Adaptive lead weighted ResNet trained with different duration signals for classifying 12-lead ECGs

Zhibin Zhao , Hui Fang, Samuel D Relton, Ruqiang Yan , Hui Fang , Yuhong Liu, Zhijing Li, Jing Qin , David Wong

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

Introduction: We describe the creation of a ensemble deep neural network architecture to classify cardiac abnormality from 12 lead ECGs. The model was created by the team between a ROC and a heart place for the PhysioNet/Computing in Cardiology Challenge 2020.
Methods: ECGs were downsampled to 257 Hz and then set to a consistent duration by randomly clipping or zeropadding the signal to 4096 samples. To learn effective features, we created a modified ResNet with larger kernel sizes that models long-term dependencies. We embedded
a Squeeze-And-Excitation layer into the modified ResNet to learn the importance of each lead, adaptively. A simple constrained grid-search method was applied to deal with class imbalance.
Results: Using the bespoke weighted accuracy metric, We achieved a 5-fold cross-validation score of 0.684, sensitivity and specificity of 0.758 and 0.969, respectively. The corresponding result for the hidden test set was 0.672.
Conclusion: The proposed prediction model performed well on the validation and hidden test data. Such models
may be potentially used for ECG screening or diagnosis.
Original languageEnglish
Title of host publicationComputing in Cardiology
Publication statusAccepted/In press - 1 Sept 2020

Publication series

NameComputing in Cardiology
PublisherIEEE Computer Society
ISSN (Print)2325-8861

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