Machine Learning Based IoT Adaptive Architecture for Epilepsy Seizure Detection: Anatomy and Analysis
This work addresses the need for accessible and accurate seizure monitoring for epilepsy patients, though it appears incremental as it applies an existing method (kNN) to a specific domain.
The paper tackled the problem of epilepsy seizure detection by proposing a simple, affordable, and real-time k-Nearest-Neighbors (kNN) machine learning system that can be customized for individual users in under four seconds of training time, achieving a mean accuracy of 94.5% in validation with 500 subjects.
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%).