Estimation of Apnea-Hypopnea Index in Sleep Breathing Disorders with the Use of Artificial Neural Networks

  • Tomasz WalczakEmail author
  • Renata Ferduła
  • Martyna Michałowska
  • Jakub Krzysztof Grabski
  • Szczepan Cofta
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 925)


Sleep Apnea Syndrome (SAS) becomes an important medical and social problem of contemporary societies. It is burdensome, it can be dangerous to health and even cause of death. The most efficient way to detect this syndrome is polysomnography. It gives good results but it is expensive and not commonly available. Main aim of this study is to present another, easier and cheaper way to detect SAS. Proposed method is based on prediction of sleep state using only oximetry and heart rate. The Artificial Neural Network (ANN) algorithm to predict time series was introduced. These networks were used to detect apneas and hypopneas to support diagnose of SAS and to detect whether patient sleeps or not. All data needed to train and test ANN were collected in sleep laboratory for a group of five considered patients with diagnosed SAS. The presented in this work results show that it is possible to predict apneas during sleep with high rate of accuracy, just with use of information about heart rate and blood oxygen saturation. It means that presented method could be effective to diagnose this disease using only simple device with implemented ANN.


Sleep apnea Artificial neural network Polysomnography 



The presented research results were funded with the grant 02/21/DSPB/3513 allocated by the Ministry of Science and Higher Education in Poland.


  1. 1.
    American Psychiatric Association: Sleep wake disorders DSM-5 Selections. American Psychiatric Association (2015)Google Scholar
  2. 2.
    Caldwell, J.P.: Sleep: The Complete Guide to Sleep Disorders and a Better Night’s Sleep. Firefly Books (1997)Google Scholar
  3. 3.
    Hamet, P., Treablay, J.: Genetics of the sleep-wake cycle and its disorders. Metab. Clin. Exp. 55(Suppl 2), S7–S12 (2006)CrossRefGoogle Scholar
  4. 4.
    Fairbanks, D.N.F., Fujita, S., Ikematsu, T.M.D., Simmons, F.B.: Snoring and Obstructive Aleep Apnea. Raven Press, New York (2016)Google Scholar
  5. 5.
    Department of Health and Human Services, Center for Medicare and Medicaid Services: Decision Memo for Continuous Positive Airway Pressure (CPAP) Therapy for Obstructive Sleep Apnea (OSA). CAG-0093R, 13 March 2008Google Scholar
  6. 6.
    Bianchi, M.T., Goparaju, B.: Potential underestimation of sleep apnea severity by at-home kits: rescoring in-laboratory polysomnography without sleep stagings. J. Clin. Sleep Med. 13, 551–555 (2017)CrossRefGoogle Scholar
  7. 7.
    Flemons, W.W., Douglas, N.J., Kuna, S.T., et al.: Access to diagnosis and treatment of patients with suspected sleep apnea. Am. J. Respirat. Crit. Care Med. 169, 668–672 (2004)CrossRefGoogle Scholar
  8. 8.
    Kapur, V.K., Auckley, D.H., Chowdhuri, S., et al.: Clinical practice guideline for diagnostic testing for adult obstructive sleep apnea: an american academy of sleep medicine clinical practice guideline. J. Clin. Sleep Med. 13, 479–504 (2017)CrossRefGoogle Scholar
  9. 9.
    Collop, N.A., McDowell Anderson, W., Boehlecke, B., et al.: Clinical guidelines for the use of unattended portable monitors in the diagnosis of obstructive sleep apnea in adult patients. J. Clin. Sleep Med. 3, 737–747 (2007)Google Scholar
  10. 10.
    Epstein, L.J., Kristo, D., Strollo, P.J., et al.: Clinical guideline for the evaluation, management and long-term care of obstructive sleep apnea in adults. J. Clin. Sleep Med. 5, 263–276 (2009)Google Scholar
  11. 11.
    Tadeusiewicz, R., Korbicz, J., Rutkowski, L., Duch, W.: Sieci neuronowe w inzynierii biomedycznej, Tom 9. (ang. Neural Networks in Biomedical Engineering, vol. 9). Akademicka Oficyna Wydawnicza EXIT, Warszawa (2013)Google Scholar
  12. 12.
    Michałowska, M., Walczak, T., Grabski, J. K., Grygorowicz, M.: Artificial neural networks in knee injury risk evaluation among professional football players. In: AIP Conference Proceedings, Lublin, p. 70002 (2018)Google Scholar
  13. 13.
    Walczak, T., Grabski, J., Grajewska, M., Michałowska, M.: Application of artificial neural networks in man’s gait recognition. In: Advances in Mechanics: Theoretical, Computational and Interdisciplinary Issues, pp. 591–594. CRC Press (2016)Google Scholar
  14. 14.
    Grabski, J.K., Walczak, T., Michałowska, M., Cieslak, M.: Gender recognition using artificial neural networks and data coming from force plates. In: Gzik, M., et al. (eds.) Innovations in Biomedical Engineering, IBE 2017. Advances in Intelligent Systems and Computing, vol. 623, pp. 53–60. Springer, Cham (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Tomasz Walczak
    • 1
    Email author
  • Renata Ferduła
    • 1
  • Martyna Michałowska
    • 1
  • Jakub Krzysztof Grabski
    • 1
  • Szczepan Cofta
    • 2
  1. 1.Institute of Applied Mechanics, Faculty of Mechanical Engineering and ManagementPoznań University of TechnologyPoznańPoland
  2. 2.Department of Pulmonology, Allergology and Respiratory OncologyPoznań University of Medical SciencesPoznańPoland

Personalised recommendations