Innovative graph based model for ECG data analysis

Abstract

Electrocardiography (ECG) is widely used in cardiography as a non-invasive diagnostic tool for providing a graphical representation of the electrical activity in the heart over a duration of time. It captures the electrical impulses generated by cardiac muscles and is used to detect several types of cardiac conditions, such as hypertrophy and arrhythmia. In this study we work on developing a deep learning model which can effectively classify abnormalities from 12 lead ECG data. We use the PTB-XL dataset, the largest publicly available dataset for 12 lead ECGs. In order to harness the inter-relationship from the data from the 12 leads, we model them as a graph, with graph structure determined using both the data as well as medical domain knowledge and design a deep learning model consisting of a graph convolution network (GCN) and present comprehensive quantitative evaluation.