Diagnostic Implications of EEG Analysis in Patients with Dementia

Author

Hudson, D.L. ; Cohen, M.E. ; Kramer, M. ; Szeri, A. ; Chang, F.L.

Author_Institution

California Univ., San Francisco, CA

fYear

2005

fDate

16-19 March 2005

Firstpage

629

Lastpage

632

Abstract

New methods of electroencephalogram (EEG) analysis show promise in differentiating among types of dementia. While these measures alone are useful, their diagnostic contribution increases when combined with clinical parameters using higher order decision models such as neural network models and hybrid systems. Three categories of patients are included in the current study, Alzheimer´s patients (AD), minimal cognitive impairment (MCI), and normal controls. Results show that patients can be categorized accurately using the combination of EEG synchronization results and selected clinical parameters

Keywords

diseases; electroencephalography; medical signal processing; neural nets; patient diagnosis; synchronisation; Alzheimer patients; EEG analysis; EEG synchronization; dementia; diagnostic implications; hybrid systems; minimal cognitive impairment; neural network models; Alzheimer´s disease; Biological neural networks; Brain modeling; Dementia; Electroencephalography; Parkinson´s disease; Scalp; Signal analysis; Spatial resolution; Wavelet analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Engineering, 2005. Conference Proceedings. 2nd International IEEE EMBS Conference on

Conference_Location

Arlington, VA

Print_ISBN

0-7803-8710-4

Type

conf

DOI

10.1109/CNE.2005.1419703

Filename

1419703