Machine learning approaches for predicting sleep arousal response based on heart rate variability, oxygen saturation, and body profiles

OBJECTIVE: Obstructive sleep apnea is a global health concern, and several tools have been developed to screen its severity. However, most tools focus on respiratory events instead of sleep arousal, which can also affect sleep efficiency. This study employed easy-to-measure parameters—namely heart r...

Descripción completa

Detalles Bibliográficos
Autores principales: Kuo, Chih-Fan, Tsai, Cheng-Yu, Cheng, Wun-Hao, Hs, Wen-Hua, Majumdar, Arnab, Stettler, Marc, Lee, Kang-Yun, Kuan, Yi-Chun, Feng, Po-Hao, Tseng, Chien-Hua, Chen, Kuan-Yuan, Kang, Jiunn-Horng, Lee, Hsin-Chien, Wu, Cheng-Jung, Liu, Wen-Te
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2023
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10576931/
https://www.ncbi.nlm.nih.gov/pubmed/37846406
http://dx.doi.org/10.1177/20552076231205744

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10576931/
https://www.ncbi.nlm.nih.gov/pubmed/37846406
http://dx.doi.org/10.1177/20552076231205744

Ejemplares similares