TY - JOUR
T1 - FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection
AU - Ye, Guanhua
AU - Yin, Hongzhi
AU - Chen, Tong
AU - Chen, Hongxu
AU - Cui, Lizhen
AU - Zhang, Xiangliang
N1 - KAUST Repository Item: Exported on 2021-01-14
Acknowledgements: This work was supported by ARC Discovery Project (GrantNo.DP190101985).
PY - 2021
Y1 - 2021
N2 - Obstructive Sleep Apnea (OSA) is a highly prevalent but inconspicuous disease that seriously jeopardizes the health of human beings. Polysomnography (PSG), the gold standard of detecting OSA, requires multiple specialized sensors for signal collection, hence patients have to physically visit hospitals and bear the costly treatment for a single detection. Recently, many single-sensor alternatives have been proposed to improve the cost efficiency and convenience. Among these methods, solutions based on RR-interval (i.e., the interval between two consecutive pulses) signals reach a satisfactory balance among comfort, portability and detection accuracy.
AB - Obstructive Sleep Apnea (OSA) is a highly prevalent but inconspicuous disease that seriously jeopardizes the health of human beings. Polysomnography (PSG), the gold standard of detecting OSA, requires multiple specialized sensors for signal collection, hence patients have to physically visit hospitals and bear the costly treatment for a single detection. Recently, many single-sensor alternatives have been proposed to improve the cost efficiency and convenience. Among these methods, solutions based on RR-interval (i.e., the interval between two consecutive pulses) signals reach a satisfactory balance among comfort, portability and detection accuracy.
UR - http://hdl.handle.net/10754/666882
UR - https://ieeexplore.ieee.org/document/9320528/
U2 - 10.1109/JBHI.2021.3050113
DO - 10.1109/JBHI.2021.3050113
M3 - Article
C2 - 33434137
SN - 2168-2208
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
ER -
