Classification algorithms for the identification of structural injury in TBI using brain electrical activity

Leslie S. Prichep, Samanwoy Ghosh Dastidar, Arnaud Jacquin, William Koppes, Jonathan Miller, Thomas Radman, Brian O'Neil, Rosanne Naunheim, J. Stephen Huff

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


Background: There is an urgent need for objective criteria adjunctive to standard clinical assessment of acute Traumatic Brain Injury (TBI). Details of the development of a quantitative index to identify structural brain injury based on brain electrical activity will be described. Methods: Acute closed head injured and normal patients (n=1470) were recruited from 16 US Emergency Departments and evaluated using brain electrical activity (EEG) recorded from forehead electrodes. Patients had high GCS (median=15), and most presented with low suspicion of brain injury. Patients were divided into a CT positive (CT+) group and a group with CT negative findings or where CT scans were not ordered according to standard assessment (CT-/CT_NR). Three different classifier methodologies, Ensemble Harmony, Least Absolute Shrinkage and Selection Operator (LASSO), and Genetic Algorithm (GA), were utilized. Results: Similar performance accuracy was obtained for all three methodologies with an average sensitivity/specificity of 97.5%/59.5%, area under the curves (AUC) of 0.90 and average Negative Predictive Validity (NPV)>99%. Sensitivity was highest for CT+ cases with potentially life threatening hematomas, where two of three classifiers were 100%. Conclusion: Similar performance of these classifiers suggests that the optimal separation of the populations was obtained given the overlap of the underlying distributions of features of brain activity. High sensitivity to CT+ injuries (highest in hematomas) and specificity significantly higher than that obtained using ED guidelines for imaging, supports the enhanced clinical utility of this technology and suggests the potential role in the objective, rapid and more optimal triage of TBI patients.

Original languageEnglish
Pages (from-to)125-133
Number of pages9
JournalComputers in Biology and Medicine
StatePublished - Oct 1 2014


  • Acute traumatic brain injury
  • Classifier algorithms
  • Electrophysiology of TBI
  • Genetic algorithms
  • Quantitative brain activity
  • Structural brain injury
  • TBI
  • TBI triage


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