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We present the implementation of seizure detection algorithms based on aminimal number of EEG channels on a parallel ultra-low-power embedded platform.The analyses are based on the CHB-MIT dataset, and include explorations ofdifferent classification approaches (Support Vector Machines, Random Forest,Extra Trees, AdaBoost) and different pre/post-processing techniques to maximizesensitivity while guaranteeing no false alarms. We analyze global andsubject-specific approaches, considering all 23-electrodes or only 4 temporalchannels. For 8s window size and subject-specific approach, we report zerofalse positives and 100% sensitivity. These algorithms are parallelized andoptimized for a parallel ultra-low power (PULP) platform, enabling 300h ofcontinuous monitoring on a 300 mAh battery, in a wearable form factor and powerbudget. These results pave the way for the implementation of affordable,wearable, long-term epilepsy monitoring solutions with low false-positive ratesand high sensitivity, meeting both patient and caregiver requirements.

Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices