Cargando…
Prediction of Sleep Apnea Events Using a CNN–Transformer Network and Contactless Breathing Vibration Signals
It is estimated that globally 425 million subjects have moderate to severe obstructive sleep apnea (OSA). The accurate prediction of sleep apnea events can offer insight into the development of treatment therapies. However, research related to this prediction is currently limited. We developed a cov...
Autores principales: | Chen, Yuhang, Yang, Shuchen, Li, Huan, Wang, Lirong, Wang, Bidou |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10376604/ https://www.ncbi.nlm.nih.gov/pubmed/37508773 http://dx.doi.org/10.3390/bioengineering10070746 |
Ejemplares similares
-
Assessment of Central Sleep Apnea Events in Children with Sleep-Disordered Breathing
por: Kaplan, Alyson, et al.
Publicado: (2022) -
An Ultrasonic Contactless Sensor for Breathing Monitoring
por: Arlotto, Philippe, et al.
Publicado: (2014) -
Automatic Detection of Obstructive Sleep Apnea Events Using a Deep CNN-LSTM Model
por: Zhang, Junming, et al.
Publicado: (2021) -
Contactless Interface Using Exhaled Breath and Thermal Imaging
por: Lee, Kanghoon, et al.
Publicado: (2023) -
A New Approach for Detecting Sleep Apnea Using a Contactless Bed Sensor: Comparison Study
por: Sadek, Ibrahim, et al.
Publicado: (2020)