FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection

FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection

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 hospita...

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Bibliographic Details
Main Authors: Chen, H. (Author), Chen, T. (Author), Cui, L. (Author), Ye, G. (Author), Yin, H. (Author), Zhang, X. (Author)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:
Article
brain depth stimulation
breathing rate
Cost efficiency
Detection accuracy
discriminant analysis
electrocardiography
energy consumption
Energy-Efficiency
event related potential
feature extraction
feature extraction algorithm
Frequency extraction
Gold standards
heart failure
heart rate
heart rate variability
hidden Markov model
human
Humans
image quality
intensive care unit
learning algorithm
machine learning
mathematical model
nerve cell network
Obstructive sleep apnea
obstructive sleep apnea detection
oximetry
Oximetry
Patient treatment
plethysmography
polysomnography
Polysomnography
prevalence
pulmonary hypertension
QRS complex
RR interval
semantics
Signal collection
Single sensor
Sleep Apnea, Obstructive
sleep disorder
sleep disordered breathing
social network
Social Networking
support vector machine
time series analysis
training
wearable devices
wrist
Wrist
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