Pineau Joelle, Guez Arthur, Vincent Robert, Panuccio Gabriella, Avoli Massimo
School of Computer Science, McGill University, Montreal, QC, Canada.
Int J Neural Syst. 2009 Aug;19(4):227-40. doi: 10.1142/S0129065709001987.
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.
本文提出了一种用于自动学习治疗癫痫的最佳神经刺激策略的新方法。技术挑战在于根据观察到的脑电图（EEG）信号自动调节神经刺激参数，以尽量减少癫痫发作的频率和持续时间。该方法利用了机器学习文献中的最新技术，特别是强化学习范式，来将此优化问题形式化。我们提出了一种算法，它能够直接从从动物脑组织获取的带标签训练数据中自动学习自适应神经刺激策略。我们的结果表明，这种方法可用于自动找到一种刺激策略，该策略能有效降低癫痫发作的发生率，同时还能尽量减少施加的刺激量。这项工作凸显了现代机器学习技术在优化癫痫等慢性疾病患者治疗策略中所能发挥的关键作用。
