A new EEG-based methodology is presented for differential diagnosis of the Alzheimer's disease (AD), Mild Cognitive Impairment (MCI), and healthy subjects employing the discrete wavelet transform (DWT), dispersion entropy index (DEI), a recently-proposed nonlinear measurement, and a fuzzy logic-based classification algorithm. The effectiveness and usefulness of the proposed methodology are evaluated by employing a database of measured EEG data acquired from 135 subjects, 45 MCI, 45 AD and 45 healthy subjects. The proposed methodology differentiates MCI and AD patients from HC subjects with an accuracy of 82.6-86.9%, sensitivity of 91 %, and specificity of 87 %.

中文翻译：

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一种新的离散熵和模糊逻辑系统方法，用于使用脑电图自动分类痴呆阶段

提出了一种新的基于脑电图的方法，用于阿尔茨海默病 (AD)、轻度认知障碍 (MCI) 和健康受试者的鉴别诊断，采用离散小波变换 (DWT)、色散熵指数 (DEI)，这是最近提出的非线性测量和基于模糊逻辑的分类算法。通过使用从 135 名受试者、45 名 MCI、45 名 AD 和 45 名健康受试者获得的测量 EEG 数据的数据库来评估所提出的方法的有效性和实用性。所提出的方法以 82.6-86.9% 的准确度、91% 的灵敏度和 87% 的特异性将 MCI 和 AD 患者与 HC 受试者区分开来。