Received August 15, 2020, accepted August 25, 2020, date of publication August 31, 2020, date of current version September 15, 2020. Digital Object Identifier 10.1109/ACCESS.2020.3020429 Neural Network-Based Closed-Loop Deep Brain Stimulation for Modulation of Pathological Oscillation in Parkinson’s Disease CHEN LIU 1 , (Member, IEEE), GE ZHAO 1 , JIANG WANG 1 , (Member, IEEE), HAO WU 2 , HUIYAN LI 3 , CHRIS FIETKIEWICZ 4 , AND KENNETH A. LOPARO 4 , (Life Fellow, IEEE) 1 School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China 2 School of Civil Engineering, Tianjin University, Tianjin 300072, China 3 School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300072, China 4 Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA Corresponding authors: Hao Wu ([email protected]) and Huiyan Li ([email protected]) This work was supported by the National Natural Science Foundation of China (Grant No. 61701336) and the Opening Foundation of Key Laboratory of Opto-Technology and Intelligent Control (Lanzhou Jiaotong University), Ministry of Education (Grant No. KFKT 2020-01). ABSTRACT Aiming at the problem that the Proportional-Integral-Derivative (PID) control strategy needs to readjust controller parameters for different Parkinson’s disease (PD) states. This work proposes an improved control strategy that considers an artificial neural network control scheme. A backpropagation neural network (BPNN) controller is designed to solve the above problem and further to improve the performance of the closed-loop control strategy. The training data set of the BPNN controller is obtained by controlling eight different PD states (PD a - PD h ) by the PID controller and the BPNN controller is trained by the training data set to obtain a set of optimal weights. By modulating other different PD states (e.g. PD1 - PD3), the effectiveness of the PID-structure controller and BPNN controller are compared. We find that the BPNN controller can modulate different PD states without changing the controller parameters and reduce energy expenditure by 58.26%. This work is helpful for the design of more effective closed-loop deep brain stimulation (DBS) systems for clinical applications and provides a framework for the further development of closed-loop DBS. INDEX TERMS Parkinsonian state, closed-loop deep brain stimulation, backpropagation neural network, energy expenditure. I. INTRODUCTION Parkinson’s disease (PD) is a neurodegenerative disease and its symptoms can be complicated to manage. The main motor symptoms of PD are loss of autonomic movement, decreased autonomous movement, muscle stiffness, and rest- ing tremor of the limb [1]. The currently recognized origin of PD is the degradation of the substantia nigra pars com- pacta (SNc) dopaminergic neurons. However, the pathogen- esis of PD is still unclear. It is generally believed that the Parkinsonian state may be related to abnormal synchronous oscillatory activity occurring in the basal ganglia-thalamo- cortical circuit [2]. The Parkinsonian state is associated with changes in neural activity, including altered neuron firing The associate editor coordinating the review of this manuscript and approving it for publication was Shuping He . rates, increased burst firings, and enhanced beta oscillatory activity across the basal ganglia-thalamo-cortical network [3], [4]. Therefore, these changes in neural firing activities are of great significance for guiding the research of PD. Deep brain stimulation (DBS) has the advantages of reversibility and adjustability, and is now increasingly favored by patients with refractory PD [5]–[7]. DBS, as a treatment approved by the US Food and Drug Administra- tion [8], stimulates basal ganglia nuclei (such as Subtha- lamo nucleus (STN), Globus Pallidus interna (GPi) [9]) and the ventral intermediate nucleus (Vim) [10] in open-loop form using continuous high-frequency (greater than 100Hz) electrical stimulation to achieve modulation of the patho- logical neuronal rhythm of networks in patients with PD [11]. Clinical studies have shown that DBS can achieve a remarkable effect and has become the first choice for patients VOLUME 8, 2020 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ 161067
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