A Lightweight DNN for ECG Image Classification

Recent advances in the field of AI have proved that deep neural networks perform and recognize arrhythmia better than cardiologists when trained with a large chunk of data. However, despite the better performance, deep neural networks demand more resources. Therefore, in this paper, a new deep neural network using low resources has been proposed while maintaining high performance, and it is enhanced with a depthwise separable convolution layer for Electrocardiogram (ECG) classification. The algorithm is performed on the Physikalisch-Technische Bundesanstalt (PTB) diagnostic dataset taken from Physionet consisting of two classes: Myocardial Infarction (MI) and Normal (N). Our simulation results show that the proposed lightweight DNN provides high performance with almost the same accuracy as conventional SquezeNets.