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Classification of Valvular Heart Diseases based on Heart Rate Variability and Hjorth Parameters in

Szymon Siecinski, Ewaryst Tkacz, Marcin Grzegorzek

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed

    Insights

    This study classifies valvular heart disease using heart rate variability (HRV) and Hjorth parameters from ECG, SCG, and GCG signals. Efficient Logistic Regression achieved high accuracy in detecting mitral regurgitation and stenosis.

    Area of Science:

    • Biomedical Engineering
    • Cardiovascular Research
    • Signal Processing

    Background:

    • Valvular heart disease (VHD) is a growing public health concern due to increasing prevalence, reduced quality of life, and high healthcare costs.
    • Accurate and early detection of VHD is crucial for effective patient management and treatment.
    • Non-invasive methods for VHD assessment are highly desirable.

    Purpose of the Study:

    • To classify valvular heart diseases using heart rate variability (HRV) indices and Hjorth parameters.
    • To evaluate the efficacy of electrocardiograms (ECG), seismocardiography (SCG), and gyrocardiogram (GCG) signals for VHD classification.
    • To identify the most effective classification algorithms and features for different types of VHD.

    Main Methods:

    • Utilized a publicly available dataset of 30 concurrent ECG, SCG, and GCG signals with annotated heartbeats.
    • Employed the Classification Learner App in MATLAB R2024a for signal analysis and classification.
    • Applied Efficient Logistic Regression, a binary linear classifier, and analyzed features from time and frequency domains, including ellipse area.

    Main Results:

    • Achieved the highest classification accuracy for mitral regurgitation (86.7%) across all signal types.
    • Demonstrated high accuracy for mitral stenosis (83.3%-86.7%) and moderate accuracy for tricuspid regurgitation (70.0%-76.7%).
    • Identified frequency domain and ellipse area features as most relevant for mitral regurgitation and stenosis, while time domain features were key for tricuspid regurgitation using SCG and GCG.

    Conclusions:

    • Heart rate variability and Hjorth parameters derived from ECG, SCG, and GCG signals show promise for classifying valvular heart diseases.
    • Efficient Logistic Regression provides a viable classification method for VHD detection.
    • The study highlights the potential of multi-modal sensor data for improving the accuracy of VHD diagnosis.

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