A seizure tracking system is crucial for monitoring and evaluating epilepsy
treatments. Caretaker seizure diaries are used in epilepsy care today, but
clinical seizure monitoring may miss seizures. Monitoring devices that can be
worn may be better tolerated and more suitable for long-term ambulatory use.
Many techniques and methods are proposed for seizure detection; However,
simplicity and affordability are key concepts for daily use while preserving
the accuracy of the detection. In this study, we propose a versal, affordable
noninvasive based on a simple real-time k-Nearest-Neighbors (kNN) machine
learning that can be customized and adapted to individual users in less than
four seconds of training time; the system was verified and validated using 500
subjects, with seizure detection data sampled at 178 Hz, the operated with a
mean accuracy of (94.5%).Comment: Under review, 5 pages, 7 figures, 3 table