Background. Primary progressive aphasia (PPA) is a neurodegenerative syndrome in which diagnosis is usually challenging. Biomarkers are needed for diagnosis and monitoring. In this study, we aimed to evaluate Electroencephalography (EEG) as a biomarker for the diagnosis of PPA. Methods. We conducted a cross-sectional study with 40 PPA patients categorized as non-fluent, semantic, and logopenic variants, and 20 controls. Resting-state EEG with 32 channels was acquired and preprocessed using several procedures (quantitative EEG, wavelet transformation, autoencoders, and graph theory analysis). Seven machine learning algorithms were evaluated (Decision Tree, Elastic Net, Support Vector Machines, Random Forest, K-Nearest Neighbors, Gaussian Naive Bayes, and Multinomial Naive Bayes). Results. Diagnostic capacity to distinguish between PPA and controls was high (accuracy 75%, F1-score 83% for kNN algorithm). The most important features in the classification were derived from network analysis based on graph theory. Conversely, discrimination between PPA variants was lower (Accuracy 58% and F1-score 60% for kNN). Conclusions. The application of ML to resting-state EEG may have a role in the diagnosis of PPA, especially in the differentiation from controls. Future studies with high-density EEG should explore the capacity to distinguish between PPA variants.

中文翻译：

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机器学习在脑电图诊断原发性进行性失语症中的应用：一项初步研究

背景。原发性进行性失语症 (PPA) 是一种神经退行性疾病，其诊断通常具有挑战性。诊断和监测需要生物标志物。在本研究中，我们旨在评估脑电图 (EEG) 作为 PPA 诊断的生物标志物。方法。我们对 40 名 PPA 患者进行了横断面研究，这些患者被归类为不流利、语义和 logopenic 变体，以及 20 名对照。使用多种程序（定量脑电图、小波变换、自动编码器和图论分析）获取和预处理具有 32 个通道的静息状态脑电图。评估了七种机器学习算法（决策树、弹性网络、支持向量机、随机森林、K-最近邻、高斯朴素贝叶斯和多项朴素贝叶斯）。结果。区分 PPA 和对照的诊断能力很高（准确率为 75%，kNN 算法的 F1-score 为 83%）。分类中最重要的特征来自基于图论的网络分析。相反，PPA 变体之间的区别较低（kNN 的准确度为 58%，F1-score 为 60%）。结论。ML 在静息态 EEG 中的应用可能在 PPA 的诊断中发挥作用，尤其是在与对照组的鉴别中。未来的高密度 EEG 研究应该探索区分 PPA 变体的能力。