TOWARDS UNOBTRUSIVE AUTOMATED SLEEP STAGE CLASSIFICATION - Polysomnography using Electrodes on the Face

Igor J. Berezhnoy, Gert-Jan de Vries, Tim Weysen, Jonce Dimov, Gary Garcia-Molina

Abstract

Although sleep stage annotation (SSA) is historically known from clinical practice and typically performed by a certified expert on the basis of visual examination of polysomnography (PSG) signals. Automatic SSA has emerged as a tool to assist sleep experts and to accelerate the analysis of PSG data. New advances in signal processing and sensor technology start to enable the application of SSA in home solutions as well. In today’s busy lives, sleep plays a central role and good quality sleep helps us to deal with the stress of everyday life. Being able to enhance sleep quality thus is a major opportunity to help people in reducing the influence of stress on their live, health and wellbeing. The advent of consumer products aimed at enhancing the sleep experience has propelled the need for home sleep monitoring and inducing solutions which can i) provide automatic SSA using sensors that interfere minimally with the sleep process and ii) provide sleep stage information in real-time in order to be suitable for closed-loop sleep inducing solutions. In this paper, we examine two possible alternatives for unobtrusive sleep monitoring. The first one uses respiratory, cardiac and wrist actigraphy signals while the second one relies on Facial PSG electrodes positioned on the facial area which allow for unobtrusive and comfortable sensors arrangements.

References

  1. Buysse, D., Reynolds-III, C., Monk, T., Berman, S., and Kupfer, D. (1989). Pittsburgh sleep quality index: A new instrument for psychiatric practice and research. Psychiatric Research, 28(2):193-213.
  2. Centre, N. N. R. (2002). Bibliography on the selforganizing maps (som) and learning vector quantization (lvq).
  3. Douglass, A., Bornstein, R., Ninomurcia, G., Keenan, S., Miles, L., and Zarcone, V. (1994). The Sleep Disorders Questionnaire-I - Creation and Multivariate Structure of SDQ. Sleep, 17(2):160-167.
  4. Kohonen, T. (1995). Learning vector quantization, The handbook of brain theory and neural networks. MIT Press, Cambridge, MA.
  5. Rechtschaffen, A. and A.Kales (1968). A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. U. S. National Institute of Neurological Diseases and Blindness, Neurological Information Network, Bethesda, Md.,.
  6. Seo, S. and Obermayer, K. (2003). Soft learning vector quantizatio. Neural Computation, 15:1589-1604.
  7. Virkkala, J. (2005). Automatic Sleep Stage Classification Using Electro-oculography. PhD thesis, Faculty of Computing and Electrical Engineering. Tampere University of Technology.
  8. Welch, P. (1967). The use of fast fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms. IEEE Transactions on Audio Electroacoustics, AU-15:7073.
  9. Witoelar, A. W., Ghosh, A., de Vries, J. J. G., Hammer, B., and Biehl, M. (2010). Window-based example selection in learning vector quantization. Neural Computation, 22(11):2924-2961.
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Paper Citation


in Harvard Style

J. Berezhnoy I., de Vries G., Weysen T., Dimov J. and Garcia-Molina G. (2012). TOWARDS UNOBTRUSIVE AUTOMATED SLEEP STAGE CLASSIFICATION - Polysomnography using Electrodes on the Face . In Proceedings of the International Conference on Health Informatics - Volume 1: BSSS, (BIOSTEC 2012) ISBN 978-989-8425-88-1, pages 487-492. DOI: 10.5220/0003890104870492


in Bibtex Style

@conference{bsss12,
author={Igor J. Berezhnoy and Gert-Jan de Vries and Tim Weysen and Jonce Dimov and Gary Garcia-Molina},
title={TOWARDS UNOBTRUSIVE AUTOMATED SLEEP STAGE CLASSIFICATION - Polysomnography using Electrodes on the Face},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: BSSS, (BIOSTEC 2012)},
year={2012},
pages={487-492},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003890104870492},
isbn={978-989-8425-88-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Health Informatics - Volume 1: BSSS, (BIOSTEC 2012)
TI - TOWARDS UNOBTRUSIVE AUTOMATED SLEEP STAGE CLASSIFICATION - Polysomnography using Electrodes on the Face
SN - 978-989-8425-88-1
AU - J. Berezhnoy I.
AU - de Vries G.
AU - Weysen T.
AU - Dimov J.
AU - Garcia-Molina G.
PY - 2012
SP - 487
EP - 492
DO - 10.5220/0003890104870492