Automatic 12-lead ECG Classification Using a Convolutional Network Ensemble

Antonio H. Ribeiro, Daniel Gedon, Daniel Martins Teixeira, Manoel Horta Ribeiro, Antonio L.Pinho Ribeiro, Thomas B. Schon, Wagner Meira

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

The 12-lead electrocardiogram (ECG) is a major diagnostic test for cardiovascular diseases and enhanced automated analysis tools might lead to more reliable diagnosis and improved clinical practice. Deep neural networks are models composed of stacked transformations that learn tasks by examples. Inspired by the success of these models in computer vision, we propose an end-to-end approach for the task at hand. We trained deep convolutional neural network models in the heterogeneous dataset provided in the Physionet 2020 Challenge and used an ensemble of seven of these convolutional models for the classification of abnormalities present in the ECG records. Ensembles use the output of multiple models to generate a combined prediction and are known to improve performance and generalization when compared to the individual models. In our submission, we use an ensemble of neural networks with the architecture similar to the one described in Nat Commun 11, 1760 (2020) for 12-lead ECGs classification. Our approach achieved a challenge validation score of 0.657, and full test score of 0.132, placing us, the 'Code Team', in 28 out of 41 in the official ranking.

Original languageEnglish (US)
Title of host publication2020 Computing in Cardiology, CinC 2020
PublisherIEEE Computer Society
ISBN (Electronic)9781728173825
DOIs
StatePublished - Sep 13 2020
Externally publishedYes
Event2020 Computing in Cardiology, CinC 2020 - Rimini, Italy
Duration: Sep 13 2020Sep 16 2020

Publication series

NameComputing in Cardiology
Volume2020-September
ISSN (Print)2325-8861
ISSN (Electronic)2325-887X

Conference

Conference2020 Computing in Cardiology, CinC 2020
Country/TerritoryItaly
CityRimini
Period9/13/209/16/20

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • Cardiology and Cardiovascular Medicine

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