gms | German Medical Science

49. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds)
19. Jahrestagung der Schweizerischen Gesellschaft für Medizinische Informatik (SGMI)
Jahrestagung 2004 des Arbeitskreises Medizinische Informatik (ÖAKMI)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie
Schweizerische Gesellschaft für Medizinische Informatik (SGMI)

26. bis 30.09.2004, Innsbruck/Tirol

A Markov State Transition Model for the Prediction of Changes in Sleep Structure Induced by Aircraft Noise

Meeting Abstract (gmds2004)

Suche in Medline nach

  • corresponding author presenting/speaker Mathias Basner - Institut für Luft- und Raumfahrtmedizin, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Köln, Deutschland
  • Uwe Siebert - Institute fpr technology Assessment and Department of Radiology, Boston, USA

Kooperative Versorgung - Vernetzte Forschung - Ubiquitäre Information. 49. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds), 19. Jahrestagung der Schweizerischen Gesellschaft für Medizinische Informatik (SGMI) und Jahrestagung 2004 des Arbeitskreises Medizinische Informatik (ÖAKMI) der Österreichischen Computer Gesellschaft (OCG) und der Österreichischen Gesellschaft für Biomedizinische Technik (ÖGBMT). Innsbruck, 26.-30.09.2004. Düsseldorf, Köln: German Medical Science; 2004. Doc04gmds358

Die elektronische Version dieses Artikels ist vollständig und ist verfügbar unter: http://www.egms.de/de/meetings/gmds2004/04gmds358.shtml

Veröffentlicht: 14. September 2004

© 2004 Basner et al.
Dieser Artikel ist ein Open Access-Artikel und steht unter den Creative Commons Lizenzbedingungen (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.de). Er darf vervielf&aauml;ltigt, verbreitet und &oauml;ffentlich zug&aauml;nglich gemacht werden, vorausgesetzt dass Autor und Quelle genannt werden.


Gliederung

Text

Introduction

The demand for mobility has been strongly increasing over the past few years. As minimum intervals between two starting or landing planes are necessary for security reasons, evasion of air traffic to shoulder hours and even the nighttime has been observed in the past an will even increase in the future. Simultaneously, the strain of residents living in the vicinity of airports is likely to increase due to noise emitted from nocturnal air traffic.

By dividing polysomnographic recordings into intervals of 30 seconds, human sleep can be classified in six distinct states: Awake, stages 1-4, and REM [1]. The sleep states differ in their contribution to the restorative power of sleep. Environmental noise is a potential disruptor of the sleep process and may cause changes in the structure of sleep. Research effort in the past was restricted to the prediction of noise induced awakenings.

The goal of this study was to use a Markov model to predict changes in total sleep structure depending on the sound pressure level and time of occurrence of aircraft noise events (ANE).

Methods

In four laboratory studies with 112 subjects lasting from 1999 to 2003, the Institute of Aerospace Medicine of the German Aerospace Center (DLR) investigated the influence of aircraft noise on human sleep [2]. Data of 33,000 ANEs and related events were used to estimate transition probabilities between different sleep states as a function of the maximum sound pressure level of the ANE.

Results

A Markov state transition model was built to reproduce the natural sleep course of one night, that is, a noise-free baseline nights. The model was extended by a second branch representing a night with ANEs. This allows the user of the model to predict changes in sleep structure depending on the timing and maximum sound pressure level of ANEs. Internal and external validation showed good results with deviations less than 5%.

Discussion

The results show that, by the use of Markov processes, it was possible to extend predictions of noise induced awakenings by simple regression models to predictions of total sleep structure.

Our study has several limitations: (1) Prediction of changes in transition probabilities between different sleep states was confined to a limited time range. (2) The interaction of several successive ANEs as well as more complex countermeasures of the body in form of adaptation processes could not be taken into account.

In conclusion, the application of Markov models extends the prediction of noise induced awakenings to the prediction of more subtle changes in sleep structure, but some limitations have to be considered.


References

1.
Rechtschaffen, A, Kales, A, Berger, RJ, Dement, WC, Jacobsen, A, Johnson, LC, Jouvet, M, Monroe, LJ, Oswald, I, Roffwarg, HP, Roth, B, Walter, RD: A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. Washington, D.C., Public Health Service, U.S. Government, Printing Office, 1968.
2.
Basner M, Samel A: DLR Research on nocturnal aircraft noise effects. Noise & Health 2004, 6 (in press)