Dataset Information

Screening for obstructive sleep apnea with novel hybrid acoustic smartphone app technology.

ABSTRACT: Background:Obstructive sleep apnea (OSA) has a high prevalence, with an estimated 425 million adults with apnea hypopnea index (AHI) of ?15 events/hour, and is significantly underdiagnosed. This presents a significant pain point for both the sufferers, and for healthcare systems, particularly in a post COVID-19 pandemic world. As such, it presents an opportunity for new technologies that can enable screening in both developing and developed countries. In this work, the performance of a non-contact OSA screener App that can run on both Apple and Android smartphones is presented. Methods:The subtle breathing patterns of a person in bed can be measured via a smartphone using the "Firefly" app technology platform [and underpinning software development kit (SDK)], which utilizes advanced digital signal processing (DSP) technology and artificial intelligence (AI) algorithms to identify detailed sleep stages, respiration rate, snoring, and OSA patterns. The smartphone is simply placed adjacent to the subject, such as on a bedside table, night stand or shelf, during the sleep session. The system was trained on a set of 128 overnights recorded at a sleep laboratory, where volunteers underwent simultaneous full polysomnography (PSG), and "Firefly" smartphone app analysis. A separate independent test set of 120 recordings was collected across a range of Apple iOS and Android smartphones, and withheld for performance evaluation by a different team. An operating point tuned for mid-sensitivity (i.e., balancing sensitivity and specificity) was chosen for the screener. Results:The performance on the test set is comparable to ambulatory OSA screeners, and other smartphone screening apps, with a sensitivity of 88.3% and specificity of 80.0% [with receiver operating characteristic (ROC) area under the curve (AUC) of 0.92], for a clinical threshold for the AHI of ?15 events/hour of detected sleep time. Conclusions:The "Firefly" app based sensing technology offers the potential to significantly lower the barrier of entry to OSA screening, as no hardware (other than the user's personal smartphone) is required. Additionally, multi-night analysis is possible in the home environment, without requiring the wearing of a portable PSG or other home sleep test (HST).

SUBMITTER: Tiron R

PROVIDER: S-EPMC7475565 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Screening for obstructive sleep apnea with novel hybrid acoustic smartphone app technology.

Tiron Roxana R Lyon Graeme G Kilroy Hannah H Osman Ahmed A Kelly Nicola N O'Mahony Niall N Lopes Cesar C Coffey Sam S McMahon Stephen S Wren Michael M Conway Kieran K Fox Niall N Costello John J Shouldice Redmond R Lederer Katharina K Fietze Ingo I Penzel Thomas T

Journal of thoracic disease 20200801 8

<h4>Background</h4>Obstructive sleep apnea (OSA) has a high prevalence, with an estimated 425 million adults with apnea hypopnea index (AHI) of ≥15 events/hour, and is significantly underdiagnosed. This presents a significant pain point for both the sufferers, and for healthcare systems, particularly in a post COVID-19 pandemic world. As such, it presents an opportunity for new technologies that can enable screening in both developing and developed countries. In this work, the performance of a n ...[more]

PMID: 32944361

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Project description:BACKGROUND:Obstructive sleep apnea syndrome (OSAS) is common in adults. People with OSAS have a higher risk of experiencing traffic accidents and occupational injuries (OIs). We aimed to clarify the diagnostic performance of a three-channel screening device (ApneaLinkTM) compared with the gold standard of full-night attended polysomnography (PSG) among hospital outpatients not referred for sleep-related symptoms. Furthermore, we aimed to determine whether manual revision of the ApneaLinkTM autoscore enhanced diagnostic performance. METHODS:We investigated 68 patients with OI and 44 without OI recruited from the University Hospital Basel emergency room, using a cross-sectional study design. Participating patients spent one night at home with ApneaLinkTM and within 2 weeks slept for one night at the sleep laboratory. We reanalyzed all ApneaLinkTM data after manual revision. RESULTS:We identified significant correlations between the ApneaLinkTM apnea-hypopnea index (AHI) autoscore and the AHI derived by PSG (r = 0.525; p <0.001) and between the ApneaLinkTM oxygen desaturation index (ODI) autoscore and that derived by PSG (r = 0.722; p <0.001). The ApneaLinkTM autoscore showed a sensitivity and specificity of 82% when comparing AHI ≥5 with the cutoff for AHI and/or ODI ≥15 from PSG. In Bland Altman plots the mean difference between ApneaLinkTM AHI autoscore and PSG was 2.75 with SD ± 8.80 (β = 0.034), and between ApneaLinkTM AHI revised score and PSG -1.50 with SD ± 9.28 (β = 0.060). CONCLUSIONS:The ApneaLinkTM autoscore demonstrated good sensitivity and specificity compared with the gold standard (full-night attended PSG). However, Bland Altman plots revealed substantial fluctuations between PSG and ApneaLinkTM AHI autoscore respectively manually revised score. This spread for the AHI from a clinical perspective is large, and therefore the results have to be interpreted with caution. Furthermore, our findings suggest that there is no clinical benefit in manually revising the ApneaLinkTM autoscore.

Project description:Rationale: Obstructive sleep apnea (OSA) is common in pregnancy and associated with maternal and fetal complications. Early detection of OSA may have important implications for maternal-fetal well-being. A screening tool combining several methods of assessment may better predict OSA among pregnant women compared with tools that rely solely on self-reported information.Objectives: To develop a screening tool combining subjective and objective measures to predict OSA in pregnant women.Methods: This study is a secondary analysis using data collected from a completed cohort of pregnant women (n = 121 during the first and n = 87 during the third trimester). Participants underwent full polysomnography and completed the Multivariable Apnea Prediction Questionnaire. The Obstructive Sleep Apnea/Hypopnea Syndrome Score and Facco apnea predictive model were obtained. Logistic regression analysis and area under the curve (AUC) were used to identify models predicting OSA risk.Results: Participants' mean age was 27.4 ± 7.0 years. The prevalence of OSA during the first and third trimester was 10.7% and 24.1%, respectively. The final model predicting OSA risk consisted of body mass index, age, and presence of tongue enlargement. During the first trimester, the AUC was 0.86 (95% confidence interval [CI], 0.76-0.96). During the third trimester, the AUC was 0.87 (95% CI, 0.77-0.96). When the first-trimester data were used to predict third-trimester OSA risk, the AUC was 0.87 (95% CI, 0.77-0.97). This model had high sensitivity and specificity when used during both trimesters. The negative posttest probabilities (probability of OSA given a negative test result) ranged from 0.03 to 0.07.Conclusions: A new model consisting of body mass index, age, and presence of tongue enlargement provided accurate screening of OSA in pregnant women, particularly African-Americans. This tool can be easily and rapidly administered in busy clinical practices without depending on patients' awareness of experiencing apnea symptoms.

Project description:BackgroundObesity is prevalent among pregnant women in the United States; obstructive sleep apnea is highly comorbid with obesity and is associated with adverse pregnancy outcomes. Screening for obstructive sleep apnea in pregnant women has remained a challenge because of a lack of validated screening tools.ObjectiveThe purpose of this study was to evaluate established obstructive sleep apnea screening tools, a sleepiness scale, and individual component items in a cohort of pregnant women with extreme obesity in mid pregnancy with the use of objective testing to determine obstructive sleep apnea status and to describe the prevalence of obstructive sleep apnea among women with extreme obesity.Study designAdult pregnant subjects, between 24 and 35 weeks gestation, with a body mass index ≥40 kg/m2 at the time of enrollment completed obstructive sleep apnea screening tools (Berlin Questionnaire, American Society of Anesthesiologists checklist, and STOP-BANG questionnaire) and the Epworth Sleepiness Scale; they also underwent physical examination of the neck, mouth, and airway. The published obstructive sleep apnea in pregnancy prediction score was calculated for each subject. Obstructive sleep apnea status for each subject was determined by the results of an overnight, unattended type III home sleep apnea test.ResultsTwenty-four percent of pregnant women with extreme obesity had obstructive sleep apnea on home sleep apnea testing in mid pregnancy (Apnea-Hypopnea Index, ≥5 events per hour]. Established obstructive sleep apnea screening tools performed very poorly to screen for obstructive sleep apnea in this cohort. Age, body mass index, neck circumference, frequently witnessed apneas, and highly likely to fall asleep while driving were associated most strongly with obstructive sleep apnea status in this cohort.ConclusionWe found that 24% of pregnant women with body mass index ≥40 kg/m2 between 24 and 35 weeks gestation have obstructive sleep apnea, defined as Apnea-Hypopnea Index ≥5 events per hour on an overnight type III home sleep apnea test. We found the Berlin Questionnaire, American Society of Anesthesiologists checklist, STOP-BANG, obstructive sleep apnea in pregnancy score by Facco et al, and the Epworth Sleepiness Scale were not useful screening tools for obstructive sleep apnea in a cohort of obese pregnant women. However, age, body mass index, neck circumference, frequently witnessed apneas, and likely to fall asleep while driving were associated with obstructive sleep apnea in this cohort. Further studies are needed to adjust the criteria and thresholds within the available screening tools to better predict obstructive sleep apnea in pregnant women with obesity.

Project description:Study objectivesThe objective of the study is to validate the performance of Belun Ring Platform, a novel home sleep apnea testing system using a patented pulse oximeter sensor and a proprietary cloud-based neural networks algorithm.MethodsThe Belun Ring captures oxygen saturation, photoplethysmography, and accelerometer signals. The Belun Ring total sleep time is derived from features extracted from accelerometer, oxygen saturation, and photoplethysmography signals. The Belun Ring respiratory event index is derived from Belun Ring total sleep time and features extracted from heart rate variability and oxygen saturation changes. A total of 50 adults without significant cardiopulmonary or neuromuscular comorbidities and heart rate affecting medications were evaluated. In-lab sleep studies were performed simultaneously with the Ring and the studies were manually scored using the American Academy of Sleep Medicine Scoring Manual 4% desaturation criteria.ResultsThe Belun Ring respiratory event index correlated well with the polysomnography-apnea-hypopnea index (AHI; r = .894, P < .001). The sensitivity and specificity in categorizing AHI ≥ 15 events/h were 0.85 and 0.87, respectively, and the positive predictive value and negative predictive value were 0.88 and 0.83, respectively. The Belun Ring total sleep time also correlated well with the polysomnography-total sleep time (r = .945, P < .001). Although the Belun Ring Platform has a good overall performance, it tends to overestimate AHI in individuals with AHI under 15 events/h and underestimate AHI in those with AHI over 15 events/h. Conclusions: In this proof-of-concept study, the Belun Ring Platform demonstrated a reasonable accuracy in predicting AHI and total sleep time in patients without significant comorbidities and heart rate-affecting medications.Clinical trial registrationRegistry: ClinicalTrials.gov; Name: Validation of a Novel Device for Screening Patients With Symptoms of Obstructive Sleep Apnea; URL: https://clinicaltrials.gov/ct2/show/NCT04121923; Identifier: NCT04121923.

Project description:BackgroundMyofunctional therapy has demonstrated efficacy in treating sleep-disordered breathing. We assessed the clinical use of a new mobile health (mHealth) app that uses a smartphone to teach patients with severe obstructive sleep apnea-hypopnea syndrome (OSAHS) to perform oropharyngeal exercises.ObjectiveWe conducted a pilot randomized trial to evaluate the effects of the app in patients with severe OSAHS.MethodsForty patients with severe OSAHS (apnea-hypoxia index [AHI]>30) were enrolled prospectively and randomized into an intervention group that used the app for 90 sessions or a control group. Anthropometric measures, Epworth Sleepiness Scale (0-24), Pittsburgh Sleep Quality Index (0-21), Iowa Oral Performance Instrument (IOPI) scores, and oxygen desaturation index were measured before and after the intervention.ResultsAfter the intervention, 28 patients remained. No significant changes were observed in the control group; however, the intervention group showed significant improvements in most metrics. AHI decreased by 53.4% from 44.7 (range 33.8-55.6) to 20.88 (14.02-27.7) events/hour (P<.001). The oxygen desaturation index decreased by 46.5% from 36.31 (27.19-43.43) to 19.4 (12.9-25.98) events/hour (P=.003). The IOPI maximum tongue score increased from 39.83 (35.32-45.2) to 59.06 (54.74-64.00) kPa (P<.001), and the IOPI maximum lip score increased from 27.89 (24.16-32.47) to 44.11 (39.5-48.8) kPa (P<.001). The AHI correlated significantly with IOPI tongue and lip improvements (Pearson correlation coefficient -0.56 and -0.46, respectively; both P<.001). The Epworth Sleepiness Scale score decreased from 10.33 (8.71-12.24) to 5.37 (3.45-7.28) in the app group (P<.001), but the Pittsburgh Sleep Quality Index did not change significantly.ConclusionsOrofacial exercises performed using an mHealth app reduced OSAHS severity and symptoms, and represent a promising treatment for OSAHS.Trial registrationSpanish Registry of Clinical Studies AWGAPN-2019-01, ClinicalTrials.gov NCT04438785; https://clinicaltrials.gov/ct2/show/NCT04438785.

Dataset Information

Screening for obstructive sleep apnea with novel hybrid acoustic smartphone app technology.

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Screening for obstructive sleep apnea with novel hybrid acoustic smartphone app technology.

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