Model-based design of closed loop deep brain stimulation controller using reinforcement learning

Conference Paper

Parkinson's disease (PD) currently Influences around one million people in the US. Deep brain stimulation (DBS) is a surgical treatment for the motor symptoms of PD that delivers electrical stimulation to the basal ganglia (BG) region of the brain. Existing commercial DBS devices employ stimulation based only on fixed-frequency periodic pulses. While such periodic high-frequency DBS controllers provide effective relief of PD symptoms, they are very inefficient in terms of energy consumption, and the lifetime of these battery- operated devices is limited to 4 years. Furthermore, fixed high- frequency stimulation may have side effects, such as speech impairment. Consequently, there is a need to move beyond (1) fixed stimulation pulse controllers, and (2) 'one-size-fits- all' patient-agnostic treatments, to provide energy efficient and effective (in terms of relieving PD symptoms) DBS controllers. In this work, we introduce a deep reinforcement learning (RL)- based approach that can derive patient-specific DBS patterns that are both effective in reducing a model-based proxy for PD symptoms, as well as energy-efficient. Specifically, we model the BG regions as a Markov decision process (MDP), and define the state and action space as state of the neurons in the BG regions and the stimulation patterns, respectively. Thereafter, we define the reward functions over the state space, and the learning objective is set to maximize the accumulated reward over a finite horizon (i.e., the treatment duration), while bounding average stimulation frequency. We evaluate the performance of our methodology using a Brain-on-Chip (BoC) FPGA platform that implements the physiologically-relevant basal ganglia model (BGM). We show that our RL-based DBS controllers significantly outperform existing fixed frequency controllers in terms of energy efficiency (e.g., by using 70% less energy than common periodic controllers), while providing suitable reduction of model-based proxy for PD symptoms.

Full Text

Duke Authors

Cited Authors

  • Gao, Q; Naumann, M; Jovanov, I; Lesi, V; Kamaravelu, K; Grill, WM; Pajic, M

Published Date

  • April 1, 2020

Published In

  • Proceedings 2020 Acm/Ieee 11th International Conference on Cyber Physical Systems, Iccps 2020

Start / End Page

  • 108 - 118

International Standard Book Number 13 (ISBN-13)

  • 9781728155012

Digital Object Identifier (DOI)

  • 10.1109/ICCPS48487.2020.00018

Citation Source

  • Scopus