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    Area of Science:

    • Biomedical Engineering
    • Cardiorespiratory Physiology
    • Machine Learning in Healthcare

    Background:

    • Obstructive sleep apnea (OSA) is a prevalent, often undiagnosed chronic condition with significant health implications.
    • Wearable health sensors offer a non-invasive method for continuous physiological data collection.
    • Current OSA detection methods may lack accessibility or comprehensive physiological modeling.

    Purpose of the Study:

    • To develop and validate a novel framework for obstructive sleep apnea (OSA) detection.
    • To integrate physiological signals from wearable sensors with mathematical cardiorespiratory models.
    • To improve the accuracy and accessibility of OSA diagnosis.

    Main Methods:

    • Utilized vector-valued Gaussian processes (GPs) to model inter-individual physiological variations.
    • Constructed GP covariance using sums of separable kernel functions.
    • Estimated GP hyperparameters via marginal likelihood maximization.
    • Implemented a likelihood ratio test for OSA detection using heart rate and SpO2 data.

    Main Results:

    • The proposed framework demonstrated effectiveness in detecting OSA on both synthetic and real-world datasets.
    • The combined approach of wearable sensor data and mathematical modeling showed superior performance compared to purely data-driven methods.
    • Validated the utility of heart rate and peripheral oxygen saturation for OSA detection.

    Conclusions:

    • The novel framework provides an effective and potentially more accessible method for obstructive sleep apnea detection.
    • Combining wearable sensor data with cardiorespiratory mathematical models offers a robust approach to diagnosing OSA.
    • This research highlights the potential of integrated physiological modeling and machine learning for sleep disorder diagnosis.