ArticleInternational journal of neural systems2009
Treating epilepsy via adaptive neurostimulation: a reinforcement learning approach.
Article in International journal of neural systems, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Harnessing electroencephalography connectomes for cognitive and clinical neuroscience.Nature biomedical engineering · 2025Review
- Review
- Toward Safety Protocols for Peripheral Nerve Stimulation (PNS): A Computational and Experimental Approach.Bioelectromagnetics · 2025Article
- Optimizing warfarin dosing for patients with atrial fibrillation using machine learning.Scientific reports · 2024Article
- Enhancing Medical Training Through Learning From Mistakes by Interacting With an Ill-Trained Reinforcement Learning Agent.IEEE transactions on learning technologies · 2024Article
- Electrical Stimulation Induced Current Distribution in Peripheral Nerves Varies Significantly with the Extent of Nerve Damage: A Computational Study Utilizing Convolutional Neural Network and Realistic Nerve Models.International journal of neural systems · 2023Article
- Closed-loop direct control of seizure focus in a rodent model of temporal lobe epilepsy via localized electric fields applied sequentially.Nature communications · 2022Article
- Modern views of machine learning for precision psychiatry.Patterns (New York, N.Y.) · 2022Review
- Optimizing the dynamic treatment regime of in-hospital warfarin anticoagulation in patients after surgical valve replacement using reinforcement learning.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Article
- Overcoming Challenges of Applying Reinforcement Learning for Intelligent Vehicle Control.Sensors (Basel, Switzerland) · 2021Article
- Computational medication regimen for Parkinson's disease using reinforcement learning.Scientific reports · 2021Article
- Multi-disease Deep Brain Stimulation.IEEE access : practical innovations, open solutions · 2020Article
- Deep Reinforcement Learning in Medicine.Kidney diseases (Basel, Switzerland) · 2019Review
- Gastroenterology Meets Machine Learning: Status Quo and Quo Vadis.Advances in bioinformatics · 2019Review
- Control of neural systems at multiple scales using model-free, deep reinforcement learning.Scientific reports · 2018Article
- Adaptive control of epileptiform excitability in an in vitro model of limbic seizures.Experimental neurology · 2013Article
- Evidence-based modeling of network discharge dynamics during periodic pacing to control epileptiform activity.Journal of neuroscience methods · 2012Article
- Progress in Neuroengineering for brain repair: New challenges and open issues.Brain and neuroscience advancesArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.