Distinguishing Obstructive Versus Central Apneas in Infrared Video of Sleep Using Deep Learning: Validation Study

Background: Sleep apnea is a respiratory disorder characterized by
an intermittent reduction (hypopnea) or cessation (apnea) of
breathing during sleep. Depending on the presence of a breathing
effort, sleep apnea is divided into obstructive sleep apnea (OSA)
and central sleep apnea (CSA) based on the different pathologies
involved. If the majority of apneas in a person are obstructive,
they will be diagnosed as OSA or otherwise as CSA. In addition, as
it is challenging and highly controversial to divide hypopneas into
central or obstructive, the decision about sleep apnea type (OSA vs
CSA) is made based on apneas only. Choosing the appropriate
treatment relies on distinguishing between obstructive apnea (OA)
and central apnea (CA). Objective: The objective of this study was
to develop a noncontact method to distinguish between OAs and CAs.
Methods: Five different computer vision-based algorithms were used
to process infrared (IR) video data to track and analyze body
movements to differentiate different types of apnea (OA vs CA). In
the first two methods, supervised classifiers were trained to
process optical flow information. In the remaining three methods, a
convolutional neural network (CNN) was designed to extract
distinctive features from optical flow and to distinguish OA from
CA. Results: Overnight sleeping data of 42 participants (mean age
53, SD 15 years; mean BMI 30, SD 7 kg/m2; 27 men and 15 women; mean
number of OA 16, SD 30; mean number of CA 3, SD 7; mean
apnea-hypopnea index 27, SD 31 events/hour; mean sleep duration 5
hours, SD 1 hour) were collected for this study. The test and train
data were recorded in two separate laboratory rooms. The
best-performing model (3D-CNN) obtained 95% accuracy and an F1
score of 89% in differentiating OA vs CA. Conclusions: In this
study, the first vision-based method was developed that
differentiates apnea types (OA vs CA). The developed algorithm
tracks and analyses chest and abdominal movements captured via an
IR video camera. Unlike previously developed approaches, this
method does not require any attachment to a user that could
potentially alter the sleeping condition.

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Distinguishing Obstructive Versus Central Apneas in Infrared
Video of Sleep Using Deep Learning: Validation Study