Morphological and Temporal ECG Features for Myocardial Infarction Detection Using Support Vector Machines

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

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

    2 Scopus citations

    Abstract

    Myocardial infarction is a leading cause of death worldwide. A 12-lead electrocardiogram (ECG) recording is commonly performed to diagnose this pathology. In this paper, we explored temporal and morphological features extracted from multi-lead ECG signals to classify subjects from the PTB Diagnostic ECG database into healthy control and myocardial infarction using a support vector machine binary classifier. After delineating the 12-lead ECG signals with a wavelet transform-based method, a unique set of characteristic points was obtained for the ECG leads by suppressing outliers and by taking the average of the remaining points. Then, mathematical operations (average, standard deviation, skewness, etc.) performed to the P wave duration, QRS complex duration, ST-T complex, QT interval, T wave duration and RR interval were used as temporal features, and mathematical operations performed to ECG signals bounded by the P wave, QRS complex, ST-T complex and QT interval were used as morphological features. A 10-fold Monte Carlo cross-validation was employed to analyze the reproducibility of the classification results by randomly splitting the dataset into training (70%) and test (30%) sets with balanced classes. Mean classification accuracies above 93% were achieved when the SVM classifier uses only temporal ECG features, only morphological ECG features, and both temporal and morphological ECG features. The best classification performance was achieved when temporal and morphological ECG features are jointly considered by the binary SVM classifier (accuracy 96.67%, error rate 3.33%, sensitivity 97.33% and specificity 96.00%).

    Original languageEnglish
    Title of host publication8th Latin American Conference on Biomedical Engineering and 42nd National Conference on Biomedical Engineering - Proceedings of CLAIB-CNIB 2019
    EditorsCésar A. González Díaz, Christian Chapa González, Eric Laciar Leber, Hugo A. Vélez, Norma P. Puente, Dora-Luz Flores, Adriano O. Andrade, Héctor A. Galván, Fabiola Martínez, Renato García, Citlalli J. Trujillo, Aldo R. Mejía
    PublisherSpringer
    Pages172-181
    Number of pages10
    ISBN (Print)9783030306472
    DOIs
    StatePublished - 2020
    Event8th Latin American Conference on Biomedical Engineering and the 42nd National Conference on Biomedical Engineering, CLAIB-CNIB 2019 - Cancún, Mexico
    Duration: 2 Oct 20195 Oct 2019

    Publication series

    NameIFMBE Proceedings
    Volume75
    ISSN (Print)1680-0737
    ISSN (Electronic)1433-9277

    Conference

    Conference8th Latin American Conference on Biomedical Engineering and the 42nd National Conference on Biomedical Engineering, CLAIB-CNIB 2019
    Country/TerritoryMexico
    CityCancún
    Period2/10/195/10/19

    Bibliographical note

    Publisher Copyright:
    © 2020, Springer Nature Switzerland AG.

    Keywords

    • Digital signal processing
    • Electrocardiography
    • Myocardial Infarction
    • Support vector machines

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