scholarly journals The Effect of Body Position on Physiological Factors that Contribute to Obstructive Sleep Apnea

SLEEP ◽  
2015 ◽  
Vol 38 (9) ◽  
pp. 1469-1478 ◽  
Author(s):  
Simon A. Joosten ◽  
Bradley A. Edwards ◽  
Andrew Wellman ◽  
Anthony Turton ◽  
Elizabeth M. Skuza ◽  
...  
2007 ◽  
Vol 42 (4) ◽  
pp. 374-379 ◽  
Author(s):  
Ehab Dayyat ◽  
Muna M.A. Maarafeya ◽  
Oscar Sans Capdevila ◽  
Leila Kheirandish-Gozal ◽  
Hawley E. Montgomery-Downs ◽  
...  

SLEEP ◽  
2002 ◽  
Vol 25 (1) ◽  
pp. 66-71 ◽  
Author(s):  
Lucila B. Fernandes do Prado ◽  
Xianbin Li ◽  
Richard Thompson ◽  
Carole L. Marcus

SLEEP ◽  
2017 ◽  
Vol 40 (5) ◽  
Author(s):  
Simon A. Joosten ◽  
Jun K. Khoo ◽  
Bradley A. Edwards ◽  
Shane A. Landry ◽  
Matthew T. Naughton ◽  
...  

Sleep is judgmental to health and well-being. Deficient quality sleep is similar with a wide range of negative outcomes varies from schizophrenia to cardiovascular disorders. Obstructive sleep apnea is one of the sleep disorders. In order to identify the various syndromes the signals are need to record by using the sensors. Sleep signals are recorded by using the polysomnography (PSG) labs which is the old traditional and gold standard for recording the sleep signals. PhysioNet is a large online medical database that consists of a large collection of recordings of various physiological signals. PhysioNet database consist of sleep apnea database available. Physionet website is a universal service, physionet resource supported by the national institute of health’s National Institute of Biomedical Imaging and Bioengineering (NIBIB) and National Institute of General Medical Sciences (NIGMS). This survey paper aims to bring the different Signal Processing Techniques for Removal of Various Artifacts from Obstructive Sleep Apnea Signals to identify sleep apnea syndrome, because pre-processing is most effective and efficient to reduce unwanted signals from the original sleep signals. While recording the sleep apnea signals various artifacts records along with raw signals either directly or indirectly due to the internal and external sources like Power line interference, Muscle contractions, Electrode contact noise, Motion Artifacts, Baseline wandering, Noise generated by electronic circuits, while breathing and coughing, body position movements etc, and they need to be eliminated in order to acquire genuine health information. So in order to remove there artificats from the sleep signals the signal processing techniques (filtering techniques) are predominantly used for pre-processing of the sleep signals and have been executed in a wide variety of systems for analysis. Filtering of the sleep signal is contingent and should be implemented only when the required statistics remains cryptic


2014 ◽  
Vol 116 (3) ◽  
pp. 302-313 ◽  
Author(s):  
Danny J. Eckert ◽  
Magdy K. Younes

Historically, brief awakenings from sleep (cortical arousals) have been assumed to be vitally important in restoring airflow and blood-gas disturbances at the end of obstructive sleep apnea (OSA) breathing events. Indeed, in patients with blunted chemical drive (e.g., obesity hypoventilation syndrome) and in instances when other defensive mechanisms fail, cortical arousal likely serves an important protective role. However, recent insight into the pathogenesis of OSA indicates that a substantial proportion of respiratory events do not terminate with a cortical arousal from sleep. In many cases, cortical arousals may actually perpetuate blood-gas disturbances, breathing instability, and subsequent upper airway closure during sleep. This brief review summarizes the current understanding of the mechanisms mediating respiratory-induced cortical arousal, the physiological factors that influence the propensity for cortical arousal, and the potential dual roles that cortical arousal may play in OSA pathogenesis. Finally, the extent to which existing sedative agents decrease the propensity for cortical arousal and their potential to be therapeutically beneficial for certain OSA patients are highlighted.


Author(s):  
Giannicola Iannella ◽  
Giuseppe Magliulo ◽  
Cristina Lo Iacono ◽  
Giulia Bianchi ◽  
Antonella Polimeni ◽  
...  

Background The purpose of this study was to evaluate the prevalence of position-dependent obstructive sleep apnea (POSA) in elderly patients (≥65 years old). Adult (range 19-65 years old) and elderly patients were also compared in order to show differences in the incidence of POSA between these two groups of patients. Methods A prospective bi-center study was performed between January 2018 and May 2019. A total of 434 participants underwent polysomnography (PSG) study at home (Embletta MPR). Body position during the PSG recordings was determined. Patients were subdivided in two groups: those aged between 19 and 65 years old (adult patients) and ≥65 years old (elderly patients). POSA patients were defined using Cartwright’s system, Bignold classification, and the new Amsterdam Positional OSA Classification (APOC). Results The prevalence of POSA in elderly patients differed according to the classification system used: 49.3% using Cartwright’s classification system, 20.5% with the Bignold classification, and 22.6%, 38.9%, and 5.4% of APOC 1, APOC 2, and APOC3 sub-classes were respectively identified for the APOC classification system. No difference between adult and elderly patients regarding the prevalence of POSA was observed. No statistical differences emerged between the two groups of patients in terms of supine (p = 0.9) and non-supine AHI (p = 0.4). Conclusions A significant number of elderly patients could be considered treatable with positional therapy according to the APOC classification. However, the efficacy and applicability of positional therapy in elderly patients must be confirmed by further research.


CHEST Journal ◽  
2000 ◽  
Vol 118 (4) ◽  
pp. 1018-1024 ◽  
Author(s):  
Arie Oksenberg ◽  
Iyad Khamaysi ◽  
Donald S. Silverberg ◽  
Ariel Tarasiuk

2003 ◽  
Vol 36 (4) ◽  
pp. 335-338 ◽  
Author(s):  
Caglar Cuhadaroglu ◽  
Nesil Keles ◽  
Burak Erdamar ◽  
Nese Aydemir ◽  
Emre Yucel ◽  
...  

2008 ◽  
Vol 72 (6) ◽  
pp. 897-900 ◽  
Author(s):  
Kevin D. Pereira ◽  
Nisha K. Rathi ◽  
Adil Fatakia ◽  
Sajid A. Haque ◽  
Richard J. Castriotta

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