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Recurrence Plot-based Classification of Ischemic and Dilated Cardiomyopathy Patients

Javier Rodriguez, Steffen Schulz, Andreas Voss

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |September 10, 2022
    PubMed

    Insights

    This study used recurrence plot analysis to classify cardiomyopathy patients. The method accurately distinguished between ischemic and dilated cardiomyopathy, suggesting potential for early disease prognosis.

    Area of Science:

    • Cardiology
    • Biomedical Engineering
    • Data Science

    Background:

    • Cardiovascular diseases significantly impact the elderly, with early cardiomyopathy prognosis remaining a challenge.
    • Distinguishing between different types of cardiomyopathy, such as ischemic (ICM) and dilated (DCM), is crucial for effective treatment.
    • Recurrence plot analysis offers a novel approach to characterizing complex physiological signals.

    Purpose of the Study:

    • To classify cardiomyopathy patients based on etiology using significant indexes from recurrence plot characterization.
    • To differentiate between ischemic cardiomyopathy (ICM) and dilated cardiomyopathy (DCM) patients.
    • To assess the utility of recurrence plot analysis in distinguishing cardiomyopathy patients from healthy controls.

    Main Methods:

    • Extracted beat-to-beat (BBI), systolic blood pressure (SBP), diastolic blood pressure (DBP), and respiratory flow (FLW) time series from 39 cardiomyopathy patients (24 ICM, 15 DCM) and 39 controls.
    • Calculated recurrence plots for each signal and characterized them using 12 indexes.
    • Developed support vector machine (SVM) models using the best classifiers for patient classification.

    Main Results:

    • The optimal SVM model achieved 92.3% accuracy, 95.8% sensitivity, and 86.6% specificity in classifying ICM versus DCM patients.
    • When comparing all cardiomyopathy patients (CMP) against control subjects (CON), the best model demonstrated 85.8% accuracy, 92.3% sensitivity, and 80.1% specificity.
    • Analysis suggested a more deterministic signal behavior in DCM patients compared to ICM patients.

    Conclusions:

    • Recurrence plot analysis, utilizing physiological time series, is a promising tool for classifying cardiomyopathy subtypes.
    • The developed SVM models show high accuracy in differentiating between ICM and DCM, and between patients and controls.
    • These findings support the clinical relevance of recurrence plot characterization for improving cardiomyopathy prognosis and patient stratification.

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