Abstract
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.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
American Psychiatric Association: Sleep wake disorders DSM-5 Selections. American Psychiatric Association (2015)
Caldwell, J.P.: Sleep: The Complete Guide to Sleep Disorders and a Better Night’s Sleep. Firefly Books (1997)
Hamet, P., Treablay, J.: Genetics of the sleep-wake cycle and its disorders. Metab. Clin. Exp. 55(Suppl 2), S7–S12 (2006)
Fairbanks, D.N.F., Fujita, S., Ikematsu, T.M.D., Simmons, F.B.: Snoring and Obstructive Aleep Apnea. Raven Press, New York (2016)
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 2008
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)
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)
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)
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)
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)
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)
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)
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)
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)
Acknowledgements
The presented research results were funded with the grant 02/21/DSPB/3513 allocated by the Ministry of Science and Higher Education in Poland.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Walczak, T., Ferduła, R., Michałowska, M., Grabski, J.K., Cofta, S. (2019). Estimation of Apnea-Hypopnea Index in Sleep Breathing Disorders with the Use of Artificial Neural Networks. In: Tkacz, E., Gzik, M., Paszenda, Z., Piętka, E. (eds) Innovations in Biomedical Engineering. IBE 2018. Advances in Intelligent Systems and Computing, vol 925. Springer, Cham. https://doi.org/10.1007/978-3-030-15472-1_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-15472-1_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-15471-4
Online ISBN: 978-3-030-15472-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)