Objective Classification of mTBI Using Machine Learning on a Combination of Frontopolar Electroencephalography Measurements and Self-reported Symptoms

Objective Classification of mTBI Using Machine Learning on a Combination of Frontopolar Electroencephalography Measurements and Self-reported Symptoms

Abstract Background The reliable diagnosis of a mild traumatic brain injury (mTBI) is a pervasive problem in sports and in the military. The frequency and severity of each occurrence, while difficult to quantify, may impact long term cognitive function and quality of life. Despite the new revelation...

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Bibliographic Details
Main Authors: M. Windy McNerney, Thomas Hobday, Betsy Cole, Rick Ganong, Nina Winans, Dennis Matthews, Jim Hood, Stephen Lane
Format: Article
Language:English
Published: SpringerOpen 2019-04-01
Series:Sports Medicine - Open
Subjects:
EEG
mTBI
Machine learning
Online Access:http://link.springer.com/article/10.1186/s40798-019-0187-y

Internet

http://link.springer.com/article/10.1186/s40798-019-0187-y

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