Journal
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
Volume 60, Issue 1, Pages 90-96Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2012.2223698
Keywords
Biomedical signal processing; EEG; medical diagnosis; traumatic brain injury (TBI)
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Funding
- National Science Foundation [CMMI-0845753]
- Department of Behavioral Science, University of Kentucky College of Medicine
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [0845753] Funding Source: National Science Foundation
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Traumatic brain injury (TBI) is the leading cause of death and disability in children and adolescents in the U. S. This is a pilot study, which explores the discrimination of chronic TBI from normal controls using scalp EEG during a memory task. Tsallis entropies are computed for responses during an old-new memory recognition task. A support vector machinemodel is constructed to discriminate between normal andmoderate/severe TBI individuals using Tsallis entropies as features. Numerical analyses of 30 records (15 normal and 15 TBI) show amaximum discrimination accuracy of 93% (p-value = 7.8557E-5) using four features. These results suggest the potential of scalp EEG as an efficacious method for noninvasive diagnosis of TBI.
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