Oleg V. Komogortsev
Corey D. Holland
ABSTRACT
This paper presents a pilot study for the automated detection of mild traumatic brain injury (mTBI) via the application of eye movement biometrics. Biometric feature vectors from multiple paradigms are evaluated for their ability to differentiate subjects diagnosed with mTBI from healthy subjects within a small subject pool. Supervised and unsupervised machine learning techniques were applied to the problem, with preliminary results indicating a potential 100% classification accuracy from a supervised learning technique and 89% classification accuracy from an unsupervised technique.
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- Ciuffreda, K. J., Ludlam, D. and Thiagarajan, P. Oculomotor Diagnostic Protocol for the mTBI Population. Optometry 82, 2 (2011), 61--63.Google Scholar
- Drew, A. S., Langan, J., Halterman, C., Osternig, L. R., Chou, L.-S. and Donkelaar, P. v. Attentional Disengagement Dysfunction Following mTBI Assessed with the Gap Saccade Task. Neuroscience Letters 417, 1 (2007), 61--65.Google Scholar
- Duchowski, A. T. Eye Tracking Methodology: Theory and Practice. Springer-Verlag (2007), 1--360. Google ScholarDigital Library
- Faul, M., Xu, L., Wald, M. M. and Coronado, V. G. Traumatic Brain Injury in the USA: Emergency Department Visits, Hospitalizations, and Deaths, 2002--2006. Centers for Disease Control and Prevention, National Center for Injury Prevention and Control (2010).Google Scholar
- Guskiewicz, K. M., McCrea, M., Marshall, S. W., Cantu, R. C., Randolph, C., Barr, W., Onate, J. A. and Kelly, J. P. Cumulative Effects Associated With Recurrent Concussion in Collegiate Football Players: The NCAA Concussion Study. Journal of the Americal Medical Association 290, (2003), 2549--2555.Google Scholar
- Heitger, M. H., Anderson, T. J., Jones, R. D., Dalrymple-Alford, J. C., Frampton, C. M. and Ardagh, M. W. Eye Movement and Visuomotor Arm Movement Deficits Following Mild Closed Head Injury. Brain 127, (2004), 575--590.Google Scholar
- Heitger, M. H., Jones, R. D., Dalrymple-Alford, J. C., Frampton, C. M., Ardagh, M. W. and Anderson, T. J. Motor Deficits and Recovery During the First Year Following Mild Closed Head Injury. Brain Injury 20, 8 (2006), 807--824.Google Scholar
- Hellerstein, L. F., Freed, S. and Maples, W. C. Vision Profile of Patients with Mild Brain Injury. (1995), 634--639.Google Scholar
- Holland, C. D. and Komogortsev, O. V. Biometric Identification via Eye Movement Scanpaths in Reading. In International Joint Conference on Biometrics (IJCB), IEEE (2011), 1--8. Google ScholarDigital Library
- Holland, C. D. and Komogortsev, O. V. Complex Eye Movement Pattern Biometrics: Analyzing Fixations and Saccades. IAPR International Conference on Biometrics, (2013), 1--8.Google Scholar
- Kay, T., Harrington, D. E., Adams, R., Anderson, T. and Berrol, S. Definition of Mild Traumatic Brain Injury. Journal of Head Trauma Rehabilitation 8, 3 (1993), 86--87.Google Scholar
- Eye Movement Biometric Database v1. http://cs.txstate.edu/~ok11/embd_v1.html.Google Scholar
- Komogortsev, O. V., Gobert, D. V., Jayarathna, U. K. S., Koh, D. H. and Gowda, S. M. Standardization of Automated Analyses of Oculomotor Fixation and Saccadic Behaviors. Transactions on Biomedical Engineering 57, (2010), 2635--2645.Google Scholar
- Komogortsev, O. V. and Holland, C. D. Biometric Authentication via Complex Oculomotor Behavior. Conference on Biometrics: Theory, Applications and Systems, IEEE (2013), 1--8.Google ScholarCross Ref
- Komogortsev, O. V., Karpov, A., Price, L. R. and Aragon, C. R. Biometric Authentication via Oculomotor Plant Characteristics. In International Conference on Biometrics, IEEE/IAPR (2012), 1--8.Google ScholarCross Ref
- Koponen, S., Taiminen, T., Kurki, T., Portin, R., Isoniemi, H., Himanen, L., Hinkka, S., Salokangas, R. K. R. and Tenovuo, O. MRI Findings and Axis I and II Psychiatric Disorders After Traumatic Brain Injury. Neuroimaging 146, (2006), 263--270.Google Scholar
- Leigh, R. J. and Zee, D. S. The Neurology of Eye Movements. Oxford University Press (2006), 1--776.Google Scholar
Index Terms
- The application of eye movement biometrics in the automated detection of mild traumatic brain injury
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