Automatic Seizure Detection in an In-Vivo Model of Epilepsy

The goal of our research is to find patterns of EEG activity that will allow us to correctly identify seizures in living rats using machine learning techniques. Features are extracted from the EEG to characterize the signal over time. We perform model selection to reduce the set of features, as the goal is to have the algorithm running on a small personal device. The chosen features are used within a supervised classifier, based on randomized forests, in order to separate the different brain states. One of the challenges of this research is to detect all seizures, while preserving a low false positive rate, and low detection latency. We present results showing we can achieve this using data from three separate animals. The long-term goal of this research is to use this seizure detection method as part of a closed-loop adaptive neuro-stimulation device to reduce the incidence and duration of seizures.