Open AccessDeep Learning for Diagnosis and Classification of Obstructive Sleep Apnea: A Nasal Airflow-Based Multi-Resolution Residual Network
Author(s) -
Huijun Yue,
Lin Yang,
Yitao Wu,
Yongquan Wang,
Yun Li,
Xiaokui Guo,
Ying Huang,
Wen Wang,
Gansen Zhao,
Xiongwen Pang,
Wenbin Lei
Publication year - 2021
Publication title -
nature and science of sleep
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.715
H-Index - 34
ISSN - 1179-1608
DOI - 10.2147/nss.s297856
Subject(s) - medicine , polysomnography , obstructive sleep apnea , sleep apnea , apnea , hypopnea , kappa , test set , residual , artificial intelligence , algorithm , computer science , linguistics , philosophy
This study evaluated a novel approach for diagnosis and classification of obstructive sleep apnea (OSA), called Obstructive Sleep Apnea Smart System (OSASS), using residual networks and single-channel nasal pressure airflow signals.