ECG Images-based Cardiovascular Disease Classification utilizing a Deep Learning Model

Abstract

Early and accurate cardiovascular diseases detection is indispensable in specifying efficient treatment and prohibiting life-threatening complications. Traditional detection schemes that depend on manual interpretation of electrocardiograms (ECGs) are generally subject to inter-observer variability and are time-consuming. In this paper, a deep learning model is proposed for classifying cardiovascular diseases relying on two benchmark publicly available datasets of “twelve-lead ECG images”. In order to improve signal diversity, distinct pre-processes and augmentation are adapted on these datasets of cardiac patients. The proposed deep learning model of Convolutional Neural Network (CNN) encompasses distinct blocks of layers (Extensible and detachable convolutions) that are intended to possess morphological patterns and wider spatial dependencies in ECG images. Furthermore, multiple layers of batch normalization and dropouts were employed for stabilizing training and achieving generalization. Experimental results revealed a superior classification accuracy for the proposed system with augmentation utilizing the two datasets, outperforming existing related systems. These results demonstrated the capability of the proposed system to assist cardiologists in early diagnosis and preventive treatment.