Fuzzy neural network model for assessment of Alzheimer-type dementia

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

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

A system for assessing dementia of the Alzheimer type (DAT) from electroencephalogram (EEG) data by means of fuzzy neural networks (FNNs) was investigated. The system consisted of two FNN models, one to discriminate DAT patients from normal subjects and the other to estimate the severity of the DAT patients' symptoms. EEG data were collected using 15 electrodes attached to the scalp. The power spectra were calculated by the fast Fourier transform. For each electrode, the power spectrum was divided into 9 frequency bands and relative power values were calculated. The θ1 (4.0-6.0 Hz), θ2 (6.0-8.0 Hz), and α (8.0-13.0 Hz) band data were used as the network input values. DAT severity was assessed by the Mini-Mental State (MMS) examination administered to each patient and the results were used as the output. The FNN model for DAT patient discrimination correctly distinguished 94% of the DAT patients from normal subjects. The FNN model for severity estimation gave an average error of 2.57 points out of 30 in the MMS scores. The FNNs were found to be useful tools for discriminating DAT patients from normal subjects as well as for estimating quantitatively the severity of DAT symptoms from EEG data.

Original languageEnglish
Pages (from-to)936-942
Number of pages7
JournalJOURNAL OF CHEMICAL ENGINEERING OF JAPAN
Volume34
Issue number7
DOIs
Publication statusPublished - Jul 1 2001
Externally publishedYes

Fingerprint

Fuzzy neural networks
Electroencephalography
Power spectrum
Electrodes
Fast Fourier transforms
Frequency bands

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Chemical Engineering(all)

Cite this

Hibino, S., Hanai, T., Nagata, E., Matsubara, M., Fukagawa, K., Shirataki, T., ... Kobayashi, T. (2001). Fuzzy neural network model for assessment of Alzheimer-type dementia. JOURNAL OF CHEMICAL ENGINEERING OF JAPAN, 34(7), 936-942. https://doi.org/10.1252/jcej.34.936

Fuzzy neural network model for assessment of Alzheimer-type dementia. / Hibino, Shin; Hanai, Taizo; Nagata, Erika; Matsubara, Michitaka; Fukagawa, Kazutoshi; Shirataki, Tatsuaki; Honda, Hiroyuki; Kobayashi, Takeshi.

In: JOURNAL OF CHEMICAL ENGINEERING OF JAPAN, Vol. 34, No. 7, 01.07.2001, p. 936-942.

Research output: Contribution to journalArticle

Hibino, S, Hanai, T, Nagata, E, Matsubara, M, Fukagawa, K, Shirataki, T, Honda, H & Kobayashi, T 2001, 'Fuzzy neural network model for assessment of Alzheimer-type dementia', JOURNAL OF CHEMICAL ENGINEERING OF JAPAN, vol. 34, no. 7, pp. 936-942. https://doi.org/10.1252/jcej.34.936
Hibino, Shin ; Hanai, Taizo ; Nagata, Erika ; Matsubara, Michitaka ; Fukagawa, Kazutoshi ; Shirataki, Tatsuaki ; Honda, Hiroyuki ; Kobayashi, Takeshi. / Fuzzy neural network model for assessment of Alzheimer-type dementia. In: JOURNAL OF CHEMICAL ENGINEERING OF JAPAN. 2001 ; Vol. 34, No. 7. pp. 936-942.
@article{38b8503ef6c14fab9119090304fd8918,
title = "Fuzzy neural network model for assessment of Alzheimer-type dementia",
abstract = "A system for assessing dementia of the Alzheimer type (DAT) from electroencephalogram (EEG) data by means of fuzzy neural networks (FNNs) was investigated. The system consisted of two FNN models, one to discriminate DAT patients from normal subjects and the other to estimate the severity of the DAT patients' symptoms. EEG data were collected using 15 electrodes attached to the scalp. The power spectra were calculated by the fast Fourier transform. For each electrode, the power spectrum was divided into 9 frequency bands and relative power values were calculated. The θ1 (4.0-6.0 Hz), θ2 (6.0-8.0 Hz), and α (8.0-13.0 Hz) band data were used as the network input values. DAT severity was assessed by the Mini-Mental State (MMS) examination administered to each patient and the results were used as the output. The FNN model for DAT patient discrimination correctly distinguished 94{\%} of the DAT patients from normal subjects. The FNN model for severity estimation gave an average error of 2.57 points out of 30 in the MMS scores. The FNNs were found to be useful tools for discriminating DAT patients from normal subjects as well as for estimating quantitatively the severity of DAT symptoms from EEG data.",
author = "Shin Hibino and Taizo Hanai and Erika Nagata and Michitaka Matsubara and Kazutoshi Fukagawa and Tatsuaki Shirataki and Hiroyuki Honda and Takeshi Kobayashi",
year = "2001",
month = "7",
day = "1",
doi = "10.1252/jcej.34.936",
language = "English",
volume = "34",
pages = "936--942",
journal = "Journal of Chemical Engineering of Japan",
issn = "0021-9592",
publisher = "Society of Chemical Engineers, Japan",
number = "7",

}

TY - JOUR

T1 - Fuzzy neural network model for assessment of Alzheimer-type dementia

AU - Hibino, Shin

AU - Hanai, Taizo

AU - Nagata, Erika

AU - Matsubara, Michitaka

AU - Fukagawa, Kazutoshi

AU - Shirataki, Tatsuaki

AU - Honda, Hiroyuki

AU - Kobayashi, Takeshi

PY - 2001/7/1

Y1 - 2001/7/1

N2 - A system for assessing dementia of the Alzheimer type (DAT) from electroencephalogram (EEG) data by means of fuzzy neural networks (FNNs) was investigated. The system consisted of two FNN models, one to discriminate DAT patients from normal subjects and the other to estimate the severity of the DAT patients' symptoms. EEG data were collected using 15 electrodes attached to the scalp. The power spectra were calculated by the fast Fourier transform. For each electrode, the power spectrum was divided into 9 frequency bands and relative power values were calculated. The θ1 (4.0-6.0 Hz), θ2 (6.0-8.0 Hz), and α (8.0-13.0 Hz) band data were used as the network input values. DAT severity was assessed by the Mini-Mental State (MMS) examination administered to each patient and the results were used as the output. The FNN model for DAT patient discrimination correctly distinguished 94% of the DAT patients from normal subjects. The FNN model for severity estimation gave an average error of 2.57 points out of 30 in the MMS scores. The FNNs were found to be useful tools for discriminating DAT patients from normal subjects as well as for estimating quantitatively the severity of DAT symptoms from EEG data.

AB - A system for assessing dementia of the Alzheimer type (DAT) from electroencephalogram (EEG) data by means of fuzzy neural networks (FNNs) was investigated. The system consisted of two FNN models, one to discriminate DAT patients from normal subjects and the other to estimate the severity of the DAT patients' symptoms. EEG data were collected using 15 electrodes attached to the scalp. The power spectra were calculated by the fast Fourier transform. For each electrode, the power spectrum was divided into 9 frequency bands and relative power values were calculated. The θ1 (4.0-6.0 Hz), θ2 (6.0-8.0 Hz), and α (8.0-13.0 Hz) band data were used as the network input values. DAT severity was assessed by the Mini-Mental State (MMS) examination administered to each patient and the results were used as the output. The FNN model for DAT patient discrimination correctly distinguished 94% of the DAT patients from normal subjects. The FNN model for severity estimation gave an average error of 2.57 points out of 30 in the MMS scores. The FNNs were found to be useful tools for discriminating DAT patients from normal subjects as well as for estimating quantitatively the severity of DAT symptoms from EEG data.

UR - http://www.scopus.com/inward/record.url?scp=0035388801&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0035388801&partnerID=8YFLogxK

U2 - 10.1252/jcej.34.936

DO - 10.1252/jcej.34.936

M3 - Article

VL - 34

SP - 936

EP - 942

JO - Journal of Chemical Engineering of Japan

JF - Journal of Chemical Engineering of Japan

SN - 0021-9592

IS - 7

ER -