Parkinsonian Tremor Detection from Subthalamic Nucleus Local Field Potentials for Closed-Loop Deep Brain Stimulation
Parkinsonian Tremor Detection from Subthalamic Nucleus Local Field Potentials for Closed-Loop Deep Brain Stimulation
复制标题
闭环深部脑刺激的丘脑底核局部场电位的帕金森震颤检测
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
P. Brown
中科院分区：
文献类型：
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作者：
S. A. Shah;G. Tinkhauser;Chiung;S. Little;P. Brown
脑深部电刺激（DBS）是一种广泛使用的治疗方法，用于改善帕金森病（PD）患者的症状。常规DBS持续开启，即使PD症状随时间波动，导致不期望的副作用和高能量需求。本研究调查了使用基于逻辑回归的分类器来识别PD患者具有静息震颤的时期，该时期利用在7名PD患者（8个半球）的丘脑底核中植入DBS电极记录的局部场电位（LFP）。分析36.1分钟的数据与512毫秒的非重叠窗口，分类准确性远高于所有患者的机会水平，曲线下面积（AUC）范围为0.67至0.93。具有最大区分能力的特征按降序为31-45 Hz、5-7 Hz、21-30 Hz、46-55 Hz和56-95 Hz频带中的功率。这些结果表明，使用基于机器学习的分类器，例如本研究中提出的分类器，可以为PD震颤的按需DBS治疗奠定基础，并有可能减少副作用和降低电池消耗。
Deep Brain Stimulation (DBS) is a widely used therapy to ameliorate symptoms experienced by patients with Parkinson’s Disease (PD). Conventional DBS is continuously ON even though PD symptoms fluctuate over time leading to undesirable side-effects and high energy requirements. This study investigates the use of a Iogistic regression-based classifier to identify periods when PD patients have rest tremor exploiting Local Field Potentials (LFPs) recorded with DBS electrodes implanted in the Subthalamic Nucleus in 7 PD patients (8 hemispheres). Analyzing 36.1 minutes of data with a 512 milliseconds non-overlapping window, the classification accuracy was well above chance-level for all patients, with Area Under the Curve (AUC) ranging from 0.67 to 0.93. The features with the most discriminative ability were, in descending order, power in the 31–45 Hz, 5–7 Hz, 21–30 Hz, 46–55 Hz, and 56–95 Hz frequency bands. These results suggest that using a machine learning-based classifier, such as the one proposed in this study, can form the basis for on-demand DBS therapy for PD tremor, with the potential to reduce side-effects and lower battery consumption.