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New method of automated sleep quantification

  • Computing and Data Processing
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Abstract

Since its discovery some 50 years ago, the electro-encephalogram (EEG) has formed the basis for classification of sleep into several stages, either laboriously performed by visual examination of the EEG and related signals or, more recently, by automated techniques. Both visual scoring and most automated analyses are highly subjective and rely on application of a predefined set of rules. A method of analysing the EEG which requires no such application of rules and aims to give some indication of the dynamics of sleep in humans is proposed in the paper.

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References

  • Barlow, J. S. (1985) Methods of analysis of nonstationary EEGs, with emphasis on segmentation techniques: a comparative review.J. Clin. Neurophysiol.,2, (3), 267–304.

    Article  Google Scholar 

  • Bodenstein, G. andPraetorius, H. M. (1975) Pattern recognition of the EEG by adaptive segmentation. Part 1: segmentation and feature extraction. Proc. of the 2nd Symp. of the Study Group for EEG methodology, 449–459.

  • Bohlin, T. (1971) Analysis of EEG signals with changing spectra. Technical Report 18.212, IBM Nordic Lab, Sweden.

    Google Scholar 

  • Box, G. E. P. andJenkins, G. M. (1976)Time series analysis: forecasting and control. Holden-Day.

  • Broomhead, D. S., Lowe, D. andWebb, A. R. (1989) A sum rule satisfied by optimised feed-forward layered networks. RSRE Memo. 4341, Malvern, 1989.

  • Cohen, B. A. andSances, A. (1977) Stationarity of the human electroencephalogram.Med. & Biol. Eng. & Comp.,15, 513–518.

    Google Scholar 

  • Cohen, B. A. (1986)Biomedical signal processing. CRC Press, Boca Raton, Florida, USA.

    Google Scholar 

  • Gath, I. andBar-On, E. (1980) Computerized method for scoring of polygraphic sleep recordings.Comp. Prog. Biomed,11, 217–223.

    Article  Google Scholar 

  • Hasan, J. (1983) Differentiation of normal and disturbed sleep by automatic analysis.Acta Physiol. Scand, supplementum 1983,526, 1–103.

    Google Scholar 

  • Hasman, A., Jansen, B. H., Landeweerd, G. H. andvon Blokland-Vogel-Sang, A. W. (1978) Demonstration of segmentation techniques for EEG records.Int. J. Biomed. Comput.,9, 311–321.

    Article  Google Scholar 

  • Jansen, B. H., Hasman, A., Lenten, R. andVisser, S. L. (1979) A study of inter- and intraindividual variability of the EEG of 16 normal subjects by means of segmentation. Amsterdam, 2nd European congress on EEG and Neurophysiology, Elsevier, 617–628.

  • Jansen, B. H. (1979) EEG segmentation and classification: an explorative study. PhD thesis, Free University of Amsterdam.

  • Jansen, B. H., Bourne, J. R. andWard, J. W. (1981) Autoregressive estimation of short segment spectra for computerized EEG analysis.IEEE Trans.,BME-28, 630–638.

    Google Scholar 

  • Kalman, R. E., (1960) A new approach to linear filtering and prediction problems.Trans. of Am. Soc. Mech. Eng. (Series D),82, 35–34.

    Google Scholar 

  • Kalman, R. E. andBucy, R. S. (1961) New results in linear filtering and prediction theory.Trans. of Am. Soc. Mech. Eng. (Series D),83, 95–108.

    MathSciNet  Google Scholar 

  • Kemp, B., Groneveld, E. W., Janssen, A. J. M. andFranzen, J. M. (1987) A model based monitor of human sleep stages.Biol. Cybern.,57, 365–378.

    Article  MATH  Google Scholar 

  • Kohonen, T. (1982) Self-organized formation of topographically correct feature maps.Biological Cybernetics,43, 59–69.

    Article  MATH  MathSciNet  Google Scholar 

  • Kohonen, T. (1990) The self-organizing map.Proc. IEEE,78, (9), 1464–1480.

    Article  Google Scholar 

  • Larson, M. J. andSchubert, B. O. (1979)Probabilistic models in engineering sciences II. Wiley, New York, Chichester, Brisbane, Toronto.

    Google Scholar 

  • Lippmann, R. P. (1987) An introduction to computing with neural nets.IEEE ASSP Magazine, 5–22.

  • Lowe, D. andWebb, A. R. (1990) Exploiting prior knowledge in network optimization: an illustration from medical prognosis.Network: Computation in Neural Systems,1, (3), 299–323.

    Article  Google Scholar 

  • Papoulis, A. (1984)Probability, random variables, and stochastic processes. McGraw-Hill.

  • Rechtschaffen, A. andKales, A. (1968) A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. Technical report, UCLA, Los Angeles, USA.

  • Roberts, S. (1990) Analysis and interpretation of the human sleep EEG. Internal Research Report, Medical Engineering Unit, University of Oxford.

  • Skagen, D. W. (1988) Estimation of running frequency spectra using a Kalman filter algorithm.J. of Biomed. Eng.,10, 275–279.

    Google Scholar 

  • Webb, W. B. andDrebelow, L. M. (1982) A modified method for scoring slow wave sleep of older subjects.Sleep, 5: 195–199.

    Google Scholar 

  • Widrow, B. andStearns, S. D. (1985)Adaptive signal processing. Prentice-Hall, New Jersey.

    MATH  Google Scholar 

  • Zadeh, L. A. (1965) Fuzzy sets.Information and Control,8, 338–353.

    Article  MATH  MathSciNet  Google Scholar 

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Roberts, S., Tarassenko, L. New method of automated sleep quantification. Med. Biol. Eng. Comput. 30, 509–517 (1992). https://doi.org/10.1007/BF02457830

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  • DOI: https://doi.org/10.1007/BF02457830

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