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Polysomnographic pattern recognition for automated classification of sleep-waking states in infants

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Abstract

A robust, automated pattern recognition system for polysomnography data targeted to the sleep-waking state and stage identification is presented. Five patterns were searched for: slow-delta and theta wave predominance in the background electro-encephalogram (EEG) activity; presence of sleep spindles in the EEG; presence of rapid eye movements in an electro-oculogram; and presence of muscle tone in an electromyogram. The performance of the automated system was measured indirectly by evaluating sleep staging, based on the experts' accepted methodology, to relate the detected patterns in infants over four months of post-term age. The set of sleep-waking classes included wakefulness, REM sleep and non-REM sleep stages I, II, and III–IV. Several noise and artifact rejection methods were implemented, including filters, fuzzy quality indices, windows of variable sizes and detectors of limb movements and wakefulness. Eleven polysomnographic recordings of healthy infants were studied. The ages of the subjects ranged from 6 to 13 months old. Six recordings counting 2665 epochs were included in the training set. Results on a test set (2369 epochs from five recordings) show an overall agreement of 87.7% (kappa 0.840) between the automated system and the human expert. These results show significant improvements compared with previous work.

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References

  • Anders, T., Emde, R., andParmelee, A. (Eds) (1971): ‘A manual of standardized terminology, techniques and criteria for scoring of states of sleep and wakefulness in newborn infants’ (UCLA Brain Information Service, NINDS, Neurological Information Network, Los Angeles, USA, 1971)

    Google Scholar 

  • Bankman, I. N., Sigillito, V. G., Wise, R. A., andSmith, P. L. (1992): ‘Feature-based detection of the K-complex wave in the human electroencephalogram using neural networks’,IEEE Trans. Biomed. Eng.,39, pp. 1305–1310

    Article  Google Scholar 

  • Besset, A. (1998): ‘L'analyse automatique du sommeil’, inBilliard, M. (Ed.): ‘Le sommeil normal et pathologique, 2nd edn’, (Masson, Paris, France, 1998), pp. 126–133

    Google Scholar 

  • Collura, T. F., Jacobs, E. C., Braun, D. S., andBurgess, R. C. (1993): ‘EVIEW—A workstation-based viewer for intensive clinical electroencephalography’,IEEE Trans. Biomed. Eng.,40, pp. 736–744

    Article  Google Scholar 

  • Cook, R. J. (1998): ‘Kappa’, inArmitage, P., andColton, T. (Eds): ‘The encyclopedia of biostatistics’ (Wiley, New York, USA, 1998) pp. 2160–2166

    Google Scholar 

  • Crowell, D., Brooks, L. J., Colton, T., Corwin, M. J., Hoppenbrouwers, T., Hunt, C. E., Kapuniai, L. E., Lister, G., Neuman, M. R., Peucker, M., Davidson Ward, S. L., Weese-Mayer, D. E., Willinger, M., andCollaborative Home Infant Monitoring Evaluation (CHIME) Steering Committee (1997): ‘Infant polysomnography: reliability’,Sleep,20, pp. 553–560

    Google Scholar 

  • Curzi-Dascalova, L., andMirmiran, M. (1996): ‘Manual of methods of recording and analyzing sleep-wakefulness states in premature and full-term infants’ (INSERM Publ., Paris, France, 1996)

    Google Scholar 

  • Estévez, P. A., Fernández, M. E., Held, C. M., Holzmann, C. A., Perez, C. A., andPérez, J. P. (2000): ‘Classification of sleep-waking states using modular neural networks’. IEEE International Conference on Systems, Man and Cybernetics, Nashville, Tennessee, USA, 2580–2585

  • Ferber, R., andKryger, M. (1995): ‘Principles and practice of sleep medicine in the child’ (Saunders, Philadelphia, USA, 1995)

    Google Scholar 

  • Fleiss, J. (1981): ‘Statistical methods for rates and proportions, 2nd edn’ (John Wiley, New York, USA, 1981)

    Google Scholar 

  • Goldman, L. R. (1992): ‘The kappa statistic — in reply’,JAMA,268, pp. 2513–2514

    Google Scholar 

  • Guilleminault, C., andSouquet, M. (1979): ‘Sleep states and related pathology’ inKorobkin, R., andGuilleminault, C. (Eds): ‘Advances in perinatal neurology, Vol. 1’, (Spectrum, New York, USA, 1979)

    Google Scholar 

  • Guilleminault, C. (1998): ‘Le sommeil normal de l'homme’, inBilliard, M. (Ed): ‘Le sommeil normal et pathologique, 2nd edn’ (Masson, Paris, France, 1998) pp. 3–11

    Google Scholar 

  • Harper, R. M., Schechtman, V. L., andKluge, K. A. (1987): ‘Machine classification of infant sleep state using cardiorespiratory measures’,Electroenceph. Clin. Neurophysiol.,67, pp. 379–387

    Article  Google Scholar 

  • Holzmann, C. A., Perez, J. P., Held, C. M., San Martín, M., Pizarro, F., Pérez, J. P., Garrido, M., andPeirano, P. (1999): ‘Expert-system classification of sleep-waking states in infants’,Med. Biol. Eng. Comput.,37, pp. 466–476

    Google Scholar 

  • Huupponen, E., Värri, A., Himanen, S. L., Hasan, J., Lehtokangas, M., andSaarinen, J. (2000): ‘Optimization of sigma amplitude threshold in sleep spindle detection’,J. Sleep Res.,9, pp. 327–334

    Google Scholar 

  • Jankel, W. R., andNiedermeyer, E. (1985): ‘Sleep spindles’,J. Clin. Neurophysiol.,2, pp. 1–35

    Google Scholar 

  • Jansen, B. H., andDawant, B. M. (1989): ‘Knowledge-based approach to sleep EEG analysis—A feasibility study’,IEEE Trans. Biomed. Eng.,36, pp. 510–518

    Article  Google Scholar 

  • Klösch, G., Kemp, B., Penzel, T., Schlögl, A., Rappelsberger, P., Trenker, E., Gruber, G., Zeitlhofer, J., Saletu, B., Herrmann, W. M., Himanen, S. L., Kunz, D., Barbanoj, M. J., Röschke, J., Värri, A., andDorffner, G. (2001): ‘The SIESTA project polygraphic and clinical database’IEEE Eng. Med. Biol. Mag.,20, pp. 51–57

    Google Scholar 

  • Kubicki, St., Höller, L., Berg, I., Pastelak-Price, C., andDorow, R. (1989): ‘Sleep EEG evaluation: A comparison of results by visual scoring and automatic analysis with the Oxford sleep stager’,Sleep,12, pp. 140–149

    Google Scholar 

  • Martin-baranera, M., Sancho, J. J., andSanz, F. (2000): ‘Controlling for chance agreement in the validation of medical expert systems with no gold standard: PNEUMON-IA and RENOIR revisited’,Comput. Biomed. Res.,33, pp. 380–397

    Google Scholar 

  • Ohayon, M. M., Guilleminault, C., Zulley, J., Palombini, L., andRaab, H. (1999): ‘Validation of the sleep-EVAL system against clinical assessments of sleep disorders and polysomnographic data’,Sleep,22, pp. 925–930

    Google Scholar 

  • Park, S., Principe, J. C., Smith, J. R., andReid, S. A. (1990): ‘TDAT — Time domain analysis tool for EEG analysis’,IEEE Trans. Biomed. Eng.,37, pp. 803–811

    Article  Google Scholar 

  • Park, H. J., Oh, J. S., Jeong, D. U., andPark, K. S. (2000): ‘Automated sleep stage scoring using hybrid rule- and case-based reasoning’,Comput. Biomed. Res.,33, pp. 330–349

    Article  Google Scholar 

  • Peirano, P., Curzi-Dascalova, L., andKorn, G. (1986): ‘Influence of sleep state and age on body motility in normal premature and full-term neonates’,Neuropediatrics,17, pp. 186–190

    Google Scholar 

  • Peirano, P., Fagioli, I., Singh, B. B., andSalzarulo, P. (1989): ‘Effect of early human malnutrition on waking and sleep organization’,Early Hum. Dev.,20, pp. 67–76

    Article  Google Scholar 

  • Peirano, P., andCurzi-Dascalova, L. (1995): ‘Motor activity patterns modulation and sleep states in low-risk prematurely born infants reaching normal term: A comparison with full-term new-borns’,Neuropediatrics,26, pp. 8–13

    Google Scholar 

  • Principe, J. C., Gala, S. K., andChang, T. G. (1989): ‘Sleep staging automaton based on the theory of evidence’,IEEE Trans. Biomed. Eng.,36, pp. 503–509

    Article  Google Scholar 

  • Proakis, J. G., andManolakis, D. G. (1996): ‘Digital signal processing: principles, algorithms and applications, 3rd edn’ (Prentice Hall, New Jersey, USA, 1996)

    Google Scholar 

  • Rechtschaffen, A., andKales, A. (1968): ‘A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects’ (UCLA, Brain Research Institute/ Brain Information Service, Los Angeles, USA, 1968)

    Google Scholar 

  • Roberts, S., andTarassenko, L. (1992): ‘Analysis of the sleep EEG using a multilayer network with spatial organization’,IEE Proc. F,139, pp. 420–425

    Google Scholar 

  • Scher, M. S. (1997): ‘Computer classification of state in healthy preterm neonates’,Sleep,20, pp. 132–141

    Google Scholar 

  • Shimada, T., Shina, T., andSaito, Y. (2000): ‘Detection of characteristic waves of sleep EEG by neural network analysis’,IEEE Trans. Biomed. Eng.,47, pp. 369–378

    Article  Google Scholar 

  • Smith, J. R. (1986): ‘Automated analysis of sleep EEG data’, inLopes De Silva, F. H., Storm van Leeuwen, W., andRémond, A. (Eds): ‘Handbook of electroencephalography and clinical neurophysiology, Vol. 2’ (Elsevier Publ., 1986) pp. 131–147

  • Tafti, M. (1998): ‘Analyse numèrique du sommeil’, inBilliard, M. (Ed.): ‘Le sommeil normal et pathologique, 2nd edn’ (Masson, Paris, France, 1998)

    Google Scholar 

  • White, D. P., andGibb, T. J. (1998): ‘Evaluation of a computarized polysomnographic system’,Sleep,21, pp. 188–196

    Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, Universidad de Chile, Santiago, Chile

    P. A. Estévez, C. M. Held, C. A. Holzmann, C. A. Perez, J. P. Pérez & J. Heiss

  2. INTA, Universidad de Chile, Santiago, Chile

    M. Garrido & P. Peirano

Authors
  1. P. A. Estévez
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  2. C. M. Held
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  3. C. A. Holzmann
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  4. C. A. Perez
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  5. J. P. Pérez
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  6. J. Heiss
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  7. M. Garrido
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  8. P. Peirano
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Correspondence to P. A. Estévez.

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Estévez, P.A., Held, C.M., Holzmann, C.A. et al. Polysomnographic pattern recognition for automated classification of sleep-waking states in infants. Med. Biol. Eng. Comput. 40, 105–113 (2002). https://doi.org/10.1007/BF02347703

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  • DOI: https://doi.org/10.1007/BF02347703

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