Deep Learning Recurrent Neural Network for Concussion Classification in Adolescents Using Raw Electroencephalography Signals: Toward a Minimal Number of Sensors

Deep Learning Recurrent Neural Network for Concussion Classification in Adolescents Using Raw Electroencephalography Signals: Toward a Minimal Number of Sensors

Artificial neural networks (ANNs) are showing increasing promise as decision support tools in medicine and particularly in neuroscience and neuroimaging. Recently, there has been increasing work on using neural networks to classify individuals with concussion using electroencephalography (EEG) data....

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Bibliographic Details
Main Authors: Babul, A. (Author), Hristopulos, D.T (Author), Thanjavur, K. (Author), Virji-Babul, N. (Author), Yi, K.M (Author)
Format: Article
Language:English
Published: Frontiers Media S.A. 2021
Subjects:
accuracy
adolescent
adolescents
Article
athlete
classifier
clinical article
concussion
concussion classification
controlled study
deep learning
disease classification
electroencephalography
human
long short term memory network
LSTM
machine learning
male
mild traumatic brain injury
recurrent neural network
resting state EEG
resting state network
scoring system
signal detection
sport injury
traumatic brain injury
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