ECG Arrhythmia Classification using ZASTI
Cardiac arrhythmias are presently diagnosed by manual interpretation of Electrocardiography (ECG) signals.
Automated ECG interpretation is required to perform efficient screening of arrhythmia from long term ECG data. Existing automated ECG interpretation tools however require extensive preprocessing and knowledge to determine relevant features. Thus, there is a need for a comprehensive feature extractor and classifier to analyze ECG signals. In this paper, we propose three robust deep neural network (DNN) architectures to perform feature extraction and classification of a given two second ECG signal.