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Cardiorespiratory and cardiovascular interactions in cardiomyopathy patients using joint symbolic dynamic analysis

Beatriz F Giraldo, Javier Rodriguez, Pere Caminal

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 7, 2016
    PubMed

    Insights

    This study classified cardiomyopathy patients using ECG, BP, and respiratory signals. Machine learning accurately identified ischemic and dilated cardiomyopathies, aiding risk stratification for heart attack.

    Area of Science:

    • Cardiology
    • Biomedical Engineering
    • Data Science

    Background:

    • Cardiovascular diseases are a leading cause of mortality in developed nations.
    • Accurate classification of cardiomyopathies like ischemic (ICM) and dilated (DCM) is crucial for patient management.
    • Left ventricular ejection fraction (LVEF) is a key indicator for stratifying heart attack risk.

    Purpose of the Study:

    • To develop a method for classifying ICM and DCM patients.
    • To stratify patients into low-risk (LR) and high-risk (HR) groups based on LVEF.
    • To analyze cardiorespiratory and cardiovascular interactions for improved diagnostic accuracy.

    Main Methods:

    • Extracted RR, SBP, and TTot time series from ECG, BP, and respiratory flow signals.
    • Transformed time series to a binary space and analyzed using Joint Symbolic Dynamics with word length three.
    • Reduced extracted parameters via correlation and statistical analysis; applied Principal Component Analysis (PCA) and Support Vector Machines (SVM).

    Main Results:

    • Achieved 85.7% accuracy in characterizing cardiorespiratory and cardiovascular interactions in ICM and DCM.
    • Successfully stratified patients into LR (LVEF>35%) and HR (LVEF≤35%) groups.
    • Identified key parameters from signal analysis for differentiating cardiomyopathy types.

    Conclusions:

    • The developed method effectively classifies cardiomyopathy patients using non-invasive physiological signals.
    • Machine learning approaches combined with signal processing offer a promising tool for risk stratification and disease characterization.
    • This approach can aid in personalized treatment strategies for cardiovascular disease patients.

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