  Polysomnography (PSG), the current gold standard method for monitoring and
detecting sleep disorders, is cumbersome and costly. At-home testing solutions,
known as home sleep apnea testing (HSAT), exist. However, they are
contact-based, a feature which limits the ability of some patient populations
to tolerate testing and discourages widespread deployment. Previous work on
non-contact sleep monitoring for sleep apnea detection either estimates
respiratory effort using radar or nasal airflow using a thermal camera, but has
not compared the two or used them together. We conducted a study on 10
participants, ages 34 - 78, with suspected sleep disorders using a hardware
setup with a synchronized radar and thermal camera. We show the first
comparison of radar and thermal imaging for sleep monitoring, and find that our
thermal imaging method outperforms radar significantly. Our thermal imaging
method detects apneas with an accuracy of 0.99, a precision of 0.68, a recall
of 0.74, an F1 score of 0.71, and an intra-class correlation of 0.70; our radar
method detects apneas with an accuracy of 0.83, a precision of 0.13, a recall
of 0.86, an F1 score of 0.22, and an intra-class correlation of 0.13. We also
present a novel proposal for classifying obstructive and central sleep apnea by
leveraging a multimodal setup. This method could be used accurately detect and
classify apneas during sleep with non-contact sensors, thereby improving
diagnostic capacities in patient populations unable to tolerate current
technology.
