed 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 objective 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 wearable devices or real-time monitoring equipment because the feature extraction 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 novel RR-interval framing (RRIF) approach. Two testing processes were implemented, the first during the training and validation of the CNN algorithm and the second using different datasets for testing under realistic conditions. The results of both tests indicated that the Compact Arrhythmia Detection System (CADS) matched the performance of conventional systems for the detection of arrhythmia in two consecutive test runs.