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Video s3
    Details
    Presenter(s)
    Tsai Chne-Wuen Headshot
    Display Name
    Tsai Chne-Wuen
    Affiliation
    Affiliation
    National University of Singapore
    Country
    Author(s)
    Display Name
    Lian Zhang
    Affiliation
    Affiliation
    National University of Singapore
    Display Name
    Miaolin Zhang
    Affiliation
    Affiliation
    National University of Singapore
    Display Name
    Tsai Chne-Wuen
    Affiliation
    Affiliation
    National University of Singapore
    Display Name
    Jerald Yoo
    Affiliation
    Affiliation
    National University of Singapore
    Abstract

    This paper reviews state-of-the-art AI-on-the-edge EEG-based patient-specific epilepsy tracking System-on-Chips (SoCs). For ambulatory tracking and effective treatment of neurological disorders such as seizure and epilepsy, long-term monitoring wearable SoCs are essential to “close the loop”. The design challenges at the Analog Front-End (AFE) (noise, power, signal fidelity, and scalability), as well as various techniques of feature extraction, classification, and online tuning to improve seizure detection accuracy at the Digital Back-End (DBE) are thoroughly analyzed from a system perspective. Furthermore, future trends of the epilepsy tracking system are discussed.

    Slides
    • Review of AI-on-the-Edge EEG-Based Patient-Specific Epilepsy Tracking Socs (application/pdf)