Moving the field forward: detection of epileptiform abnormalities on scalp electroencephalography using deep learning-clinical application perspectives.

Janmohamed, Mubeen; Nhu, Duong; Kuhlmann, Levin; Gilligan, Amanda; Tan, Chang Wei; Perucca, Piero; O'Brien, Terence J; Kwan, Patrick

Janmohamed, Mubeen; Nhu, Duong; Kuhlmann, Levin; Gilligan, Amanda; Tan, Chang Wei; Perucca, Piero; O'Brien, Terence J; Kwan, Patrick.

Afiliación

Janmohamed M; Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3004, Australia.
Nhu D; Department of Neurology, Alfred Health, Melbourne, VIC 3004, Australia.
Kuhlmann L; Department of Neurology, The Royal Melbourne Hospital, Melbourne, VIC 3050, Australia.
Gilligan A; Department of Data Science and AI, Faculty of IT, Monash University, Clayton, VIC 3800, Australia.
Tan CW; Department of Data Science and AI, Faculty of IT, Monash University, Clayton, VIC 3800, Australia.
Perucca P; Neurosciences Clinical Institute, Epworth Healthcare Hospital, Melbourne, VIC 3121, Australia.
O'Brien TJ; Department of Data Science and AI, Faculty of IT, Monash University, Clayton, VIC 3800, Australia.
Kwan P; Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3004, Australia.

Brain Commun ; 4(5): fcac218, 2022.

Article en En | MEDLINE | ID: mdl-36092304

Palabras clave

EEG; automated detection; deep learning; epilepsy; epileptiform abnormalities

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Brain Commun Año: 2022 Tipo del documento: Article País de afiliación: Australia Pais de publicación: Reino Unido

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