Scalp EEG-based discrimination of cognitive deficits after traumatic brain injury using event-related Tsallis entropy analysis.
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 machine model is constructed to discriminate between normal and moderate/severe TBI individuals using Tsallis entropies as features. Numerical analyses of 30 records (15 normal and 15 TBI) show a maximum 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.
Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, TN 37996, USA. jmcbrid4@utk.edu
J
J
McBride
X
X
Zhao
T
T
Nichols
V
V
Vagnini
N
N
Munro
D
D
Berry
Y
Y
Jiang
D000328:Adult; D001930:Brain Injuries / Q000503:physiopathology*; D016022:Case-Control Studies; D003072:Cognition Disorders / Q000175:diagnosis* / Q000503:physiopathology; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D010865:Pilot Projects; D012535:Scalp / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted