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

Thumbnail Image

Files

Srivastava+et+al_2022_Physiol._Meas._10.1088_1361-6579_ac6f40.pdf (2.94 MB)

Links to Files

https://iopscience.iop.org/article/10.1088/1361-6579/ac6f40

Permanent Link

https://doi.org/10.1088/1361-6579/ac6f40
 http://hdl.handle.net/11603/25039

Collections

UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection
UMBC Student Collection

Author/Creator

Author/Creator ORCID

https://orcid.org/0000-0001-6887-1919

Date

2022-05-12

Type of Work

journal articles
postprints
Text

Department

Program

Citation of Original Publication

Apoorva Srivastava et al 2022 Physiol. Meas. in press https://doi.org/10.1088/1361-6579/ac6f40

Rights

This is an author-created, un-copyedited version of an article accepted for publication/published in Physiological Measurement. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.1088/1361-6579/ac6f40
Access to this item will begin on date May 12, 2023

Subjects

Abstract

Objective. Most arrhythmias due to cardiovascular diseases alter the 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. Approach. This work proposes a deep residual inception network with channel attention mechanism (RINCA) for twenty-nine cardiac arrhythmia classification (CAC) along with normal ECG from multi-label ECG signal with different lead combinations. The RINCA architecture 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. Main results. Exhaustive experimental evaluation on the large-scale 'PhysioNet/Computing in Cardiology Challenge (2021)' dataset demonstrates RINCA efficacy. On the hidden test data set, RINCA 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. Significance. The proposed RINCA model is more robust against varied sampling frequency, recording time, and data with heterogeneous demographics than the existing art. The explainability analysis shows RINCA potential in clinical interpretations.