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

Eric Yeh, Eileen Wong, Chih-Wei Tsai, Wenbo Gu, Pai-Lien Chen, Lydia Leung, I-Chen Wu, Kingman P Strohl, Rodney J Folz, Wail Yar, Ambrose A Chiang, Eric Yeh, Eileen Wong, Chih-Wei Tsai, Wenbo Gu, Pai-Lien Chen, Lydia Leung, I-Chen Wu, Kingman P Strohl, Rodney J Folz, Wail Yar, Ambrose A Chiang

Abstract

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://ichgcp.net/clinical-trials-registry/NCT03997916?term=belun+ring&draw=2&rank=1.

Conflict of interest statement

A. A. C. received grant BL2018/1001 from Belun Technology for conducting this study at University Hospitals Cleveland Medical Center but otherwise has no financial conflicts of interest. I. W. is a professor in Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan, and has received research funding from Belun Technology. W. G. is a PhD student at the Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan, and is also an engineer of Belun Technology. L. L. and C. T. are Belun Technology Company employees. E. Y., E. W., W. Y., K. P. S., R. J. F., and P. C. have no financial conflicts of interest for the submitted work.

Figures

Fig 1. BSP neural network architecture diagram.
Fig 1. BSP neural network architecture diagram.
The BSP proprietary OSA detection algorithm was built using neural networks and trained with a dataset of 5,783 patients and 8,417 records of overnight sleep studies. (A) Respiratory event detection model containing 160, 80, and 20 neurons; (B) Total sleep time estimation model containing 160, 80, and 5 neurons.
Fig 2. CONSORT flow diagram.
Fig 2. CONSORT flow diagram.
Fig 3. Scatterplots.
Fig 3. Scatterplots.
(A) Scatterplot comparing bAHI to PSG-AHI; (B) Scatterplot comparing bTST to PSG-TST. bAHI = Belun apnea-hypopnea index; PSG-AHI = polysomnography apnea-hypopnea index; bTST = Belun total sleep time; PSG-TST = polysomnography total sleep time.
Fig 4. Bland-Altman plots.
Fig 4. Bland-Altman plots.
(A) Bland-Altman plot for bAHI vs. PSG-AHI; (B) Bland-Altman plot for bTST vs. PSG-TST. bAHI = Belun apnea-hypopnea index; PSG-AHI = polysomnography apnea-hypopnea index; bTST = Belun total sleep time; PSG-TST = polysomnography total sleep time.
Fig 5. ROC curves for bAHI vs.…
Fig 5. ROC curves for bAHI vs. PSG-AHI at PSG-AHI Cutoffs of 5, 15, and 30 events/h (N = 78).
ROC = receiver operator characteristic; bAHI = Belun apnea-hypopnea index; PSG-AHI = polysomnography apnea-hypopnea index.
Fig 6. The combined diagnostic algorithm using…
Fig 6. The combined diagnostic algorithm using STOP-Bang cutoff of 5 and bAHI cutoff of 15 events/h curtailed the need for further sleep testing by 61%.
bAHI = Belun apnea-hypopnea index. a This false negative case had a PSG-AHI of 17.6 events/h; b These two false positive cases had mild OSA with PSG-AHI ≥ 5 events/h.

References

    1. Gami AS, Howard DE, Olson EJ, Somers VK. Day–night pattern of sudden death in obstructive sleep apnea. N. Engl. J. Med. 2005; 352(12): 1206–1214. doi: 10.1056/NEJMoa041832
    1. Punjabi NM, Caffo BS, Goodwin JL, Gottlieb DJ, Newman AB, O’Connor GT, et al.. Sleep-disordered breathing and mortality: a prospective cohort study. PLOS Med. 2009; 6(8): e1000132. doi: 10.1371/journal.pmed.1000132
    1. Young T, Finn L, Peppard PE, Szklo-Coxe M, Austin D, Nieto FJ, et al.. Sleep disordered breathing and mortality: eighteen-year follow-up of the Wisconsin sleep cohort. Sleep. 2008; 31(8): 1071–1078. doi: 10.5665/sleep/31.8.1071
    1. Marin JM, Agusti A, Villar I, Forner M, Nieto D, Carrizo SJ, et al.. Association between treated and untreated obstructive sleep apnea and risk of hypertension. JAMA. 2012; 307(20): 2169–2176. doi: 10.1001/jama.2012.3418
    1. Gottlieb DJ, Yenokyan G, Newman AB, O’Connor GT, Punjabi NM, Quan SF, et al.. Prospective study of obstructive sleep apnea and incident coronary heart disease and heart failure. Circulation. 2010; 122(4): 352–360. doi: 10.1161/CIRCULATIONAHA.109.901801
    1. Arzt M, Young T, Finn L, Skatrud JB, Bradley TD. Association of sleep-disordered breathing and the occurrence of stroke. Am. J. Respir. Crit. Care Med. 2005; 172(11): 1447–1451. doi: 10.1164/rccm.200505-702OC
    1. Marin JM, Carrizo SJ, Vicente E, Agusti AGN. Long-term cardiovascular outcomes in men with obstructive sleep apnoea-hypopnoea with or without treatment with continuous positive airway pressure: an observational study. Lancet. 2005; 365(9464): 1046–1053. doi: 10.1016/S0140-6736(05)71141-7
    1. Peppard PE, Young T, Barnet JH, Palta M, Hagen EW, Hla KM. Increased prevalence of sleep-disordered breathing in adults. Am. J. Epidemiol. 2013; 177(9): 1006–1014. doi: 10.1093/aje/kws342
    1. Heinzer R, Vat S, Marques-Vidal P, Marti-Soler H, Andries D, Tobback N, et al.. Prevalence of sleep-disordered breathing in the general population: the HypnoLaus study. Lancet Respir. Med. 2015; 3(4): 310–318. doi: 10.1016/S2213-2600(15)00043-0
    1. Senaratna C V, Perret JL, Lodge CJ, Lowe AJ, Campbell BE, Matheson MC, et al.. Prevalence of obstructive sleep apnea in the general population: a systematic review. Sleep Med. Rev. 2017; 34: 70–81. doi: 10.1016/j.smrv.2016.07.002
    1. Collop AN, Tracy LS, Kapur V, Mehra R, Kuhlmann D, Fleishman AS, et al.. Obstructive sleep apnea devices for out-of-center (OOC) testing: technology evaluation. J. Clin. Sleep Med. 2011; 07(05): 531–548. doi: 10.5664/JCSM.1328
    1. Kapur VK, Auckley DH, Chowdhuri S, Kuhlmann DC, Mehra R, Ramar K, et al.. Clinical practice guideline for diagnostic testing for adult obstructive sleep apnea: an American Academy of Sleep Medicine clinical practice guideline. J. Clin. Sleep Med. 2017; 13: 479–504. doi: 10.5664/jcsm.6506
    1. Rosen I, Kirsch D, Chervin R, Carden K, Ramar K, Aurora R, et al.. Clinical use of a home sleep apnea test: an American Academy of Sleep Medicine position statement. J. Clin. Sleep Med. 2017; 13: 1205–1207. doi: 10.5664/jcsm.6774
    1. Mysliwiec V, Martin JL, Ulmer CS, Chowdhuri S, Brock MS, Spevak C, et al.. The Management of chronic insomnia disorder and obstructive sleep apnea: synopsis of the 2019 U.S. Department of Veterans Affairs and U.S. Department of Defense clinical practice guidelines. Ann. Intern. Med. 2020; 172(5): 325–336. doi: 10.7326/M19-3575
    1. Schnall RP, Shlitner A, Sheffy J, Kedar R, Lavie P. Periodic, Profound peripheral vasoconstriction—a new marker of obstructive sleep apnea. Sleep. 1999; 22(7): 939–946. doi: 10.1093/sleep/22.7.939
    1. Ayas NT, Pittman S, MacDonald M, White DP. Assessment of a wrist-worn device in the detection of obstructive sleep apnea. Sleep Med. 2003; 4(5): 435–442. doi: 10.1016/s1389-9457(03)00111-4
    1. Ioachimescu OC, Allam JS, Samarghandi A, Anand N, Fields BG, Dholakia SA, et al.. Performance of peripheral arterial tonometry—based testing for the diagnosis of obstructive sleep apnea in a large sleep clinic cohort. J. Clin. Sleep Med. 2020; 16(10). doi: 10.5664/jcsm.8620
    1. Peake JM, Kerr G, Sullivan JP. A critical review of consumer wearables, mobile applications, and equipment for providing biofeedback, monitoring stress, and sleep in physically active populations. Front. Physiol. 2018; 9: 743. doi: 10.3389/fphys.2018.00743
    1. Dunn J, Runge R, Snyder M. Wearables and the medical revolution. Per. Med. 2018; 15(5): 429–448. doi: 10.2217/pme-2018-0044
    1. Ko P-RT, Kientz J, Choe EK, Kay M, Carol LA, Watson NF. Consumer sleep technologies: a review of the landscape. J. Clin. Sleep Med. 2021; 11(12): 1455–1461. doi: 10.5664/jcsm.5288
    1. de Zambotti M, Cellini N, Goldstone A, Colrain I, Baker F. Wearable sleep technology in clinical and research settings. Med. Sci. Sports Exerc. 2019; 51(7): 1538–1557. doi: 10.1249/MSS.0000000000001947
    1. de Zambotti M, Cellini N, Menghini L, Sarlo M, Baker FC. Sensors capabilities, performance, and use of consumer sleep technology. Sleep Med. Clin. 2020; 15(1): 1–30. doi: 10.1016/j.jsmc.2019.11.003
    1. O’Mahony AM, Garvey JF, McNicholas WT. Technologic advances in the assessment and management of obstructive sleep apnoea beyond the apnoea-hypopnoea index: a narrative review. J. Thorac. Dis. Vol 12, No 9 (September 2020) J. Thorac. Dis. 2020. doi: 10.21037/jtd-sleep-2020-003
    1. Papini GB, Fonseca P, van Gilst MM, Bergmans JWM, Vullings R, Overeem S. Wearable monitoring of sleep-disordered breathing: estimation of the apnea–hypopnea index using wrist-worn reflective photoplethysmography. Sci. Rep. 2020; 10(1): 13512. doi: 10.1038/s41598-020-69935-7
    1. Randerath W, Bassetti CL, Bonsignore MR, Farre R, Ferini-Strambi L, Grote L, et al.. Challenges and perspectives in obstructive sleep apnoea. Eur. Respir. J. 2018; 52(3): 1702616. doi: 10.1183/13993003.02616-2017
    1. Scott H, Lack L, Lovato N. A systematic review of the accuracy of sleep wearable devices for estimating sleep onset. Sleep Med. Rev. 2020; 49: 101227. doi: 10.1016/j.smrv.2019.101227
    1. Perez-Pozuelo I, Zhai B, Palotti J, Mall R, Aupetit M, Garcia-Gomez JM, et al.. The future of sleep health: a data-driven revolution in sleep science and medicine. npj Digit. Med. 2020; 3(1): 42. doi: 10.1038/s41746-020-0244-4
    1. Hicks JL, Althoff T, Sosic R, Kuhar P, Bostjancic B, King AC, et al.. Best practices for analyzing large-scale health data from wearables and smartphone apps. npj Digit. Med. 2019; 2(1): 45. doi: 10.1038/s41746-019-0121-1
    1. He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat. Med. 2019; 25(1): 30–36. doi: 10.1038/s41591-018-0307-0
    1. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 2019; 25(1): 44–56. doi: 10.1038/s41591-018-0300-7
    1. Gu W, Leung L. A novel home screening platform for obstructive sleep apnea through wearable ring-type pulse oximeter. Sleep. 2018; 41(Apr 2018). doi: 10.1093/sleep/zsy061.481
    1. Gu W, Leung L, Kwok KC, Wu I-C, Folz RJ, Chiang AA. Belun Ring Platform: a novel home sleep apnea testing system for assessment of obstructive sleep apnea. J. Clin. Sleep Med. 2020; 16(9): 1611–1617. doi: 10.5664/jcsm.8592
    1. Chiu H-Y, Chen P-Y, Chuang L-P, Chen N-H, Tu Y-K, Hsieh Y-J, et al.. Diagnostic accuracy of the Berlin questionnaire, STOP-BANG, STOP, and Epworth sleepiness scale in detecting obstructive sleep apnea: A bivariate meta-analysis. Sleep Med. Rev. 2017; 36: 57–70. doi: 10.1016/j.smrv.2016.10.004
    1. Chung F, Abdullah HR, Liao P. STOP-Bang questionnaire: a practical approach to screen for obstructive sleep apnea. Chest. 2016; 149(3): 631–638. doi: 10.1378/chest.15-0903
    1. Nagappa M, Liao P, Wong J, Auckley D, Ramachandran SK, Memtsoudis S, et al.. Validation of the STOP-Bang questionnaire as a screening tool for obstructive sleep apnea among different populations: a systematic review and meta-analysis. PLOS One. 2015; 10(12): e0143697. doi: 10.1371/journal.pone.0143697
    1. Berry R, Brooks R, Gamaldo C, Harding S, Lloyd R, Marcus C, et al.. The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications. Version 2.4. 2017.
    1. Berry R, Budhiraja R, Gottlieb D, Gozal D, Iber C, Kapur V, et al.. Rules for scoring respiratory events in sleep: update of the 2007 AASM manual for the scoring of sleep and associated events. J. Clin. Sleep Med. 2012; 08(05): 597–619. doi: 10.5664/jcsm.2172
    1. R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, 2021. Feb 15. Available from:
    1. Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. JNCI J. Natl. Cancer Inst. 1959; 22(4): 719–748. doi: 10.1093/jnci/22.4.719
    1. Bland JM, Altman D. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986; 327(8476): 307–310. doi: 10.1016/S0140-6736(86)90837-8
    1. Efron B. Nonparametric standard errors and confidence Intervals. Can. J. Stat. 1981; 9(2): 139–158. doi: 10.2307/3314608
    1. Li W, Wang R, Huang D, Liu X, Jin W, Yang S. Assessment of a portable monitoring device Watch PAT 200 in the diagnosis of obstructive sleep apnea. Eur. Arch. Oto-Rhino-Laryngology. 2013; 270: 3099–3105. doi: 10.1007/s00405-013-2555-4
    1. Gan YJ, Lim L, Chong YK. Validation study of WatchPat 200 for diagnosis of OSA in an Asian cohort. Eur. Arch. Oto-Rhino-Laryngology. 2017; 274(3): 1741–1745. doi: 10.1007/s00405-016-4351-4
    1. Massie F, de Almeida DM, Dreesen P, Thijs I, Vranken J, Klerkx S. An evaluation of the NightOwl home sleep apnea testing system. J. Clin. Sleep Med. 2018; 14: 1791–1796. doi: 10.5664/jcsm.7398
    1. Choi JH, Kim EJ, Kim YS, Choi J, Kim TH, Kwon SY, et al.. Validation study of portable device for the diagnosis of obstructive sleep apnea according to the new AASM scoring criteria: Watch-PAT 100. Acta Otolaryngol. 2010; 130(7): 838–843. doi: 10.3109/00016480903431139
    1. Zhang Z, Sowho M, Otvos T, Sperandio LS, East J, Sgambati F, et al.. A comparison of automated and manual sleep staging and respiratory event recognition in a portable sleep diagnostic device with in-lab sleep study. J. Clin. Sleep Med. 2020. doi: 10.5664/jcsm.8278
    1. Ioachimescu OC, Dholakia SA, Venkateshiah SB, Fields B, Samarghandi A, Anand N, et al.. Improving the performance of peripheral arterial tonometry-based testing for the diagnosis of obstructive sleep apnea. J. Investig. Med. 2020; jim-2020-001448. doi: 10.1136/jim-2020-001448
    1. Morales CR, Hurley S, Wick LC, Staley B, Pack FM, Gooneratne NS, et al.. In-home, self-assembled sleep studies are useful in diagnosing sleep apnea in the elderly. Sleep. 2012; 35(11): 1491–1501. doi: 10.5665/sleep.2196
    1. Gurubhagavatula I, Fields BG, Morales CR, Hurley S, Pien GW, Wick LC, et al.. Screening for severe obstructive sleep apnea syndrome in hypertensive outpatients. J. Clin. Hypertens. 2013; 15(4): 279–288. doi: 10.1111/jch.12073
    1. Mashaqi S, Staebler D, Mehra R. Combined nocturnal pulse oximetry and questionnaire-based obstructive sleep apnea screening–A cohort study. Sleep Med. 2020; 72: 157–163. doi: 10.1016/j.sleep.2020.03.027

Source: PubMed

Sponsoren und Mitarbeiter

Krankheiten

Drogeninterventionen

3
Abonnieren