  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.
