Utilizing ECG-Based Heartbeat Classification for Hypertrophic Cardiomyopathy Identification

Quazi Abidur Rahman, Larisa G. Tereshchenko, Matthew Kongkatong, Theodore Abraham, M. Roselle Abraham, Hagit Shatkay

    Research output: Contribution to journalArticle

    37 Scopus citations

    Abstract

    Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease where the heart muscle is partially thickened and blood flow is (potentially fatally) obstructed. A test based on electrocardiograms (ECG) that record the heart electrical activity can help in early detection of HCM patients. This paper presents a cardiovascular-patient classifier we developed to identify HCM patients using standard 10-second, 12-lead ECG signals. Patients are classified as having HCM if the majority of their recorded heartbeats are recognized as characteristic of HCM. Thus, the classifier's underlying task is to recognize individual heartbeats segmented from 12-lead ECG signals as HCM beats, where heartbeats from non-HCM cardiovascular patients are used as controls. We extracted 504 morphological and temporal features-both commonly used and newly-developed ones-from ECG signals for heartbeat classification. To assess classification performance, we trained and tested a random forest classifier and a support vector machine classifier using 5-fold cross validation. We also compared the performance of these two classifiers to that obtained by a logistic regression classifier, and the first two methods performed better than logistic regression. The patient-classification precision of random forests and of support vector machine classifiers is close to 0.85. Recall (sensitivity) and specificity are approximately 0.90. We also conducted feature selection experiments by gradually removing the least informative features; the results show that a relatively small subset of 264 highly informative features can achieve performance measures comparable to those achieved by using the complete set of features.

    Original languageEnglish (US)
    Article number7094280
    Pages (from-to)505-512
    Number of pages8
    JournalIEEE Transactions on Nanobioscience
    Volume14
    Issue number5
    DOIs
    StatePublished - Jul 1 2015

    Keywords

    • Electrocardiogram
    • feature selection
    • hypertrophic cardiomyopathy
    • machine learning
    • patient classification

    ASJC Scopus subject areas

    • Biotechnology
    • Bioengineering
    • Medicine (miscellaneous)
    • Biomedical Engineering
    • Pharmaceutical Science
    • Computer Science Applications
    • Electrical and Electronic Engineering

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