An Assessment System of Dementia of Alzheimer Type Using Artificial Neural Networks View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2000

AUTHORS

Shin Hibino , Taizo Hanai , Erika Nagata , Michitaka Matsubara , Kazutoshi Fukagawa , Tatsuaki Shirataki , Hiroyuki Honda , Takeshi Kobayashi

ABSTRACT

An assessment system of dementia of Alzheimer type (DAT) from electroencephalogram (EEG) was investigated. The system consisted of two artificial neural networks (ANN) models; a model for distinction of DAT patients from non-DAT patients and an estimation model of severity of the DAT. First, EEG data of the DAT patients and the non-DAT patients were collected using 15 electrodes on the scalp. Then, power spectrum of each data was calculated by the fast Fourier transform. The power spectrum was divided into 9 frequency bands, and relative power values were calculated. The regions with 4.0-6.0, 6.0-8.0 and 8.0-13.0 Hz were used to input. The severity of DAT was assessed by Hasegawa’s dementia rating scale (HDS-R). The relative power values and the averaged absolute power value were inputted into each ANN model. Using the acquired ANN model, DAT patients were distinguished from non-DAT patients completely. The average error of the ANN model for HDS-R score was 2.64 points out of 30. These models were found useful in order to distinguish DAT patients and quantify the severity of DAT from EEG. More... »

PAGES

180-185

Book

TITLE

Artificial Neural Networks in Medicine and Biology

ISBN

978-1-85233-289-1
978-1-4471-0513-8

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-1-4471-0513-8_26

DOI

http://dx.doi.org/10.1007/978-1-4471-0513-8_26

DIMENSIONS

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