Learning-Based Control Design for Deep Brain Stimulation

Ilija Jovanov, Michael Naumann, Karthik Kumaravelu, Vuk Lesi, Aditya Zutshi, W. Grill, M. Pajic
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引用次数: 2

Abstract

By employing low-voltage electrical stimulation of the basal ganglia (BG) regions of the brain, deep brain stimulation (DBS) devices are used to alleviate the symptoms of several neurological disorders, including Parkinson's disease (PD). Recently, we have developed a Basal Ganglia Model (BGM) that can be utilized for design and evaluation of DBS devices. In this work, we focus on the use of a hardware (FPGA) implementation of the BGM platform to facilitate development of new control policies. Specifically, we introduce a design-time framework that allows for development of suitable control policies, in the form of electrical pulses with variable temporal patterns, while supporting tradeoffs between energy efficiency and efficacy (i.e., Quality-of-Control) of the therapy. The developed framework exploits machine learning and optimization based methods for design-space exploration where predictive behavior for any control configuration (i.e., temporal pattern) is obtained using the BGM platform that simulates physiological response to the considered control in real-time. To illustrate the use of the developed framework, in our demonstration we present how the BGM can be utilized for physiologically relevant BG modeling and design-state exploration for DBS controllers, as well as show the effectiveness of obtained controllers that significantly outperform conventional DBS controllers.
基于学习的深部脑刺激控制设计
通过对大脑基底神经节(BG)区域进行低压电刺激,深部脑刺激(DBS)装置被用于缓解包括帕金森病(PD)在内的几种神经系统疾病的症状。最近,我们开发了一个基底神经节模型(BGM),可以用于DBS装置的设计和评估。在这项工作中,我们专注于使用硬件(FPGA)实现BGM平台,以促进新控制策略的开发。具体来说,我们引入了一个设计时框架,允许以具有可变时间模式的电脉冲形式开发合适的控制策略,同时支持治疗的能源效率和功效(即控制质量)之间的权衡。开发的框架利用基于机器学习和优化的方法进行设计空间探索,其中使用BGM平台实时模拟对所考虑的控制的生理反应,获得任何控制配置(即时间模式)的预测行为。为了说明开发的框架的使用,在我们的演示中,我们展示了BGM如何用于生理学相关的BG建模和DBS控制器的设计状态探索,并展示了获得的控制器的有效性,其显著优于传统的DBS控制器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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