Treating epilepsy via adaptive neurostimulation: a reinforcement learning approach.

Full 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.

Author information

Author/s: Pineau, Joelle (J); Guez, Arthur (A); Vincent, Robert (R); Panuccio, Gabriella (G); Avoli, Massimo (M);

Affiliation: School of Computer Science, McGill University, Montreal, QC, Canada. jpineau(-atsign-)cs.mcgill.ca

Journal and publication information

Publication Type: In Vitro; Journal Article; Research Support, Non-U.S. Gov't

Journal: International journal of neural systems (Int J Neural Syst), published in Singapore. (Language: eng)

Reference: 2009-Aug; vol 19 (issue 4) : pp 227-40

Dates: Created 2009/09/04; Completed 2009/10/06;

PMID: 19731397, status: MEDLINE (last retrieval date: 10/6/2009, IMS Date: )

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

External Links for this article
(including full text providers, if available):

Click Electronic Full-text Provider Links to see options for finding the electronic full text links to this article. Note there may be a subscription or fee required for access to the full text. See our FAQ for information on finding FREE full text articles.

This article may also be located in paper journal collections available in many libraries. Use the Journal and Publication Information above to find the full article.