一种基于脑电图的新型离散熵与模糊逻辑系统方法用于痴呆阶段的自动分类。

A New dispersion entropy and fuzzy logic system methodology for automated classification of dementia stages using electroencephalograms.

作者信息

Amezquita-Sanchez Juan P, Mammone Nadia, Morabito Francesco C, Adeli Hojjat

机构信息

Autonomous University of Queretaro (UAQ), Faculty of Engineering, Departments Biomedical and Electromechanical, Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, C. P. 76807, San Juan del Río, Qro., Mexico.

Department DICEAM of the Mediterranean University of Reggio Calabria, 89060, Reggio Calabria, Italy.

出版信息

Clin Neurol Neurosurg. 2021 Feb;201:106446. doi: 10.1016/j.clineuro.2020.106446. Epub 2020 Dec 29.

DOI:10.1016/j.clineuro.2020.106446

PMID:33383465

Abstract

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 %.

摘要

提出了一种基于脑电图（EEG）的新方法，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，该方法采用离散小波变换（DWT）、离散熵指数（DEI，一种最近提出的非线性测量方法）以及基于模糊逻辑的分类算法。通过使用从135名受试者（45名MCI患者、45名AD患者和45名健康受试者）获取的实测EEG数据数据库，对所提出方法的有效性和实用性进行了评估。所提出的方法区分MCI和AD患者与健康受试者的准确率为82.6 - 86.9%，灵敏度为91%，特异性为87%。