Identification of early-stage Alzheimer's disease using SFAM neural network

被引：4

作者：

Ben Ali, Jaouher ^{[1
]}

Abid, Saber ^{[1
]}

Jervis, Barrie William ^{[2
]}

Fnaiech, Farhat ^{[1
]}

Bigan, Cristin ^{[3
]}

Besleaga, Mircea ^{[4
]}

机构：

[1] Univ Tunis, Higher Sch Sci & Tech Tunis, Lab Signal Image & Energy Mastery SIME, Tunis 1008, Tunisia

[2] Sheffield Hallam Univ, Sheffield S1 1WB, S Yorkshire, England

[3] Ecol Univ Bucharest, Bucharest, Romania

[4] Romanian Soc Clin Neurophysiol, Bucharest, Romania

来源：

NEUROCOMPUTING | 2014年 / 143卷

关键词：

Alzheimer's disease (AD); ARTMAP-familiarity discrimination (ARTMAP-FD); Auditory event-related potential (AERP); SFAM; EEG; RECOGNITION; P300; LATENCY;

D O I：

10.1016/j.neucom.2014.06.008

中图分类号：

TP18 [人工智能理论];

学科分类号：

140502 [人工智能];

摘要：

Alzheimer's disease (AD), the most common form of dementia, is a complex and very serious nervous disease. Currently no medication is really effective against it. Even, its diagnosis and management remain challenging research problem for scientists. This paper aims towards identifying early-stage AD based upon the characteristics of the non-oscillatory independent components (ICs) of the auditory event related potential (AERP) waveforms of an oddball task for healthy and newly diagnosed AD subjects. Using 27 sensors to record P300 Evoked Potentials (EPs) as features vectors to train the simplified fuzzy adaptive resonance theory map (SFAM) neural network as a classifier, normal and AD subjects were classified with higher than 95% of success. The use of the ARTMAP-familiarity discrimination (ARTMAP-FD) shows that the separation of the two populations was achieved with high success and without any mistake. (C) 2014 Elsevier B.V. All rights reserved.

引用

页码：170 / 181

页数：12

共 39 条

[1]

AN INFORMATION MAXIMIZATION APPROACH TO BLIND SEPARATION AND BLIND DECONVOLUTION [J].

BELL, AJ ;

SEJNOWSKI, TJ .

NEURAL COMPUTATION, 1995, 7 (06) :1129-1159

[2]

Early Detection of Alzheimer's Disease with PET Imaging [J].

Berti, V. ;

Osorio, R. S. ;

Mosconi, L. ;

Li, Y. ;

De Santi, S. ;

de Leon, M. J. .

NEURODEGENERATIVE DISEASES, 2010, 7 (1-3) :131-135

[3]

Carpenter G.A., 1991, PATTERN RECOGN

[4]

A MASSIVELY PARALLEL ARCHITECTURE FOR A SELF-ORGANIZING NEURAL PATTERN-RECOGNITION MACHINE [J].

CARPENTER, GA ;

GROSSBERG, S .

COMPUTER VISION GRAPHICS AND IMAGE PROCESSING, 1987, 37 (01) :54-115

[5]

Carpenter GA, 1997, 1997 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, P1459, DOI 10.1109/ICNN.1997.614010

[6]

Extraction of superimposed evoked potentials by combination of independent component analysis and cumulant-based matched filtering [J].

Cichocki, A ;

Gharieb, RR ;

Mourad, N .

2001 IEEE WORKSHOP ON STATISTICAL SIGNAL PROCESSING PROCEEDINGS, 2001, :237-240

[7]

INDEPENDENT COMPONENT ANALYSIS, A NEW CONCEPT [J].

COMON, P .

SIGNAL PROCESSING, 1994, 36 (03) :287-314

[8]

A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG [J].

Dauwels, J. ;

Vialatte, F. ;

Musha, T. ;

Cichocki, A. .

NEUROIMAGE, 2010, 49 (01) :668-693

[9]

De Lathauwer L, 2000, J CHEMOMETR, V14, P123, DOI 10.1002/1099-128X(200005/06)14:3<123::AID-CEM589>3.0.CO

[10]

2-1

← 1 2 3 4 →