Cardiac Arrhythmia classification using deep learning

Abstract

The present work aims to obtain an automated program based on deep learning to detect and classify cardiac arrhythmia using electrocardiogram (ECG) data. One of the main obstacles in working with medical dataset is the limited availability of public medical data and the fact that only a small fraction of data represents non-normal medical conditions. In the present work we will compare the two convolutional neural network models inspired by VGGNet and ResNet in predicting of cardiac arrhythmia. Our calculations show that the two methods are equally good in predicting the overall accuracy for large dataset. However, ResNet shows a slightly better performance in predictions for much smaller dataset.