Abstract
Obstructive Sleep Apnea (OSA) is characterized
by repetitive episodes of airflow reduction (hypopnea) or
cessation (apnea), which, as a prevalent sleep disorder, can
cause people to stop breathing for 10 to 30 seconds at a
time and lead to serious problems such as daytime fatigue,
impaired memory, and depression. This work intends to explore
automatic detection of OSA events with 1-second annotation
based on blood oxygen saturation, oronasal airflow, and ribcage
and abdomen movements. Deep Learning (DL) technology,
specifically, Convolutional Neural Network (CNN), is employed
as a feature detector to learn the characteristics of the highorder
correlation among visible data and corresponding labels.
A fully-connected layer in the last stage of the CNN is connected
to the output layer and constructs the desired number of
outputs for sleep apnea events classification. A leave-one-out
cross-validation has been conducted on the PhysioNet Sleep
Database provided by St. Vincents University Hospital and
University College Dublin, and an average accuracy of 79:61%
across normal, hypopnea, and apnea, classes is achieved.
Original language | English |
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Title of host publication | 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2018 |
Pages | 3975-3979 |
Number of pages | 5 |
DOIs | |
Publication status | Published - 2018 |
Research Field
- Exploration of Digital Health