scholarly journals Portable Sleep Monitoring Systems: Broadening the Horizons

2017 ◽  
Vol 13 (06) ◽  
pp. 773-774
Author(s):  
Naima Covassin ◽  
Virend K. Somers
Keyword(s):  
Monitoring Systems ◽  
Sleep Monitoring ◽  
Portable Sleep Monitoring

scholarly journals Assessment of Predicting Factors for Pediatric Sleep Disordered Breathing

2020 ◽  
Vol 47 (4) ◽  
pp. 377-388
Author(s):  
Soyeon Moon ◽  
Daewoo Lee ◽  
Jaegon Kim ◽  
Yeonmi Yang
Keyword(s):  
Sleep Disordered Breathing ◽  
Hyoid Bone ◽  
Apnea Hypopnea Index ◽  
Sleep Monitoring ◽  
Lateral Cephalogram ◽  
Gonial Angle ◽  
Lateral Cephalograms ◽  
Portable Sleep Monitoring ◽  
Disordered Breathing ◽  
Pediatric Sleep

The aim of this study was to evaluate the association between various predicting tools and Apnea-Hypopnea Index (AHI) to identify children with sleep disordered breathing (SDB). From 5 to 10 years old who came for orthodontic counseling, 61 children, whom had lateral cephalograms, pediatric sleep questionnaire (PSQ) records, and portable sleep monitoring results, were included in this study. A total of 17 measurements (11 distances and 6 angles) were made on lateral cephalograms. The measurements of lateral cephalograms, PSQ scales and portable sleep monitoring results were statistically analyzed. 49 of 61 (80%) patients showed AHI > 1, which suspected to have SDB and their mean AHI was 2.75. In this study, adenoid size (A/N ratio), position of the hyoid bone from mandibular plane, gonial angle, and PSQ scale were related to a higher risk of pediatric SDB. Also, oxygen desaturation index (ODI) and snoring time from sleep monitoring results were statistically significant in children with SDB using Mann-Whitney test (<i>p</i> ＜ 0.05).<br/>In conclusion, evaluation of hyoid bone position, adenoidal hypertrophy, gonial angle in lateral cephalogram, and PSQ scale was important to screen out potential SDB, especially in children with frequent snoring.

Is Portable Sleep Monitoring With Concurrent Actigraphy Accurate in Diagnosing Sleep Apnea That Requires Treatment?

CHEST Journal ◽  
2013 ◽  
Vol 144 (4) ◽  
pp. 982A
Author(s):  
Swamy Nagubadi ◽  
Abhishek Vedavalli ◽  
Mamoun Abdoh ◽  
Venkat Rajasurya ◽  
Rohit Mehta ◽  
...  
Keyword(s):  
Sleep Apnea ◽  
Sleep Monitoring ◽  
Portable Sleep Monitoring

scholarly journals Current Status and Future Challenges of Sleep Monitoring Systems: Systematic Review

10.2196/20921 ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. e20921
Author(s):  
Qiang Pan ◽  
Damien Brulin ◽  
Eric Campo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Monitoring System ◽  
The Body ◽  
Monitoring Systems ◽  
Sleep Monitoring ◽  
Advantages And Disadvantages ◽  
System Solution

Background Sleep is essential for human health. Considerable effort has been put into academic and industrial research and in the development of wireless body area networks for sleep monitoring in terms of nonintrusiveness, portability, and autonomy. With the help of rapid advances in smart sensing and communication technologies, various sleep monitoring systems (hereafter, sleep monitoring systems) have been developed with advantages such as being low cost, accessible, discreet, contactless, unmanned, and suitable for long-term monitoring. Objective This paper aims to review current research in sleep monitoring to serve as a reference for researchers and to provide insights for future work. Specific selection criteria were chosen to include articles in which sleep monitoring systems or devices are covered. Methods This review investigates the use of various common sensors in the hardware implementation of current sleep monitoring systems as well as the types of parameters collected, their position in the body, the possible description of sleep phases, and the advantages and drawbacks. In addition, the data processing algorithms and software used in different studies on sleep monitoring systems and their results are presented. This review was not only limited to the study of laboratory research but also investigated the various popular commercial products available for sleep monitoring, presenting their characteristics, advantages, and disadvantages. In particular, we categorized existing research on sleep monitoring systems based on how the sensor is used, including the number and type of sensors, and the preferred position in the body. In addition to focusing on a specific system, issues concerning sleep monitoring systems such as privacy, economic, and social impact are also included. Finally, we presented an original sleep monitoring system solution developed in our laboratory. Results By retrieving a large number of articles and abstracts, we found that hotspot techniques such as big data, machine learning, artificial intelligence, and data mining have not been widely applied to the sleep monitoring research area. Accelerometers are the most commonly used sensor in sleep monitoring systems. Most commercial sleep monitoring products cannot provide performance evaluation based on gold standard polysomnography. Conclusions Combining hotspot techniques such as big data, machine learning, artificial intelligence, and data mining with sleep monitoring may be a promising research approach and will attract more researchers in the future. Balancing user acceptance and monitoring performance is the biggest challenge in sleep monitoring system research.

scholarly journals Diagnostic accuracy and feasibility of portable sleep monitoring in patients with obstructive sleep apnea: Re-exploring the utility in the current COVID-19 pandemic

2021 ◽  
Author(s):  
Mansi Gupta ◽  
Pranav Ish ◽  
Shibdas Chakrabarti ◽  
Manas Kamal Sen ◽  
Prabhakar Mishra ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Diagnostic Accuracy ◽  
Type I ◽  
Care Hospital ◽  
Sleep Monitoring ◽  
High Burden ◽  
Sleep Studies ◽  
Obstructive Sleep ◽  
Portable Sleep Monitoring

Portable sleep monitoring (PSM) is a promising alternative diagnostic tool for Obstructive Sleep Apnea (OSA) especially in high burden resource limited settings. We aimed to determine the diagnostic accuracy and feasibility of PSM device-based studies in patients presenting for evaluation of OSA at a tertiary care hospital in North-India. PSM studies (using a Type-III PSM device) were compared for technical reliability and diagnostic accuracy with the standard laboratory-based Type-I polysomnography (PSG). Patients were also interviewed about their experience on undergoing an unsupervised PSM studies. Fifty patients (68% males) were enrolled in the study, of which only 30% patients expressed their concerns about undergoing unsupervised PSM studies which included safety issues, ease of use, diagnostic accuracy, etc. Technical acceptability criteria were easily met by the PSM studies with signal loss in 12% studies (complete data loss and inaccessible data in 6% studies), warranting repetition sleep studies in four patients. The overall sensitivity of PSM device (AHI ≥5) was 93.5% (area under curve; AUC: 0.87). The diagnostic accuracy was 68.5%, 80%, and 91.4% for mild, moderate, and severe cases of OSA, respectively. An overall strong correlation was observed between PSM-AHI (apnoea-hypopnoea index) and PSG (r>0.85, p≤0.001), especially in severe OSA. The observed sensitivity was >90% for AHI>20 (clinically significant OSA), with high specificity of 91% for severe OSA (AUC: 0.94, 0.97 for AHI>20, AHI>30 respectively). The overall Bland-Altman concordance analysis also demonstrated only a small dispersion for PSM studies with a Cronbach’s coefficient of 0.95. Therefore, there is good diagnostic accuracy as well as feasibility of home-based portable sleep studies in Indian patients. It can be promoted for widespread use in high burden countries like India for diagnosing and managing appropriately selected stable patients with high clinical probability of OSA, especially during the ongoing crises of COVID-19 pandemic.

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