Alzheimer’s Disease (AD) is a neurodegenerative disorder and the most common type of dementia with a great prevalence in western countries. The diagnosis of AD and its progression is performed through a variety of clinical procedures including neuropsychological and physical examination, Electroencephalographic (EEG) recording, brain imaging and blood analysis. During the last decades, analysis of the electrophysiological dynamics in AD patients has gained great research interest, as an alternative and cost-effective approach. This paper summarizes recent publications focusing on (a) AD detection and (b) the correlation of quantitative EEG features with AD progression, as it is estimated by Mini Mental State Examination (MMSE) score. A total of 49 experimental studies published from 2009 until 2020, which apply machine learning algorithms on resting state EEG recordings from AD patients, are reviewed. Results of each experimental study are presented and compared. The majority of the studies focus on AD detection incorporating Support Vector Machines, while deep learning techniques have not yet been applied on large EEG datasets. Promising conclusions for future studies are presented.

中文翻译：

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基于静息状态脑电图记录的阿尔茨海默病分析的机器学习算法和统计方法：系统评价

阿尔茨海默病 (AD) 是一种神经退行性疾病，是最常见的痴呆类型，在西方国家发病率很高。AD 的诊断及其进展通过多种临床程序进行，包括神经心理学和体格检查、脑电图 (EEG) 记录、脑成像和血液分析。在过去的几十年中，作为一种替代且具有成本效益的方法，AD 患者的电生理动力学分析引起了极大的研究兴趣。本文总结了最近的出版物，重点是 (a) AD 检测和 (b) 定量 EEG 特征与 AD 进展的相关性，因为它是通过迷你精神状态检查 (MMSE) 分数估计的。从 2009 年到 2020 年共发表了 49 项实验研究，回顾了将机器学习算法应用于 AD 患者的静息状态脑电图记录。呈现并比较了每个实验研究的结果。大多数研究集中在结合支持向量机的 AD 检测上，而深度学习技术尚未应用于大型 EEG 数据集。提出了未来研究的有希望的结论。