Title
Treating epilepsy via adaptive neurostimulation : a reinforcement learning approachTreating epilepsy via adaptive neurostimulation : a reinforcement learning approach
Author
Faculty/Department
Faculty of Pharmaceutical, Biomedical and Veterinary Sciences . Biomedical Sciences
Research group
Theoretical neurobiology
Publication type
article
Publication
Singapore,
Subject
Biology
Human medicine
Source (journal)
International journal of neural systems. - Singapore
Volume/pages
19(2009):4, p. 227-240
ISSN
0129-0657
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Abstract
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.