Automated Diagnosis of Reduced-Lead Electrocardiograms Using a Shared Classifier

H. T. Jessen, R. R. Van De Leur, P. A. Doevendans, R. Van Es*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Portable ECG devices with a reduced number of leads are increasingly being used in clinical practice. As part of the PhysioNet/Computing in Cardiology Challenge 2021, this study aims to develop an algorithm for automated diagnosis of reduced-lead ECGs. We compared separate baseline classifiers for the different lead-subsets with our newly proposed shared classifier. The different models were pre-trained on a physician-annotated dataset of 269,72612-lead ECGs. Fine-tuning was done on the challenge dataset, consisting of 88,243 ECGs. Even though different models showed promising results on the internal pre-training dataset, optimal scores were achieved by the baseline model on the hidden test set. Our team, UMCU, received scores of 0.47, 0.40, 0.41, 0.41, and 0.41 (ranked 14th, 17th, 17th, 17th, and 16th out of 39 teams) for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead versions of the hidden test set.

Original languageEnglish
Title of host publication2021 Computing in Cardiology, CinC 2021
PublisherIEEE Computer Society Press
ISBN (Electronic)9781665479165
DOIs
Publication statusPublished - 2021
Event2021 Computing in Cardiology, CinC 2021 - Brno, Czech Republic
Duration: 13 Sept 202115 Sept 2021

Publication series

NameComputing in Cardiology
Volume2021-September
ISSN (Print)2325-8861
ISSN (Electronic)2325-887X

Conference

Conference2021 Computing in Cardiology, CinC 2021
Country/TerritoryCzech Republic
CityBrno
Period13/09/2115/09/21

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