Identification of Major Psychiatric Disorders From Resting-State Electroencephalography Using a Machine Learning Approach.

Park, Su Mi; Jeong, Boram; Oh, Da Young; Choi, Chi-Hyun; Jung, Hee Yeon; Lee, Jun-Young; Lee, Donghwan; Choi, Jung-Seok

Park, Su Mi; Jeong, Boram; Oh, Da Young; Choi, Chi-Hyun; Jung, Hee Yeon; Lee, Jun-Young; Lee, Donghwan; Choi, Jung-Seok.

Afiliação

Park SM; Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul, South Korea.
Jeong B; Department of Statistics, Ewha Womans University, Seoul, South Korea.
Oh DY; Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul, South Korea.
Choi CH; Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul, South Korea.
Jung HY; Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul, South Korea.
Lee JY; Department of Psychiatry and Behavioral Science, Seoul National University College of Medicine, Seoul, South Korea.
Lee D; Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, South Korea.
Choi JS; Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul, South Korea.

Front Psychiatry ; 12: 707581, 2021.

Article em En | MEDLINE | ID: mdl-34483999

Palavras-chave

classification; electroencephalography; functional connectivity; machine learning; power spectrum density; psychiatric disorder; resting-state brain function

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Front Psychiatry Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Coréia do Sul

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