Scalp EEG-based discrimination of cognitive deficits after traumatic brain injury using event-related tsallis entropy analysis

J. McBride, X. Zhao, T. Nichols, V. Vagnini, N. Munro, D. Berry, Y. Jiang

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

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.

Original languageEnglish
Article number6328249
Pages (from-to)90-96
Number of pages7
JournalIEEE Transactions on Biomedical Engineering
Volume60
Issue number1
DOIs
StatePublished - 2013

Keywords

  • Biomedical signal processing
  • EEG
  • medical diagnosis
  • traumatic brain injury (TBI)

ASJC Scopus subject areas

  • Biomedical Engineering

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