Resting-state EEG (rs-EEG) has been demonstrated to aid in Parkinson's disease (PD) diagnosis. In particular, the power spectral density (PSD) of low-frequency bands ({\delta} and {\theta}) and high-frequency bands ({\alpha} and \b{eta}) has been shown to be significantly different in patients with PD as compared to subjects without PD (non-PD). However, rs-EEG feature extraction and the interpretation thereof can be time-intensive and prone to examiner variability. Machine learning (ML) has the potential to automatize the analysis of rs-EEG recordings and provides a supportive tool for clinicians to ease their workload. In this work, we use rs-EEG recordings of 84 PD and 85 non-PD subjects pooled from four datasets obtained at different centers. We propose an end-to-end pipeline consisting of preprocessing, extraction of PSD features from clinically validated frequency bands, and feature selection before evaluating the classification ability of the features via ML algorithms to stratify between PD and non-PD subjects. Further, we evaluate the effect of feature harmonization, given the multi-center nature of the datasets. Our validation results show, on average, an improvement in PD detection ability (69.6% vs. 75.5% accuracy) by logistic regression when harmonizing the features and performing univariate feature selection (k = 202 features). Our final results show an average global accuracy of 72.2% with balanced accuracy results for all the centers included in the study: 60.6%, 68.7%, 77.7%, and 82.2%, respectively.

翻译：静息态脑电图已被证明有助于帕金森病诊断。特别是，低频段（δ和θ）和高频段（α和β）的功率谱密度在帕金森病患者与无帕金森病受试者之间存在显著差异。然而，静息态脑电图特征提取及其解读既耗时又易受检查者主观差异影响。机器学习有望实现静息态脑电图记录分析的自动化，为临床医生提供支持性工具以减轻其工作负担。本研究汇集了来自四个不同中心数据集共84例帕金森病和85例非帕金森病受试者的静息态脑电图记录。我们提出了一种端到端处理流程，包括预处理、从临床验证频段提取功率谱密度特征、特征选择，随后通过机器学习算法评估这些特征的分类能力，以实现帕金森病与非帕金森病受试者分层。此外，针对数据集的多中心特性，我们评估了特征协调化的效果。验证结果显示，当进行特征协调化并采用单变量特征选择（k=202个特征）时，逻辑回归模型的帕金森病检测能力平均提升（准确率从69.6%提高至75.5%）。最终结果表明，总体平均准确率达72.2%，且各参与中心均取得平衡的准确率结果：分别为60.6%、68.7%、77.7%和82.2%。