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A Fuzzy Inference System for Closed-Loop Deep Brain Stimulation in Parkinson’s Disease

机译:帕金森氏病闭环深层脑刺激的模糊推理系统

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摘要

Parkinsons disease is a complex neurodegenerative disorder for which patients present many symptoms, tremor being the main one. In advanced stages of the disease, Deep Brain Stimulation is a generalized therapy which can significantly improve the motor symptoms. However despite its beneficial effects on treating the symptomatology, the technique can be improved. One of its main limitations is that the parameters are fixed, and the stimulation is provided uninterruptedly, not taking into account any fluctuation in the patients state. A closed-loop system which provides stimulation by demand would adjust the stimulation to the variations in the state of the patient, stimulating only when it is necessary. It would not only perform a more intelligent stimulation, capable of adapting to the changes in real time, but also extending the devices battery life, thereby avoiding surgical interventions. In this work we design a tool that learns to recognize the principal symptom of Parkinsons disease and particularly the tremor. The goal of the designed system is to detect the moments the patient is suffering from a tremor episode and consequently to decide whether stimulation is needed or not. For that, local field potentials were recorded in the subthalamic nucleus of ten Parkinsonian patients, who were diagnosed with tremor-dominant Parkinsons disease and who underwent surgery for the implantation of a neurostimulator. Electromyographic activity in the forearm was simultaneously recorded, and the relation between both signals was evaluated using two different synchronization measures. The results of evaluating the synchronization indexes on each moment represent the inputs to the designed system. Finally, a fuzzy inference system was applied with the goal of identifying tremor episodes. Results are favourable, reaching accuracies of higher 98.7 % in 70 % of the patients.
机译:帕金森病是一种复杂的神经退行性疾病,患者表现出许多症状,其中以震颤为主要症状。在疾病的晚期,深部脑刺激是一种可以显着改善运动症状的通用疗法。然而,尽管其对治疗症状有有益的作用,但该技术仍可改进。它的主要限制之一是参数是固定的,并且可以不间断地提供刺激,而不考虑患者状态的任何波动。提供按需刺激的闭环系统将根据患者状态的变化调整刺激,仅在必要时进行刺激。它不仅可以执行更智能的刺激,能够实时适应变化,而且可以延长设备的电池寿命,从而避免手术干预。在这项工作中,我们设计了一种工具来学习识别帕金森病(尤其是震颤)的主要症状。设计的系统的目标是检测患者遭受震颤发作的时刻,从而确定是否需要刺激。为此,在十名帕金森病患者的丘脑下核中记录了局部电场的潜能,这些患者被诊断患有震颤性帕金森病,并接受了植入神经刺激器的手术。同时记录前臂中的肌电活动,并使用两种不同的同步方法评估两个信号之间的关系。在每个时刻评估同步索引的结果代表了设计系统的输入。最后,应用模糊推理系统来识别震颤发作。结果令人满意,在70%的患者中准确率达到98.7%。

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