Early Detection Method of Alzheimer’s Disease Using EEG Signals

Dhiya Al-jumeily, Shamaila Iram, Abir Jaffar Hussain, Vialatte Francois-benois, Paul Fergus

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Different studies have stated that electroencephalogram signals in Alzheimer’s disease patients usually have less synchronization as compare to healthy subjects. Changes in electroencephalogram signals start at early stage but clinically, these changes are not easily detected. To detect this perturbation, three neural synchrony measurement techniques have been examined with three different sets of data. This research work have successfully reported the experiment of comparing right and left temporal of brain with the rest of the brain area (frontal, central and occipital), as temporal regions are relatively the first ones to be affected by Alzheimer’s disease. A new approach using principal component analysis before applying neural synchrony measurement techniques has been presented and compared with to other existing techniques. The simulation results indicated that applying principal component analysis before synchrony measurement techniques show significantly improvement over the lateral one. The results of the experiments were analyzed using Mann-Whitney U test.
Original languageEnglish
Title of host publicationIntelligent Computing in Bioinformatics
Subtitle of host publication10th International Conference, ICIC 2014, Taiyuan, China, August 3-6, 2014. Proceedings
EditorsDe-Shuang Huang, Kyungsook Han, Michael Gromiha
PublisherSpringer, Cham
Pages25-33
ISBN (Electronic)9783319093307
ISBN (Print)9783319093291
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event10th International Conference on Intelligent Computing - Taiyuan, China
Duration: 3 Aug 20146 Aug 2014
Conference number: 10

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume8590
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on Intelligent Computing
Abbreviated titleICIC 2014
CountryChina
CityTaiyuan
Period3/08/146/08/14

Fingerprint

Electroencephalography
Principal component analysis
Brain
Synchronization
Experiments

Cite this

Al-jumeily, D., Iram, S., Hussain, A. J., Francois-benois, V., & Fergus, P. (2014). Early Detection Method of Alzheimer’s Disease Using EEG Signals. In D-S. Huang, K. Han, & M. Gromiha (Eds.), Intelligent Computing in Bioinformatics: 10th International Conference, ICIC 2014, Taiyuan, China, August 3-6, 2014. Proceedings (pp. 25-33). (Lecture Notes in Computer Science; Vol. 8590). Springer, Cham. https://doi.org/10.1007/978-3-319-09330-7_4
Al-jumeily, Dhiya ; Iram, Shamaila ; Hussain, Abir Jaffar ; Francois-benois, Vialatte ; Fergus, Paul. / Early Detection Method of Alzheimer’s Disease Using EEG Signals. Intelligent Computing in Bioinformatics: 10th International Conference, ICIC 2014, Taiyuan, China, August 3-6, 2014. Proceedings. editor / De-Shuang Huang ; Kyungsook Han ; Michael Gromiha. Springer, Cham, 2014. pp. 25-33 (Lecture Notes in Computer Science).
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title = "Early Detection Method of Alzheimer’s Disease Using EEG Signals",
abstract = "Different studies have stated that electroencephalogram signals in Alzheimer’s disease patients usually have less synchronization as compare to healthy subjects. Changes in electroencephalogram signals start at early stage but clinically, these changes are not easily detected. To detect this perturbation, three neural synchrony measurement techniques have been examined with three different sets of data. This research work have successfully reported the experiment of comparing right and left temporal of brain with the rest of the brain area (frontal, central and occipital), as temporal regions are relatively the first ones to be affected by Alzheimer’s disease. A new approach using principal component analysis before applying neural synchrony measurement techniques has been presented and compared with to other existing techniques. The simulation results indicated that applying principal component analysis before synchrony measurement techniques show significantly improvement over the lateral one. The results of the experiments were analyzed using Mann-Whitney U test.",
keywords = "Alzheimer’s Disease, EEG Signals, Electroencephalogram signals",
author = "Dhiya Al-jumeily and Shamaila Iram and Hussain, {Abir Jaffar} and Vialatte Francois-benois and Paul Fergus",
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Al-jumeily, D, Iram, S, Hussain, AJ, Francois-benois, V & Fergus, P 2014, Early Detection Method of Alzheimer’s Disease Using EEG Signals. in D-S Huang, K Han & M Gromiha (eds), Intelligent Computing in Bioinformatics: 10th International Conference, ICIC 2014, Taiyuan, China, August 3-6, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8590, Springer, Cham, pp. 25-33, 10th International Conference on Intelligent Computing, Taiyuan, China, 3/08/14. https://doi.org/10.1007/978-3-319-09330-7_4

Early Detection Method of Alzheimer’s Disease Using EEG Signals. / Al-jumeily, Dhiya; Iram, Shamaila; Hussain, Abir Jaffar; Francois-benois, Vialatte; Fergus, Paul.

Intelligent Computing in Bioinformatics: 10th International Conference, ICIC 2014, Taiyuan, China, August 3-6, 2014. Proceedings. ed. / De-Shuang Huang; Kyungsook Han; Michael Gromiha. Springer, Cham, 2014. p. 25-33 (Lecture Notes in Computer Science; Vol. 8590).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Early Detection Method of Alzheimer’s Disease Using EEG Signals

AU - Al-jumeily, Dhiya

AU - Iram, Shamaila

AU - Hussain, Abir Jaffar

AU - Francois-benois, Vialatte

AU - Fergus, Paul

PY - 2014

Y1 - 2014

N2 - Different studies have stated that electroencephalogram signals in Alzheimer’s disease patients usually have less synchronization as compare to healthy subjects. Changes in electroencephalogram signals start at early stage but clinically, these changes are not easily detected. To detect this perturbation, three neural synchrony measurement techniques have been examined with three different sets of data. This research work have successfully reported the experiment of comparing right and left temporal of brain with the rest of the brain area (frontal, central and occipital), as temporal regions are relatively the first ones to be affected by Alzheimer’s disease. A new approach using principal component analysis before applying neural synchrony measurement techniques has been presented and compared with to other existing techniques. The simulation results indicated that applying principal component analysis before synchrony measurement techniques show significantly improvement over the lateral one. The results of the experiments were analyzed using Mann-Whitney U test.

AB - Different studies have stated that electroencephalogram signals in Alzheimer’s disease patients usually have less synchronization as compare to healthy subjects. Changes in electroencephalogram signals start at early stage but clinically, these changes are not easily detected. To detect this perturbation, three neural synchrony measurement techniques have been examined with three different sets of data. This research work have successfully reported the experiment of comparing right and left temporal of brain with the rest of the brain area (frontal, central and occipital), as temporal regions are relatively the first ones to be affected by Alzheimer’s disease. A new approach using principal component analysis before applying neural synchrony measurement techniques has been presented and compared with to other existing techniques. The simulation results indicated that applying principal component analysis before synchrony measurement techniques show significantly improvement over the lateral one. The results of the experiments were analyzed using Mann-Whitney U test.

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Al-jumeily D, Iram S, Hussain AJ, Francois-benois V, Fergus P. Early Detection Method of Alzheimer’s Disease Using EEG Signals. In Huang D-S, Han K, Gromiha M, editors, Intelligent Computing in Bioinformatics: 10th International Conference, ICIC 2014, Taiyuan, China, August 3-6, 2014. Proceedings. Springer, Cham. 2014. p. 25-33. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-09330-7_4