Thermal Imaging and Radar for Remote Sleep Monitoring of Breathing and Apnea

• URL: http://arxiv.org/abs/2407.11936v2
• Date: Wed, 7 Aug 2024 06:43:51 GMT
• Title: Thermal Imaging and Radar for Remote Sleep Monitoring of Breathing and Apnea
• Authors: Kai Del Regno, Alexander Vilesov, Adnan Armouti, Anirudh Bindiganavale Harish, Selim Emir Can, Ashley Kita, Achuta Kadambi,
• Abstract summary: We show the first comparison of radar and thermal imaging for sleep monitoring. 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 0.73. We present a novel proposal for classifying obstructive and central sleep apnea by leveraging a multimodal setup.
• Score: 42.00356210257671
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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