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

Thanjavur, Karun; Hristopulos, Dionissios T; Babul, Arif; Yi, Kwang Moo; Virji-Babul, Naznin

Thanjavur, Karun; Hristopulos, Dionissios T; Babul, Arif; Yi, Kwang Moo; Virji-Babul, Naznin.

Afiliação

Thanjavur K; Department of Physics and Astronomy, University of Victoria, Victoria, BC, Canada.
Hristopulos DT; School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece.
Babul A; Department of Physics and Astronomy, University of Victoria, Victoria, BC, Canada.
Yi KM; Department of Computer Science, University of British Columbia, Vancouver, BC, Canada.
Virji-Babul N; Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada.

Front Hum Neurosci ; 15: 734501, 2021.

Article em En | MEDLINE | ID: mdl-34899212

Palavras-chave

LSTM; adolescents; concussion; concussion classification; deep learning; machine learning; mild traumatic brain injury; resting state EEG

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article