Deep Neural Network Trained on Surface ECG Improves Diagnostic Accuracy of Prior Myocardial Infarction over Q Wave Analysis

Ozal Yildirim, Ulas B. Baloglu, Muhammed Talo, Prasanth Ganesan, Jagteshwar S. Tung, Guson Kang, James Tooley, Mahmood I. Alhusseini, Tina Baykaner, Paul J. Wang, Marco V. Perez, Larisa Tereshchenko, Sanjiv M. Narayan, Albert J. Rogers

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

    Abstract

    Clinical screening of myocardial infarction is important for preventative treatment and risk stratification in cardiology practice, however current detection by electrocardiogram Q-wave analysis provides only modest accuracy for assessing prior cardiac events. We set out to evaluate the ability of a deep neural network trained on the electrocardiogram to identify patients with clinical history of myocardial infarction. We assessed 608 patients at two academic centers with adjudicated history of myocardial infarction. Surface electrocardiograms were used to train a neural network-based model that classifies patients with and without a history of infarction. Endpoints were assessed by clinical record review and accuracy of the model was compared against the manual assessment of pathologic Q waves. The neural network outperformed the accuracy of pathologic Q waves (62%). In training, the model accuracy converged to >98%. Validation was performed by cross-validation (k=5) with validation accuracy 71 ± 5%. Receiver-operator characteristics analysis resulted in a c-statistic of 0.730. Deep learning of a 12-lead ECG can identify features of prior myocardial injury more accurately than clinical Q-wave analysis and may serve as a valuable clinical screening tool.

    Original languageEnglish (US)
    Title of host publication2021 Computing in Cardiology, CinC 2021
    PublisherIEEE Computer Society
    ISBN (Electronic)9781665479165
    DOIs
    StatePublished - 2021
    Event2021 Computing in Cardiology, CinC 2021 - Brno, Czech Republic
    Duration: Sep 13 2021Sep 15 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
    Period9/13/219/15/21

    ASJC Scopus subject areas

    • Computer Science(all)
    • Cardiology and Cardiovascular Medicine

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