Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Wavelet Lifting over Information-Based EEG Graphs for Motor Imagery Data Classification Read chapter Read first chapter

2014 | OriginalPaper | Chapter

An EOG-Based Automatic Sleep Scoring System and Its Related Application in Sleep Environmental Control

Authors : Chih-En Kuo, Sheng-Fu Liang, Yi-Chieh Lee, Fu-Yin Cherng, Wen-Chieh Lin, Peng-Yu Chen, Yen-Chen Liu, Fu-Zen Shaw

Published in: Physiological Computing Systems

Publisher: Springer Berlin Heidelberg

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Human beings spend approximately one third of their lives sleeping. Conventionally, to evaluate a subjects sleep quality, all-night polysomnogram (PSG) readings are taken and scored by a well-trained expert. Unlike a bulky PSG or EEG recorder on the head, the development of an electrooculogram (EOG)-based automatic sleep-staging system will enable physiological computing systems (PhyCS) to progress toward easy sleep and comfortable monitoring. In this paper, an EOG-based sleep scoring system is proposed. EOG signals are also coupling some of sleep characteristics of EEG signals. Compared to PSG or EEG recordings, EOG has the advantage of easy placement, and can be operated by the user individually at home. The proposed method was found to be more than 83 % accurate when compared with the manual scorings applied to sixteen subjects. In addition to sleep-quality evaluation, the proposed system encompasses adaptive brightness control of light according to online monitoring of the users sleep stages. The experiments show that the EOG-based sleep scoring system is a practicable solution for homecare and sleep monitoring due to the advantages of comfortable recording and accurate sleep staging.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Paper-Based Inkjet Electrodes
next chapter Upper Body Joint Angle Measurements for Physical Rehabilitation Using Visual Feedback
Literature
1.
Aliakseyeu, D., Du, J., Zwartkruis-Pelgrim, E., Subramanian, S.: Exploring interaction strategies in the context of sleep. In: Campos, P., Graham, N., Jorge, J., Nunes, N., Palanque, P., Winckler, M. (eds.) INTERACT 2011, Part III. LNCS, vol. 6948, pp. 19–36. Springer, Heidelberg (2011)CrossRef
2.
Bauer, J., Consolvo, S., Greenstein, B., Schooler, J., Wu, E., Watson, N.F., Kientz, J., Bauer, J.S.: Shuteye: encouraging awareness of healthy sleep recommendations with a mobile, peripheral display. In: Proceedings of the 2012 ACM Annual Conference on Human Factors in Computing Systems, pp. 1401–1410. ACM (2012)
3.
Berthomier, C., Drouot, X., Herman-Stoïca, M., Berthomier, P., Prado, J., Bokar-Thire, D., Benoit, O., Mattout, J., d’Ortho, M.P.: Automatic analysis of single-channel sleep eeg: validation in healthy individuals. Sleep 30(11), 1587 (2007)
4.
Bulling, A., Roggen, D., Tröster, G.: Wearable EOG goggles: eye-based interaction in everyday environments. ACM (2009)
5.
Chandra, H., Oakley, I., Silva, H.: Designing to support prescribed home exercises: understanding the needs of physiotherapy patients. In: Proceedings of the 7th Nordic Conference on Human-Computer Interaction: Making Sense Through Design, pp. 607–616. ACM (2012)
6.
Choe, E.K., Consolvo, S., Watson, N.F., Kientz, J.A.: Opportunities for computing technologies to support healthy sleep behaviors. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 3053–3062. ACM (2011)
7.
Costa, M., Goldberger, A.L., Peng, C.K.: Multiscale entropy analysis of biological signals. Phys. Rev. E 71(2), 021906 (2005)CrossRefMathSciNet
8.
Costa, M., Peng, C.K., Goldberger, A.L., Hausdorff, J.M.: Multiscale entropy analysis of human gait dynamics. Physica A Stat. Mech. Appl. 330(1), 53–60 (2003)CrossRefMATH
9.
Esteller, R., Echauz, J., Tcheng, T., Litt, B., Pless, B.: Line length: an efficient feature for seizure onset detection. In: Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2001, vol. 2, pp. 1707–1710. IEEE (2001)
10.
Gasio, F.P., Kräuchi, K., Cajochen, C., van Someren, E., Amrhein, I., Pache, M., Savaskan, E., Wirz-Justice, A.: Dawn-dusk simulation light therapy of disturbed circadian rest-activity cycles in demented elderly. Exp. Gerontol. 38(1), 207–216 (2003)CrossRef
11.
Fromm, E., Horlebein, C., Meergans, A., Niesner, M., Randler, C.: Evaluation of a dawn simulator in children and adolescents. Biol. Rhythm Res. 42(5), 417–425 (2011)CrossRef
12.
Giménez, M.C., Hessels, M., van de Werken, M., de Vries, B., Beersma, D.G., Gordijn, M.C.: Effects of artificial dawn on subjective ratings of sleep inertia and dim light melatonin onset. Chronobiol. Int. 27(6), 1219–1241 (2010)CrossRef
13.
Guo, L., Rivero, D., Dorado, J., Rabunal, J.R., Pazos, A.: Automatic epileptic seizure detection in eegs based on line length feature and artificial neural networks. J. Neurosci. Methods 191(1), 101–109 (2010)CrossRef
14.
Iber, C.: The aasm manual for the scoring of sleep and associated events: rules, terminology and technical specifications (2007)
15.
Kang, X., Jia, X., Geocadin, R.G., Thakor, N.V., Maybhate, A.: Multiscale entropy analysis of eeg for assessment of post-cardiac arrest neurological recovery under hypothermia in rats. IEEE Trans. Biomed. Eng. 56(4), 1023–1031 (2009)CrossRef
16.
Kay, M., Choe, E.K., Shepherd, J., Greenstein, B., Watson, N., Consolvo, S., Kientz, J.A.: Lullaby: a capture & access system for understanding the sleep environment. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 226–234. ACM (2012)
17.
Kuo, B.C., Landgrebe, D.A.: Nonparametric weighted feature extraction for classification. IEEE Trans. Geosci. Remote Sens. 42(5), 1096–1105 (2004)CrossRef
18.
Kupfer, D.J., Reynolds, C.F.: Management of insomnia. N. Engl. J. Med. 336(5), 341–346 (1997)CrossRef
19.
Lawson, S., Jamison-Powell, S., Garbett, A., Linehan, C., Kucharczyk, E., Verbaan, S., Rowland, D.A., Morgan, K.: Validating a mobile phone application for the everyday, unobtrusive, objective measurement of sleep. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2497–2506. ACM (2013)
20.
Liang, S.F., Kuo, C.E., Hu, Y.H., Pan, Y.H., Wang, Y.H.: Automatic stage scoring of single-channel sleep eeg by using multiscale entropy and autoregressive models. IEEE Trans. Instrum. Meas. 61(6), 1649–1657 (2012)CrossRef
21.
Lin, C.T., Ken-Li, L., Li-Wei, K., Sheng-Fu, L., Bor-Chen, K., et al.: Nonparametric single-trial eeg feature extraction and classification of driver’s cognitive responses. EURASIP J. Adv. Signal Process. 2008, 1–11 (2008)CrossRef
22.
Manabe, H., Fukumoto, M.: Full-time wearable headphone-type gaze detector. In: CHI’06 Extended Abstracts on Human Factors in Computing Systems, pp. 1073–1078. ACM (2006)
23.
Norman, R.G., Pal, I., Stewart, C., Walsleben, J.A., Rapoport, D.M.: Interobserver agreement among sleep scorers from different centers in a large dataset. Sleep 23(7), 901–908 (2000)
24.
Norris, P.R., Anderson, S.M., Jenkins, J.M., Williams, A.E., Morris Jr., J.A.: Heart rate multiscale entropy at three hours predicts hospital mortality in 3,154 trauma patients. Shock 30(1), 17–22 (2008)CrossRef
25.
Olbrich, E., Achermann, P., Meier, P.: Dynamics of human sleep eeg. Neurocomputing 52, 857–862 (2003)CrossRef
26.
Pardey, J., Roberts, S., Tarassenko, L.: A review of parametric modelling techniques for eeg analysis. Med. Eng. Phys. 18(1), 2–11 (1996)CrossRef
27.
Pincus, S.: Approximate entropy (apen) as a complexity measure. Chaos Interdisc. J. Nonlinear Sci. 5(1), 110–117 (1995)CrossRefMathSciNet
28.
Rechtschaffen, A., Kales, A.: A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects (1968)
29.
Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circulatory Physiol. 278(6), H2039–H2049 (2000)
30.
Rosenberg, R.S., Van Hout, S., et al.: The american academy of sleep medicine inter-scorer reliability program: sleep stage scoring. J. Clin. Sleep Med. 9(1), 81–87 (2013). JCSM: Official Publication of the American Academy of Sleep Medicine
31.
Schaltenbrand, N., Lengelle, R., Toussaint, M., Luthringer, R., Carelli, G., Jacqmin, A., Lainey, E., Muzet, A., Macher, J.P., et al.: Sleep stage scoring using the neural network model: comparison between visual and automatic analysis in normal subjects and patients. Sleep 19(1), 26 (1996)
32.
Silva, H., Palma, S., Gamboa, H.: Aal+: Continuous institutional and home care through wireless biosignal monitoring systems. In: Bos, L., Dumay, A., Goldschmidt, L., Verhenneman, G., Yogesan, K. (eds.) Handbook of Digital Homecare, pp. 115–142. Springer, Heidelberg (2011)CrossRef
33.
Stepanski, E.J., Wyatt, J.K.: Use of sleep hygiene in the treatment of insomnia. Sleep Med. Rev. 7(3), 215–225 (2003)CrossRef
34.
Takahashi, T., Cho, R.Y., Murata, T., Mizuno, T., Kikuchi, M., Mizukami, K., Kosaka, H., Takahashi, K., Wada, Y.: Age-related variation in eeg complexity to photic stimulation: a multiscale entropy analysis. Clin. Neurophysiol. 120(3), 476–483 (2009)CrossRef
35.
Thakor, N.V., Tong, S.: Advances in quantitative electroencephalogram analysis methods. Annu. Rev. Biomed. Eng. 6, 453–495 (2004)CrossRef
36.
Virkkala, J., Hasan, J., Värri, A., Himanen, S.L., Müller, K.: Automatic sleep stage classification using two-channel electrooculography. J. Neurosci. Methods 166(1), 109–115 (2007)CrossRef
Metadata
Title
An EOG-Based Automatic Sleep Scoring System and Its Related Application in Sleep Environmental Control
Authors
Chih-En Kuo
Sheng-Fu Liang
Yi-Chieh Lee
Fu-Yin Cherng
Wen-Chieh Lin
Peng-Yu Chen
Yen-Chen Liu
Fu-Zen Shaw
Copyright Year
2014
Publisher
Springer Berlin Heidelberg
Book
Physiological Computing Systems

Print ISBN: 978-3-662-45685-9

Electronic ISBN: 978-3-662-45686-6

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-662-45686-6_5

Premium Partner

    Image Credits