Classification of traumatic brain injury using support vector machine analysis of event-related Tsallis entropy

J. McBride; X. Zhao; T. Nichols; T. Abdul-Ahad; M. Wilson; V. Vagnini; N. Munro; D. Berry; Y. Jiang

doi:10.1109/BSEC.2011.5872318

Classification of traumatic brain injury using support vector machine analysis of event-related Tsallis entropy

J. McBride, X. Zhao, T. Nichols, T. Abdul-Ahad, M. Wilson, V. Vagnini, N. Munro, D. Berry, Y. Jiang

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

7 Scopus citations

Abstract

An estimated 1.4 million Americans suffer from traumatic brain injury (TBI) each year [1]. Current methods of detecting TBI, such as computerized tomography (CT), magnetic resonance imaging (MRI), and Positron Emission Tomography (PET) scanning are time-consuming and expensive [2]. Here, the viability of a potentially more cost-effective means of detecting TBI is presented. Support vector machine (SVM) analyses are employed to classify 15 TBI and 15 normal individuals' EEG recordings taken during a working memory test. The features used by the SVM analyses include different sets of event-related Tsallis entropy functionals. The analyses demonstrate a strong correlation between the event-related functionals (ERFs) and the presence of TBI, attaining classification accuracies as high as 90%.

Original language	English
Title of host publication	Proceedings of the 2011 Biomedical Sciences and Engineering Conference
Subtitle of host publication	Image Informatics and Analytics in Biomedicine, BSEC 2011
DOIs	https://doi.org/10.1109/BSEC.2011.5872318
State	Published - 2011
Event	2011 Biomedical Sciences and Engineering Conference: Image Informatics and Analytics in Biomedicine, BSEC 2011 - Knoxville, TN, United States Duration: Mar 15 2011 → Mar 17 2011

Publication series

Name	Proceedings of the 2011 Biomedical Sciences and Engineering Conference: Image Informatics and Analytics in Biomedicine, BSEC 2011

Conference

Conference	2011 Biomedical Sciences and Engineering Conference: Image Informatics and Analytics in Biomedicine, BSEC 2011
Country/Territory	United States
City	Knoxville, TN
Period	3/15/11 → 3/17/11

ASJC Scopus subject areas

Computer Graphics and Computer-Aided Design
Biomedical Engineering

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10.1109/BSEC.2011.5872318

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McBride, J., Zhao, X., Nichols, T., Abdul-Ahad, T., Wilson, M., Vagnini, V., Munro, N., Berry, D., & Jiang, Y. (2011). Classification of traumatic brain injury using support vector machine analysis of event-related Tsallis entropy. In Proceedings of the 2011 Biomedical Sciences and Engineering Conference: Image Informatics and Analytics in Biomedicine, BSEC 2011 Article 5872318 (Proceedings of the 2011 Biomedical Sciences and Engineering Conference: Image Informatics and Analytics in Biomedicine, BSEC 2011). https://doi.org/10.1109/BSEC.2011.5872318

McBride, J. ; Zhao, X. ; Nichols, T. et al. / Classification of traumatic brain injury using support vector machine analysis of event-related Tsallis entropy. Proceedings of the 2011 Biomedical Sciences and Engineering Conference: Image Informatics and Analytics in Biomedicine, BSEC 2011. 2011. (Proceedings of the 2011 Biomedical Sciences and Engineering Conference: Image Informatics and Analytics in Biomedicine, BSEC 2011).

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title = "Classification of traumatic brain injury using support vector machine analysis of event-related Tsallis entropy",

abstract = "An estimated 1.4 million Americans suffer from traumatic brain injury (TBI) each year [1]. Current methods of detecting TBI, such as computerized tomography (CT), magnetic resonance imaging (MRI), and Positron Emission Tomography (PET) scanning are time-consuming and expensive [2]. Here, the viability of a potentially more cost-effective means of detecting TBI is presented. Support vector machine (SVM) analyses are employed to classify 15 TBI and 15 normal individuals' EEG recordings taken during a working memory test. The features used by the SVM analyses include different sets of event-related Tsallis entropy functionals. The analyses demonstrate a strong correlation between the event-related functionals (ERFs) and the presence of TBI, attaining classification accuracies as high as 90%.",

author = "J. McBride and X. Zhao and T. Nichols and T. Abdul-Ahad and M. Wilson and V. Vagnini and N. Munro and D. Berry and Y. Jiang",

year = "2011",

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language = "English",

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series = "Proceedings of the 2011 Biomedical Sciences and Engineering Conference: Image Informatics and Analytics in Biomedicine, BSEC 2011",

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McBride, J, Zhao, X, Nichols, T, Abdul-Ahad, T, Wilson, M, Vagnini, V, Munro, N, Berry, D & Jiang, Y 2011, Classification of traumatic brain injury using support vector machine analysis of event-related Tsallis entropy. in Proceedings of the 2011 Biomedical Sciences and Engineering Conference: Image Informatics and Analytics in Biomedicine, BSEC 2011., 5872318, Proceedings of the 2011 Biomedical Sciences and Engineering Conference: Image Informatics and Analytics in Biomedicine, BSEC 2011, 2011 Biomedical Sciences and Engineering Conference: Image Informatics and Analytics in Biomedicine, BSEC 2011, Knoxville, TN, United States, 3/15/11. https://doi.org/10.1109/BSEC.2011.5872318

Classification of traumatic brain injury using support vector machine analysis of event-related Tsallis entropy. / McBride, J.; Zhao, X.; Nichols, T. et al.
Proceedings of the 2011 Biomedical Sciences and Engineering Conference: Image Informatics and Analytics in Biomedicine, BSEC 2011. 2011. 5872318 (Proceedings of the 2011 Biomedical Sciences and Engineering Conference: Image Informatics and Analytics in Biomedicine, BSEC 2011).

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

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AU - Vagnini, V.

AU - Munro, N.

AU - Berry, D.

AU - Jiang, Y.

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McBride J, Zhao X, Nichols T, Abdul-Ahad T, Wilson M, Vagnini V et al. Classification of traumatic brain injury using support vector machine analysis of event-related Tsallis entropy. In Proceedings of the 2011 Biomedical Sciences and Engineering Conference: Image Informatics and Analytics in Biomedicine, BSEC 2011. 2011. 5872318. (Proceedings of the 2011 Biomedical Sciences and Engineering Conference: Image Informatics and Analytics in Biomedicine, BSEC 2011). doi: 10.1109/BSEC.2011.5872318

Classification of traumatic brain injury using support vector machine analysis of event-related Tsallis entropy

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