A robust deep convolutional neural network for the classification of abnormal cardiac rhythm using single lead electrocardiograms of variable length

Abstract

We have presented a robust deep learning-based architecture that can identify abnormal cardiac rhythms using short single-lead ECG recordings. The proposed architecture is computationally fast and can also be used in real-time cardiac arrhythmia detection applications.