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Study investigated whether resting and stimulus-evoked electroencephalography (EEG) methods detect the presence of language function in patients with severe traumatic brain injury (TBI). Four EEG measures were assessed: (1) resting background (applying Forgacsâ?? criteria), (2) reactivity to speech, (3) background and reactivity (applying Synekâ??s criteria), and (4) an automated support vector machine (classifier for speech versus rest). Cohenâ??s kappa measured agreement between the four EEG measures and evidence of language function on the behavioral Coma Recovery Scale-Revised (CRS-R) and composite (CRS-R or functional magnetic resonance imaging) reference standard. Sensitivity and specificity of each EEG measure were calculated against the reference standards. Seventeen adult patients with severe TBI and 16 healthy subjects were enrolled. The classifier, followed by Forgacsâ?? criteria for resting background, demonstrated the highest agreement with the behavioral reference standard. Only Synekâ??s criteria for background and reactivity showed significant agreement with the composite reference standard. The classifier and resting background showed balanced sensitivity and specificity for behavioral (sensitivityâ??=â??84.6 and 80.8 percent; specificityâ??=â??57.1 percent for both) and composite reference standards (sensitivityâ??=â??79.3 and 75.9 percent, specificityâ??=â??50 percent for both). The findings suggest that methods applying an automated classifier, resting background, or resting background with reactivity may identify severe TBI patients with preserved language function. Automated classifier methods may enable unbiased and efficient assessment of larger populations or serial timepoints, while qualitative visual methods may be practical in community settings.


Chatelle, Camille |Rosenthal, Eric S. |Bodien, Yelena G. |Spencerâ??Salmon, Camille A.|Giacino, Joseph T. |Edlow, Brian L.|

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