Journal
CLINICAL NEUROLOGY AND NEUROSURGERY
Volume 201, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.clineuro.2020.106446
Keywords
Mild cognitive impairment; Alzheimer's disease; Discrete wavelet transform; Fuzzy logic; Electroencephalograms
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Funding
- Italian Ministry of Health [GR-2011-02351397]
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A new methodology based on EEG data analysis is proposed for the differential diagnosis of AD, MCI, and healthy subjects, achieving an accuracy of 82.6-86.9%, sensitivity of 91%, and specificity of 87%.
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 %.
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