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Consistent Ovulation Window Prediction based on Physiological Temporal Variability Patterns from Wearable Devices.

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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
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    Summary
    This summary is machine-generated.

    This study introduces a new ovulation prediction framework using heart rate variability and temperature data. The model accurately predicts ovulation, even for irregular cycles, improving fertility management.

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    Area of Science:

    • Biomedical Engineering
    • Reproductive Endocrinology
    • Data Science

    Background:

    • Accurate ovulation prediction is vital for fertility management but current methods, including calendar-based and some machine learning approaches, struggle with irregular menstrual cycles.
    • Existing machine learning models show decreased accuracy for irregular cycles, limiting their reliability in predicting the fertile window.

    Purpose of the Study:

    • To develop and validate an advanced ovulation prediction framework integrating multimodal physiological data.
    • To enhance the accuracy and reliability of ovulation prediction, particularly for individuals with irregular menstrual cycles.

    Main Methods:

    • A novel framework was developed by integrating temporal heart rate variability (HRV) patterns from electrocardiogram (ECG) data with high-resolution temperature measurements.
    • A Light Gradient Boosting Machine (LGBM) model was employed for ovulation prediction, utilizing features derived from both ECG and temperature data.
    • The prediction model focused on an 8-day window around ovulation (5 days prior to 2 days after) to capture critical physiological changes.

    Main Results:

    • The proposed framework achieved an overall Area Under the Receiver Operating Characteristic curve (AUROC) of 0.73, outperforming various other machine and deep learning models.
    • The model demonstrated superior performance in predicting ovulation for irregular cycles, with AUROC values of 0.84 for the highly irregular group and 0.88 for the undefined group.
    • The framework accurately predicts ovulation up to 5 days in advance for premenopausal women.

    Conclusions:

    • Temporal segmentation and multimodal feature integration, combining HRV and temperature data, are crucial for enhancing ovulation prediction accuracy.
    • The developed LGBM-based framework offers a significant improvement in ovulation prediction, especially for women with irregular cycles, thereby advancing fertility management.
    • This approach provides a reliable tool for predicting the fertile window, aiding in conception planning and reproductive health strategies.