Recurrent neural network-based acute concussion classifier using raw resting state EEG data.

Scientific Reports
Karun ThanjavurN. Virji-Babul

Abstract

Concussion is a global health concern. Despite its high prevalence, a sound understanding of the mechanisms underlying this type of diffuse brain injury remains elusive. It is, however, well established that concussions cause significant functional deficits; that children and youths are disproportionately affected and have longer recovery time than adults; and that individuals suffering from a concussion are more prone to experience additional concussions, with each successive injury increasing the risk of long term neurological and mental health complications. Currently, the most significant challenge in concussion management is the lack of objective, clinically- accepted, brain-based approaches for determining whether an athlete has suffered a concussion. Here, we report on our efforts to address this challenge. Specifically, we introduce a deep learning long short-term memory (LSTM)-based recurrent neural network that is able to distinguish between non-concussed and acute post-concussed adolescent athletes using only short (i.e. 90 s long) samples of resting state EEG data as input. The athletes were neither required to perform a specific task nor expected to respond to a stimulus during data collection. The acquired EEG dat...Continue Reading

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Software Mentioned

EEGLab
SCAT3
Deep Learning Toolbox
ConcNet
Adam
ChildSCAT3
Matlab
ussion classification Net work
Conc

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Brain Injury & Trauma

brain injury after impact to the head is due to both immediate mechanical effects and delayed responses of neural tissues.

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