Treating Epilepsy via Adaptive Neurostimulation: a Reinforcement Learning Approach

By Joelle Pineau, Arthur Guez, Robert D. Vincent, G. Panuccio, Massimo Avoli. 2009

78 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Neurology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Shamim Nemati
2. Stéphane Ross
3. Gheorghe Comanici
4. Chao Yu
5. Jiming Liu
6. Monica Dinculescu

Citations in the corpus

• Reinforcement Learning in Healthcare: A Survey (2019, methodology): “eness of stimulation, while simultaneously minimizing the overall amount of stimulation applied thus reducing cell damage and preserving cognitive and neurological functions [157]. Guez et al. [158], [159], [160] applied the BRL method, FQI-ERT, to optimize a deepbrain stimulation strategy for the t”
• Optimization of anemia treatment in hemodialysis patients via reinforcement learning (2014, unknown): “uired to learn use ful policies. Recently there has been a growing interest in applying FQI to optimize the treatment of several diseases including HIV/AIDS [34], psychiatric disorders [35], epilepsy [36, 37], schizophrenia [38, 39] or smoking addiction [40]. To the authors’ knowledge, this is the fi”
• Adaptive control of epileptiform excitability in an in vitro model of limbic seizures (2013, unknown): “A full description of the mathematical and computational methods underlying the adaptive controller can be found in Guez, et al. (2008) and Pineau, et al. (2009).”
• Progress in Neuroengineering for brain repair: New challenges and open issues (2018, background): “, 2016) and adaptive algorithms for DBS technology to treat epileptic disorders (Panuccio et al., 2013; Pineau et al., 2009).”
• Adaptive Control of Epileptic Seizures using Reinforcement Learning (2010, result): “Part of the results in Chapter 4 are already published in [58].”
• Towards Safe Policy Improvement for Non-Stationary MDPs (2020, unknown): “For example, researchers have proposed the use of reinforcement learning algorithms for several medical support systems, ranging from diabetes management [5], to epilepsy [46], to sepsis treatment [50].”