Channel Self-Attention Deep Learning Framework for Multi-Cardiac Abnormality Diagnosis from Varied-Lead ECG Signals

Abstract

Electrocardiogram (ECG) signals are widely used to diagnose heart health. Experts can detect multiple cardiac abnormalities using the ECG signal. In a clinical setting, 12-lead ECG is mainly used. But using fewer leads can make the ECG more pervasive as it can be integrated with wearable devices. At the same time, we need to build systems that can diagnose cardiac abnormalities automatically. This work develops a channel self-attention-based deep neural network to diagnose cardiac abnormality using a different number of ECG lead combinations. Our approach takes care of the temporal and spatial interdependence of multi-lead ECG signals. Our team participates under the name “cardiochallenger” in the “PhysioNetl-Computing in Cardiology Challenge 2021”. Our method achieves the challenge metric score of 0.55, 0.51, 0.53, 0.51, and 0.53 (ranked 2^nd, 5^th, 4^th, 5^th and 4th) for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead cases, respectively, on the test data set.