Analyzing EEG signal data for detection of epileptic seizure: Introducing weight on visibility graph with complex network feature

Supriya; Siuly; Hua Wang; Guangping Zhuo; Yanchun Zhang

doi:10.1007/978-3-319-46922-5_5

Analyzing EEG signal data for detection of epileptic seizure: Introducing weight on visibility graph with complex network feature

Supriya, Siuly, Hua Wang, Guangping Zhuo, Yanchun Zhang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Citations (Scopus)

Abstract

In the medical community, automatic epileptic seizure detection through electroencephalogram (EEG) signals is still a very challenging issue for medical professionals and also for the researchers. When measuring an EEG, huge amount of data are obtained with different categories. Therefore, EEG recording can be characterized as big data due to its high volume. Traditional methods are facing challenges to handle such Big Data as it exhibits non-stationarity, chaotic, voluminous, and volatile in nature. Motivated by this, we introduce a new idea for epilepsy detection using complex network statistical property by measuring different strengths of the edges in the natural visibility graph theory. We conducted 10-fold cross validation for evaluating the performance of our proposed methodology with support vector machine (SVM) and Discriminant Analysis (DA) families of classifiers. This study aims to investigate the effect of segmentation and non-segmentation of EEG signals in the detection of epilepsy disorder.

Original language	English
Title of host publication	Databases Theory and Applications - 27th Australasian Database Conference, ADC 2016, Proceedings
Editors	Muhammad Aamir Cheema, Wenjie Zhang, Lijun Chang
Publisher	Springer Verlag
Pages	56-66
Number of pages	11
ISBN (Print)	9783319469218
DOIs	https://doi.org/10.1007/978-3-319-46922-5_5
Publication status	Published - 2016
Externally published	Yes
Event	27th Australasian Database Conference on Databases Theory and Applications, ADC 2016 - Sydney, United States Duration: 28 Sept 2016 → 29 Sept 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9877 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	27th Australasian Database Conference on Databases Theory and Applications, ADC 2016
Country/Territory	United States
City	Sydney
Period	28/09/16 → 29/09/16

Keywords

Average weighted degree
Complex network
EEG
Epilepsy
SVM and LDA
Visibility graph

Access to Document

10.1007/978-3-319-46922-5_5

Cite this

Supriya, Siuly, Wang, H., Zhuo, G., & Zhang, Y. (2016). Analyzing EEG signal data for detection of epileptic seizure: Introducing weight on visibility graph with complex network feature. In M. A. Cheema, W. Zhang, & L. Chang (Eds.), Databases Theory and Applications - 27th Australasian Database Conference, ADC 2016, Proceedings (pp. 56-66). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9877 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46922-5_5

Supriya ; Siuly ; Wang, Hua et al. / Analyzing EEG signal data for detection of epileptic seizure : Introducing weight on visibility graph with complex network feature. Databases Theory and Applications - 27th Australasian Database Conference, ADC 2016, Proceedings. editor / Muhammad Aamir Cheema ; Wenjie Zhang ; Lijun Chang. Springer Verlag, 2016. pp. 56-66 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Analyzing EEG signal data for detection of epileptic seizure: Introducing weight on visibility graph with complex network feature",

abstract = "In the medical community, automatic epileptic seizure detection through electroencephalogram (EEG) signals is still a very challenging issue for medical professionals and also for the researchers. When measuring an EEG, huge amount of data are obtained with different categories. Therefore, EEG recording can be characterized as big data due to its high volume. Traditional methods are facing challenges to handle such Big Data as it exhibits non-stationarity, chaotic, voluminous, and volatile in nature. Motivated by this, we introduce a new idea for epilepsy detection using complex network statistical property by measuring different strengths of the edges in the natural visibility graph theory. We conducted 10-fold cross validation for evaluating the performance of our proposed methodology with support vector machine (SVM) and Discriminant Analysis (DA) families of classifiers. This study aims to investigate the effect of segmentation and non-segmentation of EEG signals in the detection of epilepsy disorder.",

keywords = "Average weighted degree, Complex network, EEG, Epilepsy, SVM and LDA, Visibility graph",

author = "Supriya and Siuly and Hua Wang and Guangping Zhuo and Yanchun Zhang",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 27th Australasian Database Conference on Databases Theory and Applications, ADC 2016 ; Conference date: 28-09-2016 Through 29-09-2016",

year = "2016",

doi = "10.1007/978-3-319-46922-5_5",

language = "English",

isbn = "9783319469218",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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}

Supriya, Siuly, Wang, H, Zhuo, G & Zhang, Y 2016, Analyzing EEG signal data for detection of epileptic seizure: Introducing weight on visibility graph with complex network feature. in MA Cheema, W Zhang & L Chang (eds), Databases Theory and Applications - 27th Australasian Database Conference, ADC 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9877 LNCS, Springer Verlag, pp. 56-66, 27th Australasian Database Conference on Databases Theory and Applications, ADC 2016, Sydney, United States, 28/09/16. https://doi.org/10.1007/978-3-319-46922-5_5

Analyzing EEG signal data for detection of epileptic seizure: Introducing weight on visibility graph with complex network feature. / Supriya; Siuly; Wang, Hua et al.
Databases Theory and Applications - 27th Australasian Database Conference, ADC 2016, Proceedings. ed. / Muhammad Aamir Cheema; Wenjie Zhang; Lijun Chang. Springer Verlag, 2016. p. 56-66 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9877 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Analyzing EEG signal data for detection of epileptic seizure

T2 - 27th Australasian Database Conference on Databases Theory and Applications, ADC 2016

AU - Supriya,

AU - Siuly,

AU - Wang, Hua

AU - Zhuo, Guangping

AU - Zhang, Yanchun

N1 - Publisher Copyright: © Springer International Publishing AG 2016.

PY - 2016

Y1 - 2016

N2 - In the medical community, automatic epileptic seizure detection through electroencephalogram (EEG) signals is still a very challenging issue for medical professionals and also for the researchers. When measuring an EEG, huge amount of data are obtained with different categories. Therefore, EEG recording can be characterized as big data due to its high volume. Traditional methods are facing challenges to handle such Big Data as it exhibits non-stationarity, chaotic, voluminous, and volatile in nature. Motivated by this, we introduce a new idea for epilepsy detection using complex network statistical property by measuring different strengths of the edges in the natural visibility graph theory. We conducted 10-fold cross validation for evaluating the performance of our proposed methodology with support vector machine (SVM) and Discriminant Analysis (DA) families of classifiers. This study aims to investigate the effect of segmentation and non-segmentation of EEG signals in the detection of epilepsy disorder.

AB - In the medical community, automatic epileptic seizure detection through electroencephalogram (EEG) signals is still a very challenging issue for medical professionals and also for the researchers. When measuring an EEG, huge amount of data are obtained with different categories. Therefore, EEG recording can be characterized as big data due to its high volume. Traditional methods are facing challenges to handle such Big Data as it exhibits non-stationarity, chaotic, voluminous, and volatile in nature. Motivated by this, we introduce a new idea for epilepsy detection using complex network statistical property by measuring different strengths of the edges in the natural visibility graph theory. We conducted 10-fold cross validation for evaluating the performance of our proposed methodology with support vector machine (SVM) and Discriminant Analysis (DA) families of classifiers. This study aims to investigate the effect of segmentation and non-segmentation of EEG signals in the detection of epilepsy disorder.

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KW - Complex network

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M3 - Conference contribution

AN - SCOPUS:84990055140

SN - 9783319469218

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Databases Theory and Applications - 27th Australasian Database Conference, ADC 2016, Proceedings

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A2 - Zhang, Wenjie

A2 - Chang, Lijun

PB - Springer Verlag

Y2 - 28 September 2016 through 29 September 2016

ER -

Supriya, Siuly, Wang H, Zhuo G, Zhang Y. Analyzing EEG signal data for detection of epileptic seizure: Introducing weight on visibility graph with complex network feature. In Cheema MA, Zhang W, Chang L, editors, Databases Theory and Applications - 27th Australasian Database Conference, ADC 2016, Proceedings. Springer Verlag. 2016. p. 56-66. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-46922-5_5

Analyzing EEG signal data for detection of epileptic seizure: Introducing weight on visibility graph with complex network feature

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Keywords

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