University Lille, Inserm, CHU Lille, Degenerative & Vascular Cognitive Disorders, Lille, France.
Centre Hospitalier Universitaire Lille, Clinical Neurophysiology Department, Lille, France.
Mov Disord. 2019 Feb;34(2):210-217. doi: 10.1002/mds.27528. Epub 2018 Oct 21.
Cognitive symptoms are common in patients with Parkinson's disease. Characterization of a patient's cognitive profile is an essential step toward the identification of predictors of cognitive worsening.
The aim of this study was to investigate the use of the combination of resting-state EEG and data-mining techniques to build characterization models.
Dense EEG data from 118 patients with Parkinson's disease, classified into 5 different groups according to the severity of their cognitive impairments, were considered. Spectral power analysis within 7 frequency bands was performed on the EEG signals. The obtained quantitative EEG features of 100 patients were mined using 2 machine-learning algorithms to build and train characterization models, namely, support vector machines and k-nearest neighbors models. The models were then blindly tested on data from 18 patients.
The overall classification accuracies were 84% and 88% for the support vector machines and k-nearest algorithms, respectively. The worst classifications were observed for patients from groups with small sample sizes, corresponding to patients with the severe cognitive deficits. Whereas for the remaining groups for whom an accurate diagnosis was required to plan the future healthcare, the classification was very accurate.
These results suggest that EEG features computed from a daily clinical practice exploration modality in-that it is nonexpensive, available anywhere, and requires minimal cooperation from the patient-can be used as a screening method to identify the severity of cognitive impairment in patients with Parkinson's disease. © 2018 International Parkinson and Movement Disorder Society.
认知症状在帕金森病患者中很常见。对患者认知特征进行描述是识别认知恶化预测因子的重要步骤。
本研究旨在探讨使用静息态 EEG 与数据挖掘技术相结合构建特征描述模型。
考虑了来自 118 名帕金森病患者的密集 EEG 数据，根据其认知障碍的严重程度将其分为 5 个不同组。对 EEG 信号进行了 7 个频带内的谱功率分析。使用 2 种机器学习算法（支持向量机和 k-最近邻算法）对 100 名患者的获得的定量 EEG 特征进行挖掘，以构建和训练特征描述模型。然后将这些模型在 18 名患者的数据上进行盲测。
支持向量机和 k-最近邻算法的总体分类准确率分别为 84%和 88%。对于来自样本量较小的组的患者（对应于认知功能严重受损的患者），分类效果最差。而对于其余需要准确诊断来规划未来医疗保健的组，分类非常准确。
这些结果表明，从日常临床实践探索模式（因为它便宜、随处可用且患者只需最小程度的配合）中计算出的 EEG 特征可作为一种筛查方法，用于识别帕金森病患者认知障碍的严重程度。 © 2018 国际帕金森病和运动障碍协会。
