Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG)

The early detection of subjects with probable Alzheimer's disease (AD) is crucial for effective appliance of treatment strategies. Here we explored the ability of a multitude of linear and non-linear classification algorithms to discriminate between the electroencephalograms (EEGs) of patients...

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Bibliographic Details
Published in:	Journal of neuroscience methods Vol. 161; no. 2; pp. 342 - 350
Main Authors:	Lehmann, Christoph, Koenig, Thomas, Jelic, Vesna, Prichep, Leslie, John, Roy E., Wahlund, Lars-Olof, Dodge, Yadolah, Dierks, Thomas
Format:	Journal Article
Language:	English
Published:	Netherlands Elsevier B.V 15.04.2007
Subjects:	Aged Aged, 80 and over Algorithms Alzheimer Disease - diagnosis Alzheimer Disease - physiopathology Alzheimer's disease Artificial Intelligence Brain - physiopathology Classification Cluster Analysis Diagnosis, Computer-Assisted - methods EEG Electroencephalography - methods Humans Middle Aged Multivariate statistics Pattern Recognition, Automated - methods Reproducibility of Results Sensitivity and Specificity Multivariate statistics Alzheimer's disease EEG Classification
ISSN:	0165-0270, 1872-678X
Online Access:	Get full text
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