scholarly journals Validation of Somno-Art Software, a novel approach of sleep staging, compared with polysomnography in disturbed sleep profiles

2021 ◽  
Author(s):  
Laurie Thiesse ◽  
Luc Staner ◽  
Patrice Bourgin ◽  
Thomas Roth ◽  
Gil Fuchs ◽  
...  
Keyword(s):  
Patient Population ◽  
Healthy Subjects ◽  
Integrated Analysis ◽  
Sleep Staging ◽  
Rater Reliability ◽  
Software Analysis ◽  
Disturbed Sleep ◽  
Novel Approach ◽  
Obstructive Sleep ◽  
Sensitivity Specificity

Abstract Integrated analysis of heart rate (electrocardiogram [ECG]) and body movements (actimetry) during sleep in healthy subjects has previously been shown to generate similar evaluation of sleep architecture and continuity with Somno-Art Software compared to polysomnography (PSG), the gold standard. However, the performance of this new approach of sleep staging has not yet been evaluated on patients with disturbed sleep. Sleep staging from 458 sleep recordings from multiple studies comprising healthy and patient population (obstructive sleep apnea [OSA], insomnia, major depressive disorder [MDD]) was obtained from PSG visual scoring using the American Academy of Sleep Medicine (AASM) rules and from Somno-Art Software analysis on synchronized ECG and actimetry. Inter-rater reliability, evaluated with 95% absolute agreement intra-class correlation coefficient, was rated as "excellent" (ICCAAAvg95% ≥0.75) or "good" (ICCAAAvg95% ≥0.60) for all sleep parameters assessed, except NREM and N3 sleep in healthy participants (ICCAAAvg95% =0.43, ICCAAAvg95% =0.56) and N3 sleep in OSA patients (ICCAAAvg95% =0.59) rated as "fair" inter-rater reliability. Overall sensitivity, specificity, accuracy and Cohen's kappa coefficient of agreement (κ)on the entire sample were respectively of 93.3%, 69.5%, 87.8% and 0.65 for wake/sleep classification and accuracy and κ were of 68.5% and 0.55 for W/N1+N2/N3/REM classification. These performances were similar in healthy and patient population. The present results suggest that Somno-Art can be a valid sleep-staging tool in both healthy subjects and patients with OSA, insomnia or MDD. It could complement existing non-attended techniques measuring sleep-related breathing pattern or be a useful alternative to laboratory-based PSG when this latter is not available.

scholarly journals Accurate Deep Learning-Based Sleep Staging in a Clinical Population with Suspected Obstructive Sleep Apnea

2019 ◽  
pp. 1-1 ◽  
Author(s):  
Henri Korkalainen ◽  
Timo Leppanen ◽  
Juhani Aakko ◽  
Sami Nikkonen ◽  
Samu Kainulainen ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Deep Learning ◽  
Sleep Apnea ◽  
Clinical Population ◽  
Sleep Staging ◽  
Obstructive Sleep

scholarly journals Detection of obstructive sleep apnea using Belun Sleep Platform wearable with neural network-based algorithm and its combined use with STOP-Bang questionnaire

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258040
Author(s):  
Eric Yeh ◽  
Eileen Wong ◽  
Chih-Wei Tsai ◽  
Wenbo Gu ◽  
Pai-Lien Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Physiological Data ◽  
Apnea Hypopnea Index ◽  
Clinical Practices ◽  
Sleep Study ◽  
Link Type ◽  
Obstructive Sleep ◽  
Sensitivity Specificity

Many wearables allow physiological data acquisition in sleep and enable clinicians to assess sleep outside of sleep labs. Belun Sleep Platform (BSP) is a novel neural network-based home sleep apnea testing system utilizing a wearable ring device to detect obstructive sleep apnea (OSA). The objective of the study is to assess the performance of BSP for the evaluation of OSA. Subjects who take heart rate-affecting medications and those with non-arrhythmic comorbidities were included in this cohort. Polysomnography (PSG) studies were performed simultaneously with the Belun Ring in individuals who were referred to the sleep lab for an overnight sleep study. The sleep studies were manually scored using the American Academy of Sleep Medicine Scoring Manual (version 2.4) with 4% desaturation hypopnea criteria. A total of 78 subjects were recruited. Of these, 45% had AHI < 5; 18% had AHI 5–15; 19% had AHI 15–30; 18% had AHI ≥ 30. The Belun apnea-hypopnea index (bAHI) correlated well with the PSG-AHI (r = 0.888, P < 0.001). The Belun total sleep time (bTST) and PSG-TST had a high correlation coefficient (r = 0.967, P < 0.001). The accuracy, sensitivity, specificity in categorizing AHI ≥ 15 were 0.808 [95% CI, 0.703–0.888], 0.931 [95% CI, 0.772–0.992], and 0.735 [95% CI, 0.589–0.850], respectively. The use of beta-blocker/calcium-receptor antagonist and the presence of comorbidities did not negatively affect the sensitivity and specificity of BSP in predicting OSA. A diagnostic algorithm combining STOP-Bang cutoff of 5 and bAHI cutoff of 15 events/h demonstrated an accuracy, sensitivity, specificity of 0.938 [95% CI, 0.828–0.987], 0.944 [95% CI, 0.727–0.999], and 0.933 [95% CI, 0.779–0.992], respectively, for the diagnosis of moderate to severe OSA. BSP is a promising testing tool for OSA assessment and can potentially be incorporated into clinical practices for the identification of OSA. Trial registration: ClinicalTrial.org NCT03997916 https://clinicaltrials.gov/ct2/show/NCT03997916?term=belun+ring&draw=2&rank=1

scholarly journals A Narrative Review of the Association between Post-Traumatic Stress Disorder and Obstructive Sleep Apnea

2022 ◽  
Vol 11 (2) ◽  
pp. 415
Author(s):  
Catherine A. McCall ◽  
Nathaniel F. Watson
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Post Traumatic Stress Disorder ◽  
Traumatic Stress ◽  
Stress Disorder ◽  
Post Traumatic Stress ◽  
Disturbed Sleep ◽  
Obstructive Sleep ◽  
Dose Dependent Fashion ◽  
Post Traumatic

Obstructive sleep apnea (OSA) and post-traumatic stress disorder (PTSD) are often co-morbid with implications for disease severity and treatment outcomes. OSA prevalence is higher in PTSD sufferers than in the general population, with a likely bidirectional effect of the two illnesses. There is substantial evidence to support the role that disturbed sleep may play in the pathophysiology of PTSD. Sleep disturbance associated with OSA may interfere with normal rapid eye movement (REM) functioning and thus worsen nightmares and sleep-related movements. Conversely, hyperarousal and hypervigilance symptoms of PTSD may lower the arousal threshold and thus increase the frequency of sleep fragmentation related to obstructive events. Treating OSA not only improves OSA symptoms, but also nightmares and daytime symptoms of PTSD. Evidence suggests that positive airway pressure (PAP) therapy reduces PTSD symptoms in a dose-dependent fashion, but also presents challenges to tolerance in the PTSD population. Alternative OSA treatments may be better tolerated and effective for improving both OSA and PTSD. Further research avenues will be introduced as we seek a better understanding of this complex relationship.

Influence of simulated obstructive sleep apnea on thoracic fluid volume and airways resistance in asthmatic and healthy subjects

2019 ◽  
Vol 64 ◽  
pp. S54
Author(s):  
X. Cao ◽  
T.D. Bradley ◽  
S. Saha ◽  
B. Gavrilovic ◽  
C.O. Francisco ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Healthy Subjects ◽  
Fluid Volume ◽  
Obstructive Sleep ◽  
Airways Resistance

scholarly journals 0577 Cluster Analysis for the Association Between Obstructive Sleep Apnea Phenotypes: A Population-Based Longitudinal Study

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A221-A221
Author(s):  
P F Tempaku ◽  
L O Silva ◽  
T M Guimaraes ◽  
T A Vidigal ◽  
V D’Almeida ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Latent Class ◽  
Cluster Solution ◽  
Optimal Care ◽  
Sleep Study ◽  
Disturbed Sleep ◽  
Obstructive Sleep

Abstract Introduction The identification of subgroups of obstructive sleep apnea (OSA) is critical to understand disease causality and ultimately develop optimal care strategies customized for each subgroup. In this sense, we aimed to perform a cluster analysis to identify subgroups of individuals with OSA based on clinical parameters. Furthermore, we aimed to analyze whether subgroups remain after 8 years. Methods We used data derived from the Sao Paulo Epidemiologic Sleep Study (EPISONO) cohort, which was followed over 8 years. All individuals underwent polysomnography, answered questionnaires and had their blood collected for biochemical exams. OSA was defined according to an AHI equal or greater than 15 events per hour. Cluster analysis was performed using latent class analysis (LCA). Results Of the 1,042 individuals in the EPISONO baseline cohort, 68.3% accepted to participate in the follow-up study (n=712). We were able to replicate the OSA 3-cluster solution observed in previous studies: disturbed sleep, minimally symptomatic and excessively sleepy in both baseline (35.5%, 45.4% and 19.1%, respectively) and follow-up studies (41.9%, 43.4% and 14.8%, respectively). 44.8% of the participants migrated clusters between the two evaluations and the factor associated with this was a greater delta-AHI (B=-0.033, df=1, p=0.003). The optimal cluster solution for our sample based on Bayesian information criterion (BIC) was 2 clusters for baseline (disturbed sleep and excessively sleepy) and 3 clusters for follow-up (disturbed sleep, minimally symptomatic and excessively sleepy). Conclusion The results found replicate and confirm previously identified clinical clusters in OSA even in a longitudinal analysis. Support This work was supported by grants from AFIP, FAPESP and CAPES.

A NOVEL APPROACH OSA DETECTION USING SINGLE-LEAD ECG SCALOGRAM BASED ON DEEP NEURAL NETWORK

2019 ◽  
Vol 19 (04) ◽  
pp. 1950026 ◽  
Author(s):  
SINAM AJITKUMAR SINGH ◽  
SWANIRBHAR MAJUMDER
Keyword(s):  
Neural Network ◽  
Sleep Apnea ◽  
Learning Algorithm ◽  
Low Cost ◽  
Performance Outcomes ◽  
Classification Systems ◽  
Analysis Model ◽  
Time Frequency ◽  
Novel Approach ◽  
Obstructive Sleep

Obstructive sleep apnea (OSA) is the most common and severe breathing dysfunction which frequently freezes the breathing for longer than 10[Formula: see text]s while sleeping. Polysomnography (PSG) is the conventional approach concerning the treatment of OSA detection. But, this approach is a costly and cumbersome process. To overcome the above complication, a satisfactory and novel technique for interpretation of sleep apnea using ECG were recording is under development. The methods for OSA analysis based on ECG were analyzed for numerous years. Early work concentrated on extracting features, which depend entirely on the experience of human specialists. A novel approach for the prediction of sleep apnea disorder based on the convolutional neural network (CNN) using a pre-trained (AlexNet) model is analyzed in this study. After filtering per-minute segment of the single-lead ECG recording accompanied by continuous wavelet transform (CWT), the 2D scalogram images are generated. Finally, CNN based on deep learning algorithm is adopted to enhance the classification performance. The efficiency of the proposed model is compared with the previous methods that used the same datasets. Proposed method based on CNN is able to achieve the accuracy of 86.22% with 90% sensitivity in per-minute segment OSA classification. Based on per-recording OSA diagnosis, our works correctly classify all the abnormal apneic recording with 100% accuracy. Our OSA analysis model using time-frequency scalogram generates excellent independent validation performance with different state-of-the-art OSA classification systems. Experimental results proved that the proposed method produces excellent performance outcomes with low cost and less complexity.

scholarly journals Standard Diffusion-weighted MRI for the Diagnosis of Central Retinal Artery Occlusion

2020 ◽  
Author(s):  
L. A. Danyel ◽  
G. Bohner ◽  
F. Connolly ◽  
E. Siebert
Keyword(s):  
Optic Nerve ◽  
Central Retinal Artery Occlusion ◽  
Retinal Artery Occlusion ◽  
Artery Occlusion ◽  
Central Retinal Artery ◽  
Rater Reliability ◽  
Predictive Values ◽  
Diffusion Weighted ◽  
Retinal Artery ◽  
Sensitivity Specificity

Abstract Purpose To evaluate diffusion abnormalities of the retina and optic nerve in patients with central retinal artery occlusion (CRAO) using standard stroke diffusion-weighted magnetic resonance imaging (DWI). Methods In this case-control study, DWI scans of patients with nonarteritic CRAO were retrospectively assessed for acute ischemia of the retina and optic nerve. Two neuroradiologists, blinded for patient diagnosis, randomly evaluated DWI of CRAO patients and controls (a collective of stroke and transient ischemic attack [TIA] patients) for restrictions of the retina and optic nerve. We calculated statistical quality criteria and analyzed inter-rater reliability using unweighted Kappa statistics. Results 20 CRAO patients (60,6 ± 17 years) and 20 controls (60,7 ± 17 years) were included in the study. Sensitivity, specificity, positive and negative predictive values for retinal DWI restrictions were 75%/80%/79%/76% (reader 1) and 75%/100%/100%/80% (reader 2), respectively. Unweighted Kappa was κ = 0,70 (95% CI 0,48‑0,92), indicating “substantial” interrater reliability. In comparison, sensitivity, specificity, PPV and NPV (positive and negative predictive values) for restrictions of the optic nerve in CRAO were 55%/70%/65%/61% (reader 1) and 25%/100%/100%/57% (reader 2). Inter-rater reliability was “fair” with unweighted Kappa κ = 0,32 (95% CI 0,09‑0,56). Conclusions Retinal diffusion restrictions were present in a majority of CRAO patients and detectable with reasonable sensitivity, high specificity and substantial inter-rater reliability. Further studies are necessary to study time dependency of retinal diffusion restrictions, improve image quality and investigate the reliability of retinal DWI to discern CRAO from other causes of acute loss of vision.

Design Automation of Modular Industrial Robots

2009 ◽  
Author(s):  
Mehdi Tarkian ◽  
Johan O¨lvander ◽  
Xiaolong Feng ◽  
Marcus Petterson
Keyword(s):  
Design Automation ◽  
Parametric Design ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Modular Robots ◽  
Integrated Analysis ◽  
Analysis Tool ◽  
Modeling Methodology ◽  
Novel Approach ◽  
Main Components

This paper presents a novel approach for designing modular robots. There are two main components in this approach namely the modeling methodology of the robot and a framework for simulation of the models and execution of an optimization process. To illustrate the presented methodology an integrated analysis tool for an industrial robot is developed combining dynamic and geometric models in a parametric design approach. An optimization case is conducted to visualize the automation capabilities of the proposed framework, and enhance the design for modular industrial robots.

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