Publication
Title
Treating epilepsy via adaptive neurostimulation : a reinforcement learning approach
Author
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.
Language
English
Source (journal)
International journal of neural systems. - Singapore
Publication
Singapore : 2009
ISSN
0129-0657
Volume/pages
19:4(2009), p. 227-240
Full text (Publishers DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
External links
Record
Identification
Creation 31.10.2014
Last edited 19.11.2015