Treating epilepsy via adaptive neurostimulation: a reinforcement learning approach.
This paper presents a new methodology for automatically learning an optimal neurostimulation strategy for the treatment of epilepsy. The technical challenge is to automatically modulate neurostimulation parameters, as a function of the observed EEG signal, so as to minimize the frequency and duration of seizures. The methodology leverages recent techniques from the machine learning literature, in particular the reinforcement learning paradigm, to formalize this optimization problem. We present an algorithm which is able to automatically learn an adaptive neurostimulation strategy directly from labeled training data acquired from animal brain tissues. Our results suggest that this methodology can be used to automatically find a stimulation strategy which effectively reduces the incidence of seizures, while also minimizing the amount of stimulation applied. This work highlights the crucial role that modern machine learning techniques can play in the optimization of treatment strategies for patients with chronic disorders such as epilepsy.
Pineau
Joelle
J
School of Computer Science, McGill University, Montreal, QC, Canada. jpineau@cs.mcgill.ca
Guez
Arthur
A
Vincent
Robert
R
Panuccio
Gabriella
G
Avoli
Massimo
M
D015761:4-Aminopyridine / Q000494:pharmacology; D000465:Algorithms; D000818:Animals; D001703:Biophysics; D004195:Disease Models, Animal; D004599:Electric Stimulation Therapy / Q000379:methods*; D004569:Electroencephalography / Q000379:methods; D018728:Entorhinal Cortex / Q000503:physiopathology; D004827:Epilepsy / Q000139:chemically induced / Q000473:pathology / Q000628:therapy*; D066298:In Vitro Techniques; D007858:Learning / Q000502:physiology*; D008297:Male; D008328:Man-Machine Systems; D026902:Potassium Channel Blockers / Q000494:pharmacology; D051381:Rats; D017207:Rats, Sprague-Dawley; D012054:Reinforcement, Psychology
D026902:Potassium Channel Blockers; D015761:4-Aminopyridine