Linear and Nonlinear Features for Myocardial Infarction Detection Using Support Vector Machine on 12-Lead ECG Recordings

Wilson J. Arenas, Martha L. Zequera, Miguel Altuve, Silvia A. Sotelo

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

    2 Scopus citations

    Abstract

    The development of non-invasive techniques to assess cardiovascular risks has grown rapidly. In this sense, a multi-lead electrocardiogram (ECG) provides useful information to diagnose myocardial infarction (MI), the leading cause of death worldwide. In this paper we used a support vector machine (SVM) to detect MI by exploiting temporal, morphological and nonlinear features extracted from 12-lead ECG recording from the PTB Diagnostic ECG database. Temporal features correspond to QT, ST-T and RR intervals, morphological features were extracted from P and T waves, and QRS complexes, and nonlinear features correspond to the sample entropy of QT, ST-T and RR intervals. A 10-fold Monte Carlo cross-validation was implemented by randomly splitting the data set into training (70%) and test (30%) sets with balanced classes. Sensitivity of 97.33%, specificity of 96.67%, and accuracy of 97.00% were obtained by jointly exploiting temporal, morphological and nonlinear features by the SVM. The inclusion of entropy favors the detection of healthy control cases because the information of signal regularity improves the specificity of classification.

    Original languageEnglish
    Title of host publication8th European Medical and Biological Engineering Conference - Proceedings of the EMBEC 2020
    EditorsTomaz Jarm, Aleksandra Cvetkoska, Samo Mahnič-Kalamiza, Damijan Miklavcic
    PublisherSpringer Science and Business Media Deutschland GmbH
    Pages758-766
    Number of pages9
    ISBN (Print)9783030646097
    DOIs
    StatePublished - 2021
    Event8th European Medical and Biological Engineering Conference, EMBEC 2020 - Portorož, Slovenia
    Duration: 29 Nov 20203 Dec 2020

    Publication series

    NameIFMBE Proceedings
    Volume80
    ISSN (Print)1680-0737
    ISSN (Electronic)1433-9277

    Conference

    Conference8th European Medical and Biological Engineering Conference, EMBEC 2020
    Country/TerritorySlovenia
    CityPortorož
    Period29/11/203/12/20

    Bibliographical note

    Publisher Copyright:
    © 2021, Springer Nature Switzerland AG.

    Keywords

    • Classification
    • Entropy
    • Myocardial infarction
    • Support vector machine

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