A feature selection for detection of non ST elevation myocardial infarction

Cesar Navarro, Mary Jo Kurth, David J. McEneaney, James McLaughlin

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

    1 Citation (Scopus)


    A feature selection (FS) process of biomarkers for detecting Acute Myocardial Infarction (AMI) for Non ST Elevation patients (NSTEMI) is presented. FS has been applied by a retrospective analysis of biomarkers - high sensitivity cardiac troponin T (hs-cTnT), heart-type fatty acid-binding protein (H-FABP), creatine kinase-MB (CK-MB), myoglobin, GPBB, CA3 and NTproBNP which are measured at different times from presentation. ECG anomalies at presentation which are key for diagnosis were not considered for FS since they are routinely assessed in the emergency department (ED). Biomarkers measurements and additional data were collected at the ED from patients with chest pain of suspected cardiac origin comprising 478 cases (97NSTEMI). hs-cTnT, H-FABP and CK-MB are statistically significant biomarkers to detect AMI according to ROC curve analysis and logistic regressions using data at different time windows. Overall, hs-cTnT as a sole marker is superior for AMI detection. However, H-FABP can be detected earlier and it demonstrates net gains in classification for non-AMI that makes it relevant for AMI rule-out approaches.

    Original languageEnglish
    Title of host publicationComputing in Cardiology Conference, CinC 2016
    EditorsAlan Murray
    PublisherIEEE Computer Society
    Number of pages4
    ISBN (Electronic)9781509008964
    Publication statusPublished (in print/issue) - 1 Mar 2016
    Event43rd Computing in Cardiology Conference, CinC 2016 - Vancouver, Canada
    Duration: 11 Sept 201614 Sept 2016

    Publication series

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


    Conference43rd Computing in Cardiology Conference, CinC 2016

    Bibliographical note

    Publisher Copyright:
    © 2016 CCAL.

    Copyright 2017 Elsevier B.V., All rights reserved.


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