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    This study introduces an adaptable Gaussian Mixture Model (GMM) to reliably track multiple cardiac event features from seismocardiogram (SCG) signals, improving real-time hemodynamic monitoring. The method enhances accuracy for cardiovascular evaluations, even with noise, aiding applications like trauma care.

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    Area of Science:

    • Biomedical Engineering
    • Cardiovascular Physiology
    • Signal Processing

    Background:

    • Wearable systems offer real-time cardiovascular evaluation by estimating hemodynamic indices.
    • Seismocardiogram (SCG) signals provide non-invasive estimation of key hemodynamic parameters linked to cardiac events like aortic valve opening (AO) and closing (AC).
    • Tracking single SCG features is unreliable due to physiological variability, motion artifacts, and external vibrations.

    Purpose of the Study:

    • To develop an adaptable Gaussian Mixture Model (GMM) for tracking multiple AO/AC correlated features from SCG signals in quasi-real-time.
    • To improve the reliability and accuracy of hemodynamic index extraction from SCG data.

    Main Methods:

    • An adaptable Gaussian Mixture Model (GMM) was employed to calculate the likelihood of extrema being AO/AC features for each SCG beat.
    • The Dijkstra algorithm was used to select heartbeat-related extrema.
    • A Kalman filter was utilized to update GMM parameters and filter features.
    • The system was tested on a porcine hypovolemia dataset with varying noise levels.

    Main Results:

    • The proposed algorithm demonstrated low tracking latency (4.5 ms) and improved Root Mean Square Errors (RMSE) for AO and AC features across different noise levels.
    • At 10 dB noise, RMSE was 1.47 ms for AO and 7.67 ms for AC; at -10 dB noise, it was 6.18 ms for AO and 15.3 ms for AC.
    • Combined RMSE for all tracked features remained within acceptable ranges, indicating robustness.

    Conclusions:

    • The adaptable GMM-based algorithm provides accurate and low-latency tracking of multiple SCG features, suitable for real-time cardiovascular monitoring.
    • This approach enhances the extraction of hemodynamic indices, supporting applications in critical care and field settings.
    • The method offers a reliable solution for overcoming challenges associated with SCG signal analysis.