Automatic Respiratory Event Scoring in Obstructive Sleep Apnea Using a Long Short-Term Memory Neural Network

Automatic Respiratory Event Scoring in Obstructive Sleep Apnea Using a Long Short-Term Memory Neural Network

The diagnosis of obstructive sleep apnea is based on daytime symptoms and the frequency of respiratory events during the night. The respiratory events are scored manually from polysomnographic recordings, which is time-consuming and expensive. Therefore, automatic scoring methods could considerably...

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Bibliographic Details
Main Authors: Duce, B. (Author), Korkalainen, H. (Author), Leino, A. (Author), Leppanen, T. (Author), Myllymaa, S. (Author), Nikkonen, S. (Author), Toyras, J. (Author)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:
accuracy
adult
aged
algorithm
apnea hypopnea index
Apnea-hypopnea indices
Article
artificial intelligence
Artificial neural networks
Automatic scoring
blood oxygen tension
blood oxygenation
body mass
Brain
controlled study
Diagnosis
diagnostic test accuracy study
Efficiency
electroencephalography
electrooculography
Epworth sleepiness scale
female
human
Humans
hypoxia
Intraclass correlation coefficients
Large dataset
learning algorithm
long short term memory network
Long short-term memory
Machine learning
major clinical study
male
Mean absolute error
Memory, Short-Term
middle aged
nerve cell network
Neural Networks, Computer
Obstructive sleep apnea
oxygen blood level
oxygen saturation
Peripheral blood
polysomnography
Polysomnography
Respiratory efforts
Respiratory event scoring
sensitivity and specificity
short term memory
signal noise ratio
Sleep Apnea Syndromes
Sleep Apnea, Obstructive
sleep disordered breathing
Sleep research
thorax
training
visual field
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