A deep residual inception network with channel attention modules for multi-label cardiac abnormality detection from reduced-lead ECG.

Most arrhythmias due to cardiovascular diseases alter the heart's electrical activity, resulting in morphological alterations in electrocardiogram (ECG) recordings. ECG acquisition is a low-cost, non-invasive process and is commonly used for continuous monitoring as a diagnostic tool for cardiac abnormality identification. Our objective is to diagnose twenty-nine cardiac abnormalities and sinus rhythm using varied lead ECG signals.This work proposes a deep residual inception network with channel attention mechanism () for twenty-nine cardiac arrhythmia classification along with normal ECG from multi-label ECG signal with different lead combinations. Thearchitecture employing the inception-based convolutional neural network backbone uses residual skip connections with the channel attention mechanism. The inception model facilitates efficient computation and prevents overfitting while exploring deeper networks through dimensionality reduction and stacked 1-dimensional convolutions. The residual skip connections alleviate the vanishing gradient problem. The attention modules selectively leverage the temporally significant segments in a sequence and predominant channels for multi-lead ECG signals, contributing to the decision-making.Exhaustive experimental evaluation on the large-scale 'PhysioNet/Computing in Cardiology Challenge (2021)' dataset demonstrates's efficacy. On the hidden test data set,achieves the challenge metric score of 0.55, 0.51, 0.53, 0.51, and 0.53 (ranked 2nd, 5th, 4th, 5th and 4th) for the twelve-lead, six-lead, four-lead, three-lead, and two-lead combination cases, respectively.The proposedmodel is more robust against varied sampling frequency, recording time, and data with heterogeneous demographics than the existing art. The explainability analysis shows's potential in clinical interpretations.