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

    Producción científica: Capítulo del libro/informe/acta de congresoPonencia publicada en las memorias del evento con ISBNrevisión exhaustiva

    2 Citas (Scopus)

    Resumen

    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.

    Idioma originalInglés
    Título de la publicación alojada8th European Medical and Biological Engineering Conference - Proceedings of the EMBEC 2020
    EditoresTomaz Jarm, Aleksandra Cvetkoska, Samo Mahnič-Kalamiza, Damijan Miklavcic
    EditorialSpringer Science and Business Media Deutschland GmbH
    Páginas758-766
    Número de páginas9
    ISBN (versión impresa)9783030646097
    DOI
    EstadoPublicada - 2021
    Evento8th European Medical and Biological Engineering Conference, EMBEC 2020 - Portorož, Eslovenia
    Duración: 29 nov. 20203 dic. 2020

    Serie de la publicación

    NombreIFMBE Proceedings
    Volumen80
    ISSN (versión impresa)1680-0737
    ISSN (versión digital)1433-9277

    Conferencia

    Conferencia8th European Medical and Biological Engineering Conference, EMBEC 2020
    País/TerritorioEslovenia
    CiudadPortorož
    Período29/11/203/12/20

    Nota bibliográfica

    Publisher Copyright:
    © 2021, Springer Nature Switzerland AG.

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