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    A new time-series segmentation (TSS) algorithm enables on-device seizure detection in neural implants, improving epilepsy management. This approach reduces data transmission needs and power consumption for long-term use.

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    Area of Science:

    • Biomedical Engineering
    • Neuroscience
    • Signal Processing

    Background:

    • Epilepsy impacts over 50 million globally, with many patients unresponsive to standard treatments.
    • Neural implants with on-device algorithms offer advanced, real-time seizure detection, reducing data transmission burdens.
    • Increasing electrode counts in implants demand efficient processing to manage data and power for long-term viability.

    Purpose of the Study:

    • To introduce a novel time-series segmentation (TSS) algorithm for extracting labeled information from raw neural recordings.
    • To develop an efficient on-device processing system for implantable epilepsy management devices.
    • To enhance seizure detection accuracy and enable clinical interpretation of local field potentials (LFPs).

    Main Methods:

    • Utilized time-series segmentation (TSS) with outlier detection and a heuristic event classifier.
    • Implemented a multichannel consensus strategy to improve detection accuracy via multichannel agreement.
    • Tested the algorithm on microelectrode array (MEA) recordings from 4-aminopyridine-treated mouse hippocampus-cortex slices.

    Main Results:

    • The system achieved high-performance seizure detection and segmented LFPs into clinically relevant labels.
    • Demonstrated robust reliability on experimental data.
    • Achieved 95% accuracy, 94% sensitivity, and a 0.03% false positive rate (FPR) with 128 mW power consumption on a Pynq-Z2 board.

    Conclusions:

    • The developed TSS algorithm is effective for on-device processing in implantable neural devices.
    • This approach advances personalized epilepsy treatment by enabling efficient, real-time seizure detection.
    • The system offers a low-power, high-accuracy solution for managing the increasing data demands of advanced neural implants.