Doppler Radar-Based Non-Contact Health Monitoring for Obstructive Sleep Apnea Diagnosis: A Comprehensive Review
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Centre for Health Technologies, Faculty of Engineering & Information Technology (FEIT), University of Technology, Sydney (UTS), Ultimo NSW 2007, Australia
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Digital Health Technology, Respiratory Care Solutions, ResMed Ltd., Bella Vista NSW 2153, Australia
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The Discipline of Computing and Security, School of Science, Edith Cowan University (ECU), Joondalup WA 6027, Australia
4
School of Computer Science and Software Engineering (Adjunct), University of Western Australia (UWA), Crawley WA 6009, Australia
*
Author to whom correspondence should be addressed.
Big Data Cogn. Comput. 2019, 3(1), 3; https://doi.org/10.3390/bdcc3010003
Received: 2 December 2018 / Revised: 23 December 2018 / Accepted: 24 December 2018 / Published: 1 January 2019
(This article belongs to the Special Issue Health Assessment in the Big Data Era)
Today’s rapid growth of elderly populations and aging problems coupled with the prevalence of obstructive sleep apnea (OSA) and other health related issues have affected many aspects of society. This has led to high demands for a more robust healthcare monitoring, diagnosing and treatments facilities. In particular to Sleep Medicine, sleep has a key role to play in both physical and mental health. The quality and duration of sleep have a direct and significant impact on people’s learning, memory, metabolism, weight, safety, mood, cardio-vascular health, diseases, and immune system function. The gold-standard for OSA diagnosis is the overnight sleep monitoring system using polysomnography (PSG). However, despite the quality and reliability of the PSG system, it is not well suited for long-term continuous usage due to limited mobility as well as causing possible irritation, distress, and discomfort to patients during the monitoring process. These limitations have led to stronger demands for non-contact sleep monitoring systems. The aim of this paper is to provide a comprehensive review of the current state of non-contact Doppler radar sleep monitoring technology and provide an outline of current challenges and make recommendations on future research directions to practically realize and commercialize the technology for everyday usage.
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Keywords:
sleep monitoring; patient monitoring; non-contact monitoring; vital signs monitoring; health monitoring; obstructive sleep apnea; sleep; sensors; Doppler radar; non-contact vital signs; respiration; cardiac activity; pressure; Tidal volume; sleep wake pattern; apnea-hypopnea index; Cheyne-Stokes respiration; computer vision; machine learning; body orientations; body movements
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MDPI and ACS Style
Tran, V.P.; Al-Jumaily, A.A.; Islam, S.M.S. Doppler Radar-Based Non-Contact Health Monitoring for Obstructive Sleep Apnea Diagnosis: A Comprehensive Review. Big Data Cogn. Comput. 2019, 3, 3. https://doi.org/10.3390/bdcc3010003
AMA Style
Tran VP, Al-Jumaily AA, Islam SMS. Doppler Radar-Based Non-Contact Health Monitoring for Obstructive Sleep Apnea Diagnosis: A Comprehensive Review. Big Data and Cognitive Computing. 2019; 3(1):3. https://doi.org/10.3390/bdcc3010003
Chicago/Turabian StyleTran, Vinh P.; Al-Jumaily, Adel A.; Islam, Syed M.S. 2019. "Doppler Radar-Based Non-Contact Health Monitoring for Obstructive Sleep Apnea Diagnosis: A Comprehensive Review" Big Data Cogn. Comput. 3, no. 1: 3. https://doi.org/10.3390/bdcc3010003
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