Retrospective cross-validation of automated sleep staging using electroocular recording in patients with and without sleep disordered breathing

Daniel J Levendowski, Djordje Popovic, Chris Berka, Philip R Westbrook, Daniel J Levendowski, Djordje Popovic, Chris Berka, Philip R Westbrook

Abstract

Background: Alterations of sleep duration and architecture have been associated with increased morbidity and mortality, and specifically linked to chronic cardiovascular disease and psychiatric disorders, such as type 2 diabetes or depression. Measurement of sleep quality to assist in the diagnosis or treatment of these diseases is not routinely performed due to the complexity and cost of conventional methods. The objective of this study is to cross-validate the accuracy of an automated algorithm that stages sleep from the EEG signal acquired with sensors that can be self-applied by patients.

Methods: This retrospective study design included polymsomnographic records from 19 presumably healthy individuals and 68 patients suspected of having sleep disordered breathing (SDB). Epoch-by-epoch comparisons were made between manual vs. automated sleeps staging (from the left and right electrooculogram) with the impact of SDB severity considered.

Results: Both scoring methods reported decreased Stage N3 and REM and increased wake and N1 as SDB severity increased. Inter-class correlations and Kappa coefficients were strong across all stages except N1. Agreements across all epochs for subjects with normal and patients with mild SDB were: wake = 80%, N1 = 25%, N2 = 78%, N3 = 84% and REM = 75%. Agreement decreased in patients with moderate and severe SDB amounting to: wake = 71%, N1 = 30%, N2 = 71%, N3 = 65%, and REM = 67%. Differences in detection of sleep onset were within three-minutes in 48 % of the subjects and 10-min in 73 % of the cases and were not impacted by SDB severity. Automated staging slightly underestimated total sleep time but this difference had a limited impact on the respiratory disturbance indexes.

Conclusions: This cross-validation study demonstrated that measurement of sleep architecture obtained from a single-channel of forehead EEG can be equivalent to between-rater agreement using conventional manual scoring. The accuracies obtained with automated sleep staging were inversely proportional to SDB severity at a rate similar to manual scorers. These results suggest that the automated sleep staging used in this study may prove useful in evaluating sleep quality in patients with chronic diseases.

Figures

Figure 1
Figure 1
Block diagram of the algorithm for automated sleep staging. SBI – ratio of the average sigma and beta power; DBI – ratio of delta and beta power; BEI – ratio of beta and EMG power; EMI – ratio of the delta power before and after median filtering; NAR, LAR – number and length of arousals; NSP – number of sleep spindles.
Figure 2
Figure 2
Box-Whisker plots comparing stage-specific sensitivity and positive predictive value (PPV) by SDB severity. The box represents the distributions of the 2nd and 3rd quartile about the median, the whiskers represent the 10% and 90%, and the Δ identifies outliers. Only subjects with a minimum of 20 manually scored epochs of the pertinent stage were included the respective plot.
Figure 3
Figure 3
Bland-Altman plot comparing estimates of total sleep time (TST) for manual vs. automated scoring.
Figure 4
Figure 4
Bland-Altman plot comparing estimates of sleep efficiency for manual vs. automated scoring.
Figure 5
Figure 5
Bland-Altman plot comparing the RDI calculated from total sleep time (TST) derived from manual vs. automated sleep staging.
Figure 6
Figure 6
Box-Whisker plots across all subjects showing the percentage of epochs classified as Wake or N1 by automated (manual) scoring subsequent to its detection of sleep onset but prior to recognition of sleep onset by manual (automated) scoring.

References

    1. Cappuccio FP, D’Elia L, Strazzullo P, Miller MA. Sleep duration and all-cause mortality: a systematic review and meta-analysis of prospective studies. Sleep. 2010;33(5):585–592.
    1. Gangwisch JE, Heymsfield SB, Boden-Albala B, Buijs RM, Kreier F, Pickering TG, Rundle AG, Zammit GK, Malaspina D. Sleep duration as a risk factor for diabetes incidence in a large US sample. Sleep. 2007;30(12):1667–1673.
    1. Tasali E, Laproult R, Ehrmann DA, Van Cauter E. Slow-wave sleep and the risk of type 2 diabetes in humans. PNAS. 2008;105(3):1044–1049. doi: 10.1073/pnas.0706446105.
    1. Knutson KL, Ryden AM, Mander BA, Van Cauter E. Role of sleep duration and quality in the risk and severity of type 2 diabetes mellitus. Arch Intern Med. 2006;166(16):1768–1774. doi: 10.1001/archinte.166.16.1768.
    1. Knutson KL, Van Cauter E. Association between sleep loss and increased risk of obesity and diabetes. Ann NY AcadSci. 2008;1129:287–304. doi: 10.1196/annals.1417.033.
    1. Spiegel K, Knutson K, Leproult R, Tasali E, Van Cauter E. Sleep loss: a novel risk factor for insulin resistance and Type 2 diabetes. J ApplPhysiol. 2005;99(5):2008–2019.
    1. Leproult R, Van Cauter E. Role of sleep and sleep loss in hormonal release and metabolism. EndocrDev. 2010;17:11–21.
    1. Theorell-Haglöw J, Berne C, Janson C, Sahlin C, Lindberg E. Associations between short sleep duration and central obesity in women. Sleep. 2010;33(5):593–598.
    1. Benca RM, Obermeyer WH, Thisted RA, Gillin JC. Sleep and psychiatric disorders: a meta-analysis. Arch Gen Psychiatry. 1992;49(8):651–668. doi: 10.1001/archpsyc.1992.01820080059010.
    1. Benca RM. Diagnosis and treatment of chronic insomnia: a review. PsychiatrServ. 2005;56(3):332–343.
    1. Ohayon MM. Insomnia: a ticking clock for depression? J Psychiatr Res. 2007;41(11):893–894. doi: 10.1016/j.jpsychires.2007.07.008.
    1. Ohayon MM. Epidemiology of insomnia: what we know and what we still need to learn. Sleep Med Rev. 2002;6(2):97–111. doi: 10.1053/smrv.2002.0186.
    1. Ohayon MM. Prevalence and correlates of nonrestorative sleep complaints. Arch Intern Med. 2005;165(1):35–41. doi: 10.1001/archinte.165.1.35.
    1. Ohayon MM. Nocturnal awakenings and comorbid disorders in the American general population. J Psychiatric Res. 2008;43(1):48–54. doi: 10.1016/j.jpsychires.2008.02.001.
    1. Fung MM, Peters K, Redline S, Ziegler MG, Ancoli-Israel S, Barrett-Connor E, Stone KL. Decreased slow wave sleep increases risk of developing hypertension in elderly men. Hypertension. 2011;58(4):596–603. doi: 10.1161/HYPERTENSIONAHA.111.174409.
    1. Pallayova M, Donic V, Gresova S, Peregrim I, Tomori Z. Do differences in sleep architecture exist between persons with type 2 diabetes and nondiabetic controls? J Diabetes SciTechnol. 2010;4(2):344–352.
    1. Rao MN, Blackwell T, Redline S, Stefanick ML, Ancoli-Israel S, Stone KL. Association between sleep architecture and measures of body composition. Sleep. 2009;32(4):483–490.
    1. Pillai V, Kalmbach DA, Ciesla JA. A meta-analysis of electroencephalographic sleep in depression: evidence for genetic biomarkers. Biol Psychiatry. 2011;70(10):912–919. doi: 10.1016/j.biopsych.2011.07.016.
    1. Postuma RB, Bertrand JA, Montplaisir J, Diesjardins C, Vendette M, Rios Romenets S, Panisset M, Gagnon JF. Rapid eye movement sleep behavior disorder and risk of dementia in Parkinson’s disease: A prospective study. MovDisord. 2012. Feb epub.
    1. Germain A, Buysse DJ, Nofzinger E. Sleep-specific mechanisms underlying posttraumatic stress disorder: integrative review and neurobiological hypotheses. Sleep Med Rev. 2008;12(3):185–195. doi: 10.1016/j.smrv.2007.09.003.
    1. Moser D, Anderer P, Gruber G, Parapatics S, Loretz E, Kloesch G, Heller E, Schmidt A, Danker-Hopfe H, Saletu B, Zeithofer J, Dorffner G. Sleep classification according to AASM and Rechtschaffen& Kales: effects on sleep scoring parameters. Sleep. 2009;32(2):139–149.
    1. Norman RG, Pal I, Stewart C, Walsleben JA, Rapoport DM. Interobserver agreement among sleep scores from different centers in a large dataset. Sleep. 2000;23(7):901–908.
    1. Danker-Hopfe H, Kunz D, Gruber G, Klosch G, Lorenzo JL, Himanen SL, Kemp B, Penzel T, Roschke J, Dorn H, Schlogl A, Trenker E, Dorffner G. Inter-rater reliability between scorers from eight European sleep laboratories in subjects with different sleep disorders. J Sleep Res. 2004;13:63–69. doi: 10.1046/j.1365-2869.2003.00375.x.
    1. Whitney CW, Gottlieb DJ, Redline S, Norman R, Dodge RR, Shahar E, Surovec S, Nieto FJ. Reliability of scoring respiratory disturbance indices and sleep staging. Sleep. 1998;21(7):749–757.
    1. Virkkala J, Hasan J, Varri A, Himanen SL, Muller K. Automatic sleep stage classification using two channel electro-oculography. J Neuro Methods. 2007;166:109–115. doi: 10.1016/j.jneumeth.2007.06.016.
    1. Shambroom JR, Fabregas S, Johnstone J. Validation of an automated wireless system to monitor sleep in healthy adults. J Sleep Res. 2011. epub.
    1. Berthomier C, Drouot X, Herman-Stoica M, Berthomier P, Prado J, Bokar-Thire D, Benoit O, Mattout J, D’ Ortho MP. Automated analysis of single-channel sleep EEG: validation in healthy individuals. Sleep. 2007;30(11):1587–1595.
    1. Popovic D, Khoo M, Westbrook PR. Automatic detection of slow-wave sleep from a single forehead EEG channel. Euro J Sleep Res. accepted.
    1. Rechtschafen A, Kales A. A manual of standardized terminology, techniques, and scoring system for sleep states of human subjects. U.S. Government Printing Office, Washington, DC; 1968.
    1. American Academy of Sleep Medicine Task Force. Sleep-related breathing disorders in adults: recommendations for syndrome definition and measurement techniques in clinical research. Sleep. 1999;22:667–689.
    1. Iber C, Ancoli-Israel S, Chesson A, Quan SF. he AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications. American Academy of Sleep Medicine, Westchester; 2007.
    1. Krystal AD, Edinger JD. Sleep EEG predictors and correlates of the response to cognitive behavioral therapy for insomnia. Sleep. 2010;33(5):669–677.
    1. Ulmer CS, Edinger JD, Calhoun PS. A multi-component cognitive-behavioral intervention for sleep disturbance in veterans with PTSD: a pilot study. J Clin Sleep Med. 2011;7(1):57–68.
    1. Habukawa M, Uchimura N, Maeda M, Kotorii N, Maeda H. Sleep findings in young adult patients with posttraumatic stress disorder. Biol Psychiatry. 2007;62(10):1179–1182. doi: 10.1016/j.biopsych.2007.01.007.
    1. Kobayashi I, Boarts JM, Delahanty DL. Polysomnographically measured sleep abnormalities in PTSD: a meta-analytic review. Psychophysiology. 2007;44(4):660–669. doi: 10.1111/j.1469-8986.2007.537.x.
    1. Babson KA, Feldner MT. Temporal relations between sleep problems and both traumatic event exposure and PTSD: a critical review of the empirical literature. J Anxiety Disord. 2010;24(1):1–15. doi: 10.1016/j.janxdis.2009.08.002.
    1. Spoormaker VI, Montgomery P. Disturbed sleep in post-traumatic stress disorder: secondary symptom or core feature? Sleep Med Rev. 2008;12(3):169–184. doi: 10.1016/j.smrv.2007.08.008.
    1. Germain A. Sleep disturbances in posttraumatic stress disorder. Psychiatr Ann. 2009;39(6):335–342. doi: 10.3928/00485713-20090514-02.
    1. Walker MP, Van Der Helm E. Overnight therapy? The role of sleep in emotional brain processing. Psychol Bull. 2009;135(5):731–748.
    1. Miller LJ. Prazosin for the treatment of posttraumatic stress disorder sleep disturbances. Pharmacotherapy. 2008;28(5):656–666. doi: 10.1592/phco.28.5.656.
    1. Taylor FB, Martin P, Thompson C, Williams J, Mellman TA, Gross C, Peskind ER, Raskind MA. Prazosin effects on objective sleep measures and clinical symptoms in civilian trauma posttraumatic stress disorder: a placebo-controlled study. Biol Psychiatry. 2008;63(6):629–632. doi: 10.1016/j.biopsych.2007.07.001.

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