Seizure Control in a Computational Model Using a Reinforcement Learning Stimulation Paradigm

By Vivek Nagaraj, Andrew G. Lamperski, Theoden I. Netoff. 2017

42 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Neurology

How researchers see this paper

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

1. Jiming Liu
2. Shamim Nemati
3. Hongyi Zhao
4. Chao Yu
5. Guoqi Ren
6. Pierre Humbert

Citations in the corpus

• Reinforcement Learning in Healthcare: A Survey (2019, background): “[162] proposed the first computational model that captures the transition from inter-ictal to ictal activity and applied naive Q-learning to optimize the stimulation frequency for controlling seizures with minimum stimulation.”
• Inverse reinforcement learning for intelligent mechanical ventilation and sedative dosing in intensive care units (2019, background): “…learning (RL) [1] has achieved tremendous achievements in solving complex sequential decision making problems in various health care domains, including treatment in HIV [2], cancer [3], diabetics [4], anaemia [5], schizophrenia [6], epilepsy [7], anesthesia [8], and sepsis [9], just to name a few.”