Abstract
Sleep Apnea/Hypopnea Syndrome (SAHS) is a very common sleep disorder characterized by the repeated occurrence of involuntary breathing pauses during sleep. Cessation in breathing often causes Electroencephalographic (EEG) arousals as a response, and therefore detection of arousals is important since they provide important evidence for localization of apneic events and their number is directly related with SAHS severity. Arousals result in fragmented sleep and so they are one of the most important causes of daytime sleepiness. In this paper we present an approach to detect these arousals over polysomnographic recordings based on the machine learning paradigm. First a signal processing technique is proposed for the construction of learning patterns. Subsequently classifiers based on Fisher’s linear and quadratic discriminates, Support Vector Machines (SVM) and Artificial Neural Networks (ANN) are compared for the learning process. The more suitable model was chosen finally showing an accuracy of 0.92.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Guilleminault, C., Hoed, J., Milter, M.M.: Sleep apneas syndromes. Liss, New York (1978)
Iber, C., Ancoli-Israel, S., Chesson, A., Quan, S.F.: The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications, 1st edn. American Academy of Sleep Mecidine, Westchester (2007)
De Carli, F., Nobili, L., Gelcich, P., Ferrillo, F.: A Method for the Automatic Detection of Arousals During Sleep. Sleep 22(5), 561–572 (1999)
Cho, S.P., Lee, J., Park, H.D., Lee, K.J.: Detection of arousal in patients with respiratory sleep disorders using a single channel EEG. In: 27th IEEE Engineering in Medicine and Biology Annual Conference, pp. 2733–2735 (2005)
Cabrero-Canosa, M., Hernandez-Pereira, E., Moret-Bonillo, V.: Intelligent Diagnosis of Sleep Apnea Syndrome. IEEE Eng. Med. Biol. Mag. 23(2), 72–81 (2004)
Alvarez-Estevez, D., Moret-Bonillo, V.: Fuzzy Reasoning Used to Detect Apneic Events in the Sleep Apnea-Hypopnea Syndrome. Expert Systems with Applications 36, 7778–7785 (2009)
Michie, D., Spiegelhalter, D.J., Taylor, C.C.: Machine Learning: Neural and Statistical Classification. Ellis Horwood, Chichester (1994)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)
Moller, M.F.: A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning. Neural Networks 6, 525–533 (1993)
Hollander, M., Wolfe, D.A.: Nonparametric Statistical Methods. Wiley, Chichester (1973)
Toothaker, L.E.: Multiple Comparison Procedures. Sage Publications Inc., Thousand Oaks (1992)
Quan, S.F., Howard, B.V., Iber, C., Kiley, J.P., Nieto, F.J., O’Connor, G.T., Rapoport, D.M., Redline, S., Robbins, J., Samet, J.M., Wahl, P.W.: The Sleep Heart Health Sutdy: design, rationale and methods. Sleep 20(12), 1077–1085 (1997)
Pitson, D., Stradling, J.: Autonomic Markers of Arousal during Sleep in Patients Undergoing Investigation for Obstructive Sleep Apnoea, Their Relationship to EEG Arousals, Respiratory Events and Subjetive Sleepiness. J. Sleep Res. 7(1), 53–59 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Álvarez-Estévez, D., Moret-Bonillo, V. (2009). Model Comparison for the Detection of EEG Arousals in Sleep Apnea Patients. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds) Bio-Inspired Systems: Computational and Ambient Intelligence. IWANN 2009. Lecture Notes in Computer Science, vol 5517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02478-8_125
Download citation
DOI: https://doi.org/10.1007/978-3-642-02478-8_125
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02477-1
Online ISBN: 978-3-642-02478-8
eBook Packages: Computer ScienceComputer Science (R0)