Clinician's Road Map to Wavelet EEG as an Alzheimer's disease Biomarker

Clinician's Road Map to Wavelet EEG as an Alzheimer's disease Biomarker

Author Medeiros Kanda, Paulo Afonso Google Scholar
Trambaiolli, Lucas R. Google Scholar
Lorena, Ana C. Autor UNIFESP Google Scholar
Fraga, Francisco J. Google Scholar
Basile, Luis Fernando I. Google Scholar
Nitrini, Ricardo Google Scholar
Anghinah, Renato Google Scholar
Institution Universidade de São Paulo (USP)
Universidade Federal do ABC (UFABC)
Universidade Federal de São Paulo (UNIFESP)
Abstract Alzheimer's disease (AD) is considered the main cause of dementia in Western countries. Consequently, there is a need for an accurate, universal, specific and cost-effective biomarker for early AD diagnosis, to follow disease progression and therapy response. This article describes a new diagnostic approach to quantitative electroencephalogram (QEEG) diagnosis of mild and moderate AD. the data set used in this study was composed of EEG signals recorded from 2 groups: (S1) 74 normal subjects, 33 females and 41 males (mean age 67 years, standard deviation = 8) and (S2) 88 probable AD patients (NINCDS-ADRDA criteria), 55 females and 33 males (mean age 74.7 years, standard deviation = 7.8) with mild to moderate symptoms (DSM-IV-TR). Attention is given to sample size and the use of state of the art open source tools (LetsWave and WEKA) to process the EEG data. This innovative technique consists in associating Morlet wavelet filter with a support vector machine technique. A total of 111 EEG features (attributes) were obtained for 162 probands. the results were accuracy of 92.72% and area under the curve of 0.92 (percentage split test). Most important, comparing a single patient versus the total data set resulted in accuracy of 84.56% (leave-one-patient-out test). Particular emphasis was on clinical diagnosis and feasibility of implementation of this low-cost procedure, because programming knowledge is not required. Consequently, this new method can be useful to support AD diagnosis in resource-limited settings.
Keywords quantitative EEG (QEEG)
Alzheimer's disease
wavelets
support vector machine (SVM)
Language English
Date 2014-04-01
Published in Clinical Eeg and Neuroscience. Thousand Oaks: Sage Publications Inc, v. 45, n. 2, p. 104-112, 2014.
ISSN 1550-0594 (Sherpa/Romeo, impact factor)
Publisher Sage Publications Inc
Extent 104-112
Origin http://dx.doi.org/10.1177/1550059413486272
Access rights Closed access
Type Article
Web of Science ID WOS:000337692100006
URI http://repositorio.unifesp.br/handle/11600/37649

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