Classification of Depth of Coma Using Complexity Measures and Nonlinear Features of Electroencephalogram Signals.

Altintop, Çigdem Gülüzar; Latifoglu, Fatma; Akin, Aynur Karayol; Bayram, Adnan; Çiftçi, Murat

Altintop, Çigdem Gülüzar; Latifoglu, Fatma; Akin, Aynur Karayol; Bayram, Adnan; Çiftçi, Murat.

Afiliação

Altintop ÇG; Department of Biomedical Engineering, Erciyes University, Turkey.
Latifoglu F; Department of Biomedical Engineering, Erciyes University, Turkey.
Akin AK; Department of Anesthesiology and Reanimation, Erciyes University, Turkey.
Bayram A; Department of Anesthesiology and Reanimation, Erciyes University, Turkey.
Çiftçi M; Department of Neurosurgery, Erciyes University, Turkey.

Int J Neural Syst ; 32(5): 2250018, 2022 May.

Article em En | MEDLINE | ID: mdl-35300584

Assuntos

Coma; Eletroencefalografia; Algoritmos; Coma/diagnóstico; Estado de Consciência/fisiologia; Transtornos da Consciência; Eletroencefalografia/métodos; Entropia; Humanos

Palavras-chave

Complexity measures; EEG; Electroencephalogram; Glasgow coma scale; LempelZiv complexity; classification; entropy; level of consciousness

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Coma / Eletroencefalografia Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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