Objective: The objective of this work is the development of an accurate seizure forecasting algorithm that considers brain's functional connectivity for electrode selection.
Methods: We start by proposing Kmeans-directed transfer function, an adaptive functional connectivity method intended for seizure onset zone localization in bilateral intracranial EEG recordings. Electrodes identified as seizure activity sources and sinks are then used to implement a seizure-forecasting algorithm on long-term continuous recordings in dogs with naturally-occurring epilepsy. A precision-recall genetic algorithm is proposed for feature selection in line with a probabilistic support vector machine classifier.
Results: Epileptic activity generators were focal in all dogs confirming the diagnosis of focal epilepsy in these animals while sinks spanned both hemispheres in 2 of 3 dogs. Seizure forecasting results show performance improvement compared to previous studies, achieving average sensitivity of 84.82% and time in warning of 0.1.
Conclusion: Achieved performances highlight the feasibility of seizure forecasting in canine epilepsy.
Significance: The ability to improve seizure forecasting provides promise for the development of EEG-triggered closed-loop seizure intervention systems for ambulatory implantation in patients with refractory epilepsy.