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目的动态检测与帕金森病治疗相关的丘脑底核场电位信号特征。方法基于小波包变换,对帕金森病患者丘脑底核局部场电位信号的同步化和模式特征进行分析,建立动态阈值判别模型,对丘脑底核功能状态进行判定。利用仿真实验优化检测方法参数,使用优化后的检测方法对服药前后患者丘脑底核场电位beta频段进行检测。结果帕金森病患者丘脑底核场电位信号beta频段中具有幅度高、随机程度低、规则程度高的特征模式。患者服药前后的比较结果表明药物对场电位信号的动态特性具有显著影响,且特征模式信号被检测出的总时间、次数、每次持续时间以及幅度等特征在患者服药后显著性降低。结论本文提出的方法可以动态检测出帕金森病患者特征模式信号,并量化患者脑功能状态。提供了一种可以作为实现自适应闭环深部脑刺激反馈判定依据的方法。
Objective To dynamically detect the signal characteristics of the subthalamic nucleus field potential associated with the treatment of Parkinson’s disease. Methods Based on the wavelet packet transform, the synchronization and pattern characteristics of local field potential signals in the subthalamic nucleus of Parkinson’s disease patients were analyzed. A dynamic threshold discriminant model was established to determine the functional status of the subthalamic nucleus. The simulation test was used to optimize the parameters of the detection method, and the optimized test method was used to detect the beta-band of the subthalamic nucleus before and after taking the drug. Results The pattern of high frequency, low degree of randomness and high degree of regularity in the beta band of the signal of the hypothalamus field in Parkinson’s disease patients. The results of patients before and after taking the drug showed that the drug had a significant effect on the dynamic characteristics of the field potential signal, and the total time, number of times, duration and amplitude of the characteristic pattern signal were significantly decreased after the patient took the drug. Conclusion The proposed method can dynamically detect characteristic pattern signals of patients with Parkinson’s disease and quantify the brain function of patients. Provides a method that can be used as a basis to determine adaptive closed-loop deep brain stimulation feedback.