Closed-Loop Modulation of Parkinson’s State Based on Fuzzy Control

doi:10.3778/j.issn.1002-8331.2006-0452

Abstract

Abstract:

The extensive energy consumption of open-loop Deep Brain Stimulation（DBS） therapy for Parkinson’s Disease（PD） can induce side effects, therefore this paper designs an adaptive closed-loop DBS method to adjust stimulation parameters in real-time according to the variation of clinical states. Firstly, the [β] band （13-35 Hz） oscillation power of the globus pallidus internal that is closely related to clinical state is selected as the feedback signal, and the dynamic [β] band power values changing with movement states are defined as the desired signal. Secondly, the fuzzy control algorithm with strong robustness is used to calculate the DBS parameters, after that the performance of fuzzy control is compared with that of proportional-integral control. Finally, the usability of this adaptive closed-loop DBS method is testified on a physiological model of the cortex-basal ganglia-thalamus network. When the [β] power generated by the open-loop 130 Hz DBS is taken as the desired value, the fuzzy controller can successfully track the expected power and reduce the average stimulation frequency to 108.77 Hz. Without changing the stimulus parameters and changing the expected [β] power value, the successful tracking can be achieved, which proves the robustness of the fuzzy controller. In this paper, a closed-loop DBS scheme based on fuzzy control for Parkinson’s state [β] band oscillation suppression is designed, which can track in real-time according to the power change of [β] band oscillation, reduce the side effects by reducing the energy consumption of open-loop stimulation, and provide a scheme reference for clinical closed-loop DBS to optimize PD therapy.

Key words: Parkinson’s Disease（PD）, closed-loop deep brain stimulation, fuzzy control, feedback signal, [β] band power

摘要：

针对帕金森疾病（Parkinson’s Disease，PD）开环深部脑刺激（Deep Brain Stimulation，DBS）疗法存在能耗过多而引起副作用的问题，提出根据患者临床状态变化实时调节刺激参数的自适应闭环DBS方案。选取与临床状态密切相关的内侧苍白球[β]频段（13~35?Hz）振荡功率作为反馈信号，定义随运动状态动态变化的[β]功率值作为参考信号；选取鲁棒性强的模糊控制算法实时求解DBS参数并与传统比例-积分算法的控制效果进行比较；应用皮层-基底核-丘脑网络生理模型验证所设计自适应闭环DBS方案的可行性。将开环130?Hz DBS产生的[β]功率作为期望值时，模糊控制器在成功跟踪期望功率的同时将平均刺激频率降为108.77?Hz，能够降低刺激能耗。在不改变刺激参数的情况下，改变期望的[β]功率值，均能实现成功跟踪，证明了模糊控制器的鲁棒性。设计的基于模糊控制的帕金森状态[β]频段振荡抑制的闭环DBS方案能够根据[β]频段振荡功率变化进行实时跟踪，通过降低开环刺激能耗减少副作用，为临床闭环DBS优化PD疗法提供方案参考。

关键词: 帕金森（PD）；闭环深部脑刺激；模糊控制；反馈信号；[&beta, ]频段功率

SU Fei, WANG Hong, ZU Linlu, WANG Jiang, LIU Chen. Closed-Loop Modulation of Parkinson’s State Based on Fuzzy Control[J]. Computer Engineering and Applications, 2021, 57(22): 273-280.

苏斐，王红，祖林禄，王江，刘晨. 基于模糊控制的帕金森状态闭环调节[J]. 计算机工程与应用, 2021, 57(22): 273-280.

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