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Updated: Aug 29, 2025

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
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Prenatal Cortisol Levels Estimation Using Heart Rate and Heart Rate Variability: A Weak Supervised Learning Based

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    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |September 10, 2022
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    Summary

    This study introduces a novel machine learning method to estimate salivary cortisol levels using heart rate (HR) and heart rate variability (HRV) data from pregnant women. This non-invasive approach offers a convenient alternative to traditional cortisol monitoring methods.

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    Area of Science:

    • Endocrinology
    • Biomedical Engineering
    • Machine Learning

    Background:

    • Cortisol, a vital steroid hormone, has crucial physiological roles but is difficult to monitor conveniently.
    • Existing cortisol measurement techniques are often invasive or impractical for daily use.
    • Heart Rate (HR) and Heart Rate Variability (HRV) show strong correlations with cortisol levels, yet have not been utilized for its estimation.

    Purpose of the Study:

    • To develop and validate a machine learning-based method for estimating salivary cortisol levels.
    • To explore the feasibility of using non-invasive HR and HRV signals for cortisol monitoring.
    • To establish a novel approach for assessing cortisol fluctuations in pregnant women.

    Main Methods:

    • Extraction of HR and HRV parameters from electrocardiogram (ECG) inter-beat-interval data.
    • Application of feature selection algorithms to identify significant HR and HRV contributors.
    • Implementation of a weak supervision machine learning model to handle imbalanced cortisol label data.
    • Utilizing five machine learning algorithms for binary classification (Baseline Cortisol vs. Cortisol Level 1/2) and a deep neural network for multi-class classification.

    Main Results:

    • Achieved up to 69% prediction accuracy for Baseline Cortisol (BL) vs. Cortisol Level 1 (CL1).
    • Obtained up to 71% prediction accuracy for BL vs. Cortisol Level 2 (CL2).
    • Reached 60% accuracy for classifying all three cortisol levels (BL, CL1, CL2).

    Conclusions:

    • This study presents the first machine learning-based method to estimate salivary cortisol using HR and HRV.
    • The findings demonstrate the potential of non-invasive physiological signals for cortisol level estimation.
    • This pioneering work opens avenues for convenient, real-time cortisol monitoring, particularly in prenatal care.