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Extraction of Risk Markers from ECG in Patients with Hypertrophic Cardiomyopathy

Marion Taconne, Valentina D A Corino, Alex Melot

    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

    New electrocardiogram (ECG) biomarkers can identify hypertrophic cardiomyopathy (HCM) patients at high risk for sudden cardiac death. These novel ECG features help stratify risk for arrhythmic events in HCM.

    Area of Science:

    • Cardiology
    • Biomedical Engineering
    • Medical Diagnostics

    Background:

    • Hypertrophic cardiomyopathy (HCM) is a primary cause of sudden cardiac death (SCD) in young individuals, with an annual risk of ~1%.
    • Electrocardiogram (ECG) abnormalities are common in HCM patients due to underlying structural and electrical changes, yet reliable ECG biomarkers for risk stratification are lacking.

    Purpose of the Study:

    • To extract novel morphological ECG biomarkers for differentiating HCM patients based on arrhythmic risk.
    • To assess the efficacy of these biomarkers in stratifying HCM patients into high- and low-risk categories for arrhythmic events.

    Main Methods:

    • Extraction of various ECG features, including morphological characteristics (width, amplitudes, slopes) and advanced signal processing techniques (Hermite transform, variational mode decomposition).
    • Feature selection using combined metrics, with median values as cutoffs to categorize patients.
    • Univariate Cox regression analysis to identify significant risk predictors.

    Main Results:

    • QRS and T wave-related ECG features demonstrated effectiveness in separating HCM patients into high and low arrhythmic risk groups.
    • Increased local QRS optima and a higher percentage of negative QRS were significantly associated with the highest risk of arrhythmic events (p<0.01 and p<0.05, respectively).

    Conclusions:

    • Automatic extraction of specific ECG features can effectively stratify hypertrophic cardiomyopathy patients according to their risk of arrhythmic events.
    • These findings offer potential tools for early identification of high-risk individuals, aiding in the prevention of sudden cardiac death.

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