An InceptionTime-Inspired Convolutional Neural Network to Detect Cardiac Abnormalities in Reduced-Lead ECG Data

Harry J. Crocker, Aaron W. Costall

Research output: Chapter in Book/Report/Conference proceedingChapter in a published conference proceeding

1 Citation (SciVal)
29 Downloads (Pure)

Abstract

Cardiovascular disease is the leading cause of death worldwide. The twelve-lead electrocardiogram (ECG) is a common tool for diagnosing cardiac abnormalities, but its interpretation requires a trained cardiologist. Thus there is growing interest in automated ECG diagnosis, especially using fewer leads. Hence the PhysioNet-CinC Challenge 2021: Will two (leads) do? The University of Bath team (UoB HBC) developed InceptionTime-inspired deep convolutional neural networks, using parallel 1D convolutions of varying length, for twelve-, six-, four-, three-, and two-lead models. The twelve-lead model achieved a Challenge metric score of 0.35 on the test set, placing the University of Bath team 23rd out of 39 entries. Though the twelve-lead model performed best, three-lead performance was lower by only 0.25 %, suggesting potential for reliable reduced-lead diagnoses. Furthermore, the three-lead model performed consistently better than the six-lead, highlighting the importance of selection of type of lead, not just their number.
Original languageEnglish
Title of host publicationComputing in Cardiology 2021
PublisherIEEE
Number of pages4
Volume48
ISBN (Electronic)9781665479165
DOIs
Publication statusE-pub ahead of print - 10 Jan 2022
EventComputing in Cardiology 2021 - Brno, Czech Republic
Duration: 12 Sep 202115 Sep 2021
http://www.cinc2021.org/

Publication series

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

Conference

ConferenceComputing in Cardiology 2021
Country/TerritoryCzech Republic
CityBrno
Period12/09/2115/09/21
Internet address

ASJC Scopus subject areas

  • Computer Science(all)
  • Cardiology and Cardiovascular Medicine

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