Parkinson's Disease afflicts millions of individuals globally. Emerging as a promising brain rehabilitation therapy for Parkinson's Disease, Closed-loop Deep Brain Stimulation (CL-DBS) aims to alleviate motor symptoms. The CL-DBS system comprises an implanted battery-powered medical device in the chest that sends stimulation signals to the brains of patients. These electrical stimulation signals are delivered to targeted brain regions via electrodes, with the magnitude of stimuli adjustable. However, current CL-DBS systems utilize energy-inefficient approaches, including reinforcement learning, fuzzy interface, and field-programmable gate array (FPGA), among others. These approaches make the traditional CL-DBS system impractical for implanted and wearable medical devices. This research proposes a novel neuromorphic approach that builds upon Leaky Integrate and Fire neuron (LIF) controllers to adjust the magnitude of DBS electric signals according to the various severities of PD patients. Our neuromorphic controllers, on-off LIF controller, and dual LIF controller, successfully reduced the power consumption of CL-DBS systems by 19% and 56%, respectively. Meanwhile, the suppression efficiency increased by 4.7% and 6.77%. Additionally, to address the data scarcity of Parkinson's Disease symptoms, we built Parkinson's Disease datasets that include the raw neural activities from the subthalamic nucleus at beta oscillations, which are typical physiological biomarkers for Parkinson's Disease.

翻译：帕金森病困扰着全球数百万人。作为帕金森病一种前景广阔的大脑康复疗法，闭环深部脑刺激旨在缓解运动症状。CL-DBS系统包含一个植入胸腔的电池供电医疗设备，该设备向患者大脑发送刺激信号。这些电刺激信号通过电极传递至目标脑区，且刺激强度可调。然而，当前CL-DBS系统采用能效较低的方法，包括强化学习、模糊接口和现场可编程门阵列等。这些方法使得传统CL-DBS系统难以适用于植入式和可穿戴医疗设备。本研究提出了一种新颖的神经形态方法，该方法基于Leaky Integrate and Fire神经元控制器，根据帕金森病患者的不同严重程度调整DBS电信号的强度。我们设计的神经形态控制器——开关型LIF控制器和双LIF控制器——分别成功地将CL-DBS系统的功耗降低了19%和56%，同时抑制效率提升了4.7%和6.77%。此外，为应对帕金森病症状数据稀缺的问题，我们构建了帕金森病数据集，其中包含来自丘脑底核β振荡的原始神经活动数据，该振荡是帕金森病的典型生理生物标志物。