Adoption of Transformer Neural Network to Improve the Diagnostic Performance of Oximetry for Obstructive Sleep Apnea.

Almarshad, Malak Abdullah; Al-Ahmadi, Saad; Islam, Md Saiful; BaHammam, Ahmed S; Soudani, Adel

Almarshad, Malak Abdullah; Al-Ahmadi, Saad; Islam, Md Saiful; BaHammam, Ahmed S; Soudani, Adel.

Afiliación

Almarshad MA; Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
Al-Ahmadi S; Computer Science Department, College of Computer and Information Sciences, Al-Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia.
Islam MS; Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
BaHammam AS; Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
Soudani A; The University Sleep Disorders Center, Department of Medicine, College of Medicine, King Saud University, Riyadh 11324, Saudi Arabia.

Sensors (Basel) ; 23(18)2023 Sep 15.

Article en En | MEDLINE | ID: mdl-37765980

Asunto(s)

Trabajo de Parto; Apnea Obstructiva del Sueño; Embarazo; Femenino; Humanos; Oximetría; Suministros de Energía Eléctrica; Redes Neurales de la Computación; Apnea Obstructiva del Sueño/diagnóstico

Palabras clave

artificial intelligence (AI); autoscoring; deep learning (DL); obstructive sleep apnea (OSA); oxygen saturation (SpO2); polysomnography (PSG); transformer neural networks

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Trabajo de Parto / Apnea Obstructiva del Sueño Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Female / Humans / Pregnancy Idioma: En Revista: Sensors (Basel) Año: 2023 Tipo del documento: Article País de afiliación: Arabia Saudita

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