A machine-learning approach for predicting impaired consciousness in absence epilepsy.

Springer, Max; Khalaf, Aya; Vincent, Peter; Ryu, Jun Hwan; Abukhadra, Yasmina; Beniczky, Sandor; Glauser, Tracy; Krestel, Heinz; Blumenfeld, Hal

Springer, Max; Khalaf, Aya; Vincent, Peter; Ryu, Jun Hwan; Abukhadra, Yasmina; Beniczky, Sandor; Glauser, Tracy; Krestel, Heinz; Blumenfeld, Hal.

Afiliação

Springer M; Department of Neurology, Yale University School of Medicine, New Haven, Connecticut, USA.
Khalaf A; Department of Neurology, Yale University School of Medicine, New Haven, Connecticut, USA.
Vincent P; Biomedical Engineering and Systems, Faculty of Engineering, Cairo University, Giza, Egypt.
Ryu JH; Department of Neurology, Yale University School of Medicine, New Haven, Connecticut, USA.
Abukhadra Y; Department of Neurology, Yale University School of Medicine, New Haven, Connecticut, USA.
Beniczky S; Department of Neurology, Yale University School of Medicine, New Haven, Connecticut, USA.
Glauser T; Department of Clinical Neuorophysiology, Danish Epilepsy Center, Dianalund, Denmark.
Krestel H; Aarhus University Hospital, Aarhus, Denmark.
Blumenfeld H; Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.

Ann Clin Transl Neurol ; 9(10): 1538-1550, 2022 10.

Article em En | MEDLINE | ID: mdl-36114696

Assuntos

Epilepsia Tipo Ausência; Estado de Consciência; Eletroencefalografia/métodos; Epilepsia Tipo Ausência/diagnóstico; Humanos; Aprendizado de Máquina; Convulsões/diagnóstico

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Epilepsia Tipo Ausência Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Ann Clin Transl Neurol Ano de publicação: 2022 Tipo de documento: Article

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