Citation

Prichep L, Jacquin A, Filipenko J, Ghosh Dastidar S, Zabele S, Vodencarevic A, Rothman N. IEEE Trans. Neural Syst. Rehabil. Eng. 2012; 20(6): 806-822.

Copyright

(Copyright © 2012, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TNSRE.2012.2206609

PMID

22855231

Abstract

Assessment of medical disorders is often aided by objective diagnostic tests which can lead to early intervention and appropriate treatment. In the case of brain dysfunction caused by head injury, there is an urgent need for quantitative evaluation methods to aid in acute triage of those subjects who have sustained Traumatic Brain Injury (TBI). Current clinical tools to detect mild TBI (mTBI/concussion) are limited to subjective reports of symptoms and short neurocognitive batteries, offering little objective evidence for clinical decisions; or CT scans, with radiation-risk, that are most often negative in mTBI. This paper describes a novel methodology for the development of algorithms to provide multi-class classification in a substantial population of brain injured subjects, across a broad age range and representative subpopulations. The method is based on age-regressed quantitative features (linear and nonlinear) extracted from brain electrical activity recorded from a limited montage of scalp electrodes. These features are used as input to a unique informed data reduction method, maximizing confidence of prospective validation and minimizing over-fitting. A training set for supervised learning was used, including: normal control, concussed, and structural injury/CT positive (CT+). The classifier function separating CT+ from the other groups demonstrated a sensitivity of 96% and specificity of 78%; the classifier separating normal controls from the other groups demonstrated a sensitivity of 81% and specificity of 74%, suggesting high utility of such classifiers in acute clinical settings. The use of a sequence of classifiers where the desired risk can be stratified further supports clinical utility.

Language: en