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

Author

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

Author_Institution

Dept. of Mech., Univ. of Tennessee (UT), Knoxville, TN, USA

fYear

2011

fDate

15-17 March 2011

Firstpage

1

Lastpage

4

Abstract

An estimated 1.4 million Americans suffer from traumatic brain injury (TBI) each year. 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. 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%.

Keywords

bioelectric potentials; brain; electroencephalography; entropy; feature extraction; injuries; medical signal processing; pattern classification; signal classification; support vector machines; EEG recordings; SVM analysis; event-related Tsallis entropy; event-related functionals; feature extraction; memory test; signal classification; support vector machine analysis; traumatic brain injury; Accuracy; Brain injuries; Electroencephalography; Entropy; Magnetic resonance imaging; Support vector machines; Temporal lobe;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Sciences and Engineering Conference (BSEC), 2011

Conference_Location

Knoxville, TN

Print_ISBN

978-1-61284-411-4

Electronic_ISBN

978-1-61284-410-7

Type

conf

DOI

10.1109/BSEC.2011.5872318

Filename

5872318