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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Vydané v:	Journal of neuroscience methods Ročník 161; číslo 2; s. 342 - 350
Hlavní autori:	Lehmann, Christoph, Koenig, Thomas, Jelic, Vesna, Prichep, Leslie, John, Roy E., Wahlund, Lars-Olof, Dodge, Yadolah, Dierks, Thomas
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Netherlands Elsevier B.V 15.04.2007
Predmet:	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
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