Citation

Jacquin AE, Bazarian JJ, Casa DJ, Elbin RJ, Hotz G, Schnyer DM, Yeargin S, Prichep LS, Covassin T. J. Concussion 2021; 5: e20597002211004333.

Copyright

(Copyright © 2021, SAGE Publishing)

DOI

10.1177/20597002211004333

PMID

unavailable

Abstract

OBJECTIVEPrompt, accurate, objective assessment of concussion is crucial as delays can lead to increased short and long-term consequences. The purpose of this study was to derive an objective multimodal concussion index (CI) using EEG at its core, to identify concussion, and to assess change over time throughout recovery.

METHODSMale and female concussed (N?=?232) and control (N?=?206) subjects 13?25 years were enrolled at 12 US colleges and high schools. Evaluations occurred within 72?h of injury, 5?days post-injury, at return-to-play (RTP), 45?days after RTP (RTP?+?45); and included EEG, neurocognitive performance, and standard concussion assessments. Concussed subjects had a witnessed head impact, were removed from play for ≥ 5?days using site guidelines, and were divided into those with RTP?

Language: en

Keywords

classifier algorithm; CONCUSSION INDEX; EEG; machine learning; mild Traumatic Brain Injury; return-to-play; sport-related concussion