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    GenClean is a new label-free framework that accurately detects and removes artefacts from medical time series data in real-time. This method improves the reliability of physiological monitoring by generalizing across patients and sites.

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    Area of Science:

    • Biomedical Engineering
    • Medical Informatics

    Background:

    • Artefacts in medical time series data can compromise clinical decisions.
    • Existing artefact detection methods often require supervision and fail to account for patient-level data variations.

    Purpose of the Study:

    • To introduce GenClean, a generalized, label-free framework for real-time artefact detection and cleaning in medical time series.
    • To evaluate the framework's performance across different patient populations and clinical sites.

    Main Methods:

    • Developed a generalized label-free framework (GenClean) for real-time artefact cleaning.
    • Trained and validated the framework using a large dataset of arterial blood pressure (ABP) samples.
    • Extended the method to photoplethysmography (PPG) signals.

    Main Results:

    • GenClean demonstrated robust performance under intra- and inter-patient distribution shifts.
    • The framework showed effective site-level generalization on the MIMIC-III database for ABP.
    • Successful extension to PPG signals indicates broad applicability.

    Conclusions:

    • GenClean offers a practical solution for real-time artefact cleaning in continuous physiological monitoring.
    • The framework enhances the reliability of high-resolution medical time series analysis.
    • GenClean shows potential for diverse medical pulsatile signals.