Deep Learning-Based Arrhythmia Detection Using RR-Interval Framed Electrocardiograms
This work provides a compact arrhythmia detection system for real-time monitoring and wearable devices, potentially benefiting patients needing continuous cardiac surveillance.
The paper developed a deep learning model for arrhythmia detection using time-sliced RR-interval data from ECGs as input for a CNN. The Compact Arrhythmia Detection System (CADS) achieved performance comparable to conventional systems in two test runs.
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 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. All features of the CADS are fully implemented and publicly available in MATLAB.