Dynamic training of a novelty classifier algorithm for real-time detection of early seizure onset.

Objective: To develop an adaptive framework for seizure detection in real-time that is practical to use in the Epilepsy Monitoring Unit (EMU) as a warning signal, and whose output helps characterize epileptiform activity.

Methods: Our algorithm was tested on intracranial EEG from epilepsy patients admitted to the EMU for presurgical evaluation. Our framework uses a one-class Support Vector Machine (SVM) that is being trained dynamically according to past activity in all available channels to classify the novelty of the current activity. In this study we compared multiple configurations using a one-class SVM to assess if there is significance over specific neural features or electrode locations.

Results: Our results show that the algorithm reaches a sensitivity of 87% for early-onset seizure detection and of 97.7% as a generic seizure detection.

Conclusions: Our algorithm is capable of running in real-time and achieving a high performance for early seizure-onset detection with a low false positive rate and robustness in detection of different type of seizure-onset patterns.

Significance: This algorithm offers a solution to warning systems in the EMU as well as a tool for seizure characterization during post-hoc analysis of intracranial EEG data for surgical resection of the epileptogenic network.