Epileptic Engineering: From Basic Neurophysiological Mechanisms to Closed Loop Intervention Devices

Abstract

Epileptic patients suffer from recurrent seizures characterized by transient manifestations due to excessive neuronal discharges, reminiscent of an electrical storm in the brain. Seizure manifestations may range from subtle sensations to dramatic life-threatening convulsions depending on the location, intensity and propagation of the discharge within distinct epileptic brain networks. The first line of treatment is the use of antiepileptic drugs but a third of patients are drug-resistant, up to half have poor adherence, and side effects are frequent. For drug-resistant patients, epilepsy surgery and neurostimulation may be considered but success rates remain modest notably due to limitations of current techniques to accurately delineate the epileptic focus and network.

In this keynote presentation, Dr. Nguyen will provide a brief overview of epilepsy, explain current challenges and unmet needs in the management of epileptic patients, and how the field of engineering is uniquely positioned to answer them: machine learning algorithms to improve interpretation of multimodal (clinical, neurophysiological and neuroimaging) data and help in decision making, implantable bio-sensors to improve drug monitoring and adjustments, quantitative connectivity approaches to characterize epileptic networks, tailor seizure onset zone localization and predict surgical outcome, seizure anticipation strategies (i.e. seizure detection and prediction) that could be implemented in closed-loop devices for advisory/intervention purposes.