Sleep Apnea Classification Algorithm Development Using a Machine-Learning Framework and Bag-of-Features Derived from Electrocardiogram Spectrograms

Background: Heart rate variability (HRV) and electrocardiogram (ECG)-derived respiration (EDR) have been used to detect sleep apnea (SA) for decades. The present study proposes an SA-detection algorithm using a machine-learning framework and bag-of-features (BoF) derived from an ECG spectrogram. Met...

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Detalles Bibliográficos
Autores principales: Lin, Cheng-Yu, Wang, Yi-Wen, Setiawan, Febryan, Trang, Nguyen Thi Hoang, Lin, Che-Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8745785/
https://www.ncbi.nlm.nih.gov/pubmed/35011934
http://dx.doi.org/10.3390/jcm11010192

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8745785/
https://www.ncbi.nlm.nih.gov/pubmed/35011934
http://dx.doi.org/10.3390/jcm11010192

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