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