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    This study introduces a Kalman smoothing method to preprocess glucose time-series data, offering interpolated estimates and variance. It homogenizes data from various devices and corrects errors, improving glucose monitoring reliability.

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    Area of Science:

    • Biomedical Engineering
    • Data Science

    Background:

    • Glucose time-series data often suffer from irregular sampling and errors.
    • Homogenizing data from diverse glucose monitoring devices is challenging.
    • Existing methods may not adequately handle data quality issues.

    Purpose of the Study:

    • To present a Kalman smoothing-based method for preprocessing glucose time-series data.
    • To provide interpolated glucose estimates with associated variance.
    • To enable homogenization of data from different glucose measurement equipment.

    Main Methods:

    • Applied Kalman smoothing to irregularly sampled glucose time-series data.
    • Incorporated separate measurement noise parameters for device-specific homogenization.
    • Established a link between ISO 15197 and Kalman smoother noise variance for SMBG.
    • Developed a phaseless smoothing approach capable of automatic error correction.

    Main Results:

    • Generated interpolated glucose estimates with mean and variance.
    • Demonstrated data homogenization across different glucose measurement devices.
    • Showcased automatic correction of data fallouts and erroneous readings.
    • Validated the method on Self-Monitoring of Blood Glucose (SMBG) and Flash Glucose Monitor (FGM) data.

    Conclusions:

    • The Kalman smoothing method effectively preprocesses glucose time-series data, enhancing reliability.
    • The estimated variance aids in assessing data trustworthiness.
    • This method serves as a valuable preprocessing step for various glucose data analysis tasks.
    • A publicly available MATLAB implementation facilitates its application.