A Review on Detection and Treatment  Methods of Sleep Apnea

Jayaraj, Rajeswari; Mani, Mohan Prasath; Adalarasu, K.

doi:10.7860/jcdr/2017/24129.9535

Cited by 14 publications

(13 citation statements)

References 5 publications

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“…Obstructive sleep apnea (OSA) is defined by pauses in breathing (apneas) during sleep, but during wakefulness physiology is also affected 1 . One potential OSA pathophysiology is elevated pharyngeal dilator muscle activity, which is thought to be a compensatory mechanism during wakefulness but at sleep onset, loss in pharyngeal dilator muscular activity could allow upper airway collapse in those patients who are anatomically susceptible [2][3][4] .…”

Section: Introductionmentioning

confidence: 99%

Breathing rate variability in obstructive sleep apnea during wakefulness

Pal

Martínez

Akey

et al. 2022

Journal of Clinical Sleep Medicine

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Study objectives: Obstructive sleep apnea (OSA) is defined by pauses in breathing during sleep, but daytime breathing dysregulation may also be present. Sleep may unmask breathing instability in OSA that is usually masked by behavioral influences during wakefulness. A breath-hold (BH) challenge has been used earlier to demonstrate breathing instability. One measure of breathing stability is breathing rate variability (BRV). We aimed to assess BRV during rest and in response to BH in OSA.Methods. We studied 62 participants (31 untreated OSA: Respiratory Event Index (REI) [mean±s.d.] 20±15 events/hour, 12 females, age 51±14years, BMI 32±8kg/m 2 ; 31 control: 17 females, age 47±13; BMI 26±4). Breathing movements were collected using a chest belt for 5 minutes rest and during a BH protocol (60 s baseline, 30 s BH, 90 s recovery, 3 repeats). From the breathing movements, we calculated median breathing rate (BR) and interquartile BRV at rest. We calculated change in BRV during BH recovery from baseline. Group comparisons of OSA vs. control were conducted using analysis of covariance (ANCOVA) with age, sex and BMI as covariates.Results. We found 10% higher BRV in OSA vs. control (p<0.05) during rest. In response to BH, BRV increased 7% in OSA vs. 1% in controls (p<0.001). Resting BR was not significantly different in OSA and control, and sex and age did not have any significant interaction effects. BMI was associated with BR at rest (p<0.05) and change in BRV with BH (p<0.001), but no significant BMI-by-group interaction effect was observed. Conclusions.The findings suggest breathing instability as reflected by BRV is high in OSA during wakefulness, both at rest and in response to a stimulus. Breathing instability together with high blood pressure variability in OSA may reflect a compromised cardiorespiratory consequence in OSA during wakefulness.

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Section: Introductionmentioning

confidence: 99%

Breathing rate variability in obstructive sleep apnea during wakefulness

Pal

Martínez

Akey

et al. 2022

Journal of Clinical Sleep Medicine

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“…Although previous reviews have been performed in the field of sleep apnea detection, such as analyzing devices for home detection of obstructive sleep apnea (OSA) [26], classification methods based on respiratory and oximetry signals [26], different detection approaches [27], detection and treatment methods [28]. However, no review was previously performed to assess the current development of methods for detecting sleep apnea using deep learning.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Detecting Sleep Apnea Using Deep Learning

Mostafa

Mendonça

Ravelo-García

et al. 2019

Sensors

119

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Sleep apnea is a sleep related disorder that significantly affects the population. Polysomnography, the gold standard, is expensive, inaccessible, uncomfortable and an expert technician is needed to score. Numerous researchers have proposed and implemented automatic scoring processes to address these issues, based on fewer sensors and automatic classification algorithms. Deep learning is gaining higher interest due to database availability, newly developed techniques, the possibility of producing machine created features and higher computing power that allows the algorithms to achieve better performance than the shallow classifiers. Therefore, the sleep apnea research has currently gained significant interest in deep learning. The goal of this work is to analyze the published research in the last decade, providing an answer to the research questions such as how to implement the different deep networks, what kind of pre-processing or feature extraction is needed, and the advantages and disadvantages of different kinds of networks. The employed signals, sensors, databases and implementation challenges were also considered. A systematic search was conducted on five indexing services from 2008–2018. A total of 255 papers were found and 21 were selected by considering the inclusion and exclusion criteria, using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach.

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“…Severe OSA, in addition to sleep fragmentation, produces intermittent hypoxemia and significant oxidative stress [2]. OSA represents a very important risk factor for stroke, hypertension, cardiovascular disease, hyperlipidemia, atrial fibrillation, type 2 diabetes mellitus and promote mechanisms that can increase a risk of dementia [3].…”

Section: Introductionmentioning

confidence: 99%

Continuous Positive Airway Pressure Treatment in Patients with Alzheimer’s Disease: A Systematic Review

Pérez-Cabezas

Ruiz-Molinero

Jiménez‐Rejano

et al. 2020

JCM

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Background: Epidemiological studies have suggested a pathophysiological relationship between obstructive sleep apnea syndrome (OSAS) and Alzheimer’s disease (AD). The aim of this study is to evaluate the treatment of obstructive sleep apnea with continuous positive airway pressure (CPAP) in AD and its relationship with neurocognitive function improvement. Methods: Systematic review conducted following PRISMA’s statements. Relevant studies were searched in MEDLINE, PEDro, SCOPUS, PsycINFO, Web of Science, CINAHL and SportDicus. Original studies in which CPAP treatment was developel in AD patients have been included. Results: 5 studies, 3 RCTs (Randomized controlled trials) and 2 pilot studies. In all RCTs the CPAP intervention was six weeks; 3 weeks of therapeutic CPAP vs. 3 weeks placebo CPAP (pCPAP) followed by 3 weeks tCPAP in patients with AD and OSA. The two pilot studies conducted a follow-up in which the impact on cognitive impairment was measured. Conclusions: CPAP treatment in AD patients decreases excessive daytime sleepiness and improves sleep quality. There are indications that cognitive deterioration function measured with the Mini Mental Scale decreases or evolves to a lesser extent in Alzheimer’s patients treated with CPAP. Caregivers observe stabilization in disease progression with integration of CPAP. More research is needed on the topic presented.

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A Review on Detection and Treatment Methods of Sleep Apnea

Cited by 14 publications

References 5 publications

Breathing rate variability in obstructive sleep apnea during wakefulness

Breathing rate variability in obstructive sleep apnea during wakefulness

A Systematic Review of Detecting Sleep Apnea Using Deep Learning

Continuous Positive Airway Pressure Treatment in Patients with Alzheimer’s Disease: A Systematic Review

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