scholarly journals A New Feature with the Potential to Detect the Severity of Obstructive Sleep Apnoea via Snoring Sound Analysis

2020 ◽  
Vol 17 (8) ◽  
pp. 2951
Author(s):  
Shota Hayashi ◽  
Meiyo Tamaoka ◽  
Tomoya Tateishi ◽  
Yuki Murota ◽  
Ibuki Handa ◽  
...  
Keyword(s):  
Obstructive Sleep Apnoea ◽  
Sleep Apnoea ◽  
Statistical Analyses ◽  
Mel Frequency Cepstral Coefficients ◽  
Sound Analysis ◽  
Obstructive Sleep ◽  
Snore Sounds ◽  
New Feature ◽  
Physiological Sensors ◽  
Cluster 2

The severity of obstructive sleep apnoea (OSA) is diagnosed with polysomnography (PSG), during which patients are monitored by over 20 physiological sensors overnight. These sensors often bother patients and may affect patients’ sleep and OSA. This study aimed to investigate a method for analyzing patient snore sounds to detect the severity of OSA. Using a microphone placed at the patient’s bedside, the snoring and breathing sounds of 22 participants were recorded while they simultaneously underwent PSG. We examined some features from the snoring and breathing sounds and examined the correlation between these features and the snore-specific apnoea-hypopnea index (ssAHI), defined as the number of apnoea and hypopnea events during the hour before a snore episode. Statistical analyses revealed that the ssAHI was positively correlated with the Mel frequency cepstral coefficients (MFCC) and volume information (VI). Based on clustering results, mild snore sound episodes and snore sound episodes from mild OSA patients were mainly classified into cluster 1. The results of clustering severe snore sound episodes and snore sound episodes from severe OSA patients were mainly classified into cluster 2. The features of snoring sounds that we identified have the potential to detect the severity of OSA.

scholarly journals The different clinical faces of obstructive sleep apnoea: a cluster analysis

2014 ◽  
Vol 44 (6) ◽  
pp. 1600-1607 ◽  
Author(s):  
Lichuan Ye ◽  
Grace W. Pien ◽  
Sarah J. Ratcliffe ◽  
Erla Björnsdottir ◽  
Erna Sif Arnardottir ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Obstructive Sleep Apnoea ◽  
Clinical Presentation ◽  
Professional Community ◽  
Sleep Apnoea ◽  
Care Seeking ◽  
Entire Cohort ◽  
Disturbed Sleep ◽  
Obstructive Sleep ◽  
Cluster 2

Although commonly observed in clinical practice, the heterogeneity of obstructive sleep apnoea (OSA) clinical presentation has not been formally characterised.This study was the first to apply cluster analysis to identify subtypes of patients with OSA who experience distinct combinations of symptoms and comorbidities. An analysis of baseline data from the Icelandic Sleep Apnoea Cohort (822 patients with newly diagnosed moderate-to-severe OSA) was performed.Three distinct clusters were identified. They were classified as the “disturbed sleep group” (cluster 1), “minimally symptomatic group” (cluster 2) and “excessive daytime sleepiness group” (cluster 3), consisting of 32.7%, 24.7% and 42.6% of the entire cohort, respectively. The probabilities of having comorbid hypertension and cardiovascular disease were highest in cluster 2 but lowest in cluster 3. The clusters did not differ significantly in terms of sex, body mass index or apnoea–hypopnoea index.Patients with OSA have different patterns of clinical presentation, which need to be communicated to both the lay public and the professional community with the goal of facilitating care-seeking and early identification of OSA. Identifying distinct clinical profiles of OSA creates a foundation for offering more personalised therapies in the future.

The severity of obstructive sleep apnoea does not influence ambient IGF-I levels

2017 ◽  
Author(s):  
Julie Lynch ◽  
Nikolaos Kyriakakis ◽  
Mark Elliott ◽  
Dipansu Ghosh ◽  
Mitchell Nix ◽  
...  
Keyword(s):  
Obstructive Sleep Apnoea ◽  
Sleep Apnoea ◽  
Obstructive Sleep ◽  
Igf I

Requirement of staged interventions in a complex case of Acromegaly and Obstructive Sleep Apnoea

2020 ◽  
Author(s):  
Mili Dhar ◽  
Jennifer Elias ◽  
Benjamin Field ◽  
Sunil Zachariah ◽  
Julian Emmanuel
Keyword(s):  
Obstructive Sleep Apnoea ◽  
Sleep Apnoea ◽  
Complex Case ◽  
Obstructive Sleep

Obstructive sleep apnoea is highly prevalent in a bariatric surgical population

2020 ◽  
Author(s):  
Rachel Agius ◽  
Claudia Coelho ◽  
James Crane ◽  
Piya Sen Gupta ◽  
Barbara McGowan
Keyword(s):  
Obstructive Sleep Apnoea ◽  
Sleep Apnoea ◽  
Obstructive Sleep

What Non-Alcoholic Fatty Liver Disease Has Got to Do with Obstructive Sleep Apnoea Syndrome and viceversa?

2014 ◽  
Vol 23 (3) ◽  
pp. 291-299 ◽  
Author(s):  
Giovanni Tarantino ◽  
Vincenzo Citro ◽  
Carmine Finelli
Keyword(s):  
Liver Disease ◽  
Fatty Liver ◽  
Obstructive Sleep Apnoea ◽  
Fatty Liver Disease ◽  
Sleep Apnoea ◽  
Obstructive Sleep Apnoea Syndrome ◽  
Alcoholic Fatty Liver ◽  
Sleep Apnoea Syndrome ◽  
Obstructive Sleep ◽  
Alcoholic Fatty Liver Disease

Non-alcoholic fatty liver disease (NAFLD) and obstructive sleep apnoea syndrome (OSAS) are common conditions, frequently encountered in patients with obesity and/or metabolic syndrome. NAFLD and OSAS are complex diseases that involve an interaction of several intertwined factors. Several lines of evidence lend credence to an immune system derangement in these patients, i.e. the low grade chronic inflammation status, reckoned to be the most important factor in causing and maintaining these two illnesses. Furthermore, it is emphasized the main role of spleen involvement, as a novel mechanism. In this review the contribution of the visceral adiposity in both NAFLD and OSAS is stressed as well as the role of intermittent hypoxia. Finally, a post on the prevention of systemic inflammation is made.Abbreviations: ALT: alanine aminotransferase; BMI: body mass index; CCR2: chemokine (C-C motif) receptor 2; CRP: C-reactive protein; CPAP: continuous positive airway pressure; FFA: free fatty acid; IGF-I: insulin-like growth factor; IR: insulin resistance; IL-6: interleukin-6; IH: intermittent hypoxia; IKK-β: IκB kinase β; LPS: lipopolysaccharide; MCP-1: monocyte chemoattractant protein-1; NAFLD: non-alcoholic fatty liver disease; NASH: nonalcoholic steatohepatitis; NEFA: non-esterified fatty acid; NF-κB: nuclear factor-κB; OSAS: obstructive sleep apnoea syndrome; PAI-1: plasminogen activator inhibitor-1; ROS: reactive oxygen species; TNF-α: tumor necrosis factor-α; T2D: type 2 diabetes.

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