Computational Reconstruction of Electrocardiogram Lead Placement

Alexander D. Wissner-Gress, Suraj Kapa, James Lee, Desmond B. Keenan, Natasha Drapeau, Kenneth Londoner

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

Abstract

We present a method for computationally reconstructing the spatial placement of electrocardiogram (ECG) leads using only correlations between their recorded signals and without requiring external calibration or other prior knowledge. We then apply our method to 12-lead ECGs obtained from the training dataset of the PhysioNet 2020 Challenge and examine the association of various cardiac abnormalities with the reconstructed geometries. Finally, we review potential clinical applications of our method, including automated recommendation of optimal lead placement, simplified visual summarization of ECG recordings, and improved automated classification of patient conditions.

Original languageEnglish (US)
Title of host publication2020 Computing in Cardiology, CinC 2020
PublisherIEEE Computer Society
ISBN (Electronic)9781728173825
DOIs
StatePublished - Sep 13 2020
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
CountryItaly
CityRimini
Period9/13/209/16/20

ASJC Scopus subject areas

  • Computer Science(all)
  • Cardiology and Cardiovascular Medicine

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