Abstract
This work presents a new system to diagnose the syndrome of obstructive sleep apnea (OSA) that includes a specific block for the removal of Electrocardiogram (ECG) artifacts and the R wave detection. The system is modeled by ECG cepstral coefficients. The final decision is done with two different approaches. The first one is based on Hidden Markov Model (HMM), as classifier. On the other hand, another classification system is based on Support Vector Machines, being the parameterization based on the transformation of HMM by a kernel. Our results reached up to 98.67%.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Baumel, M.J., Maislin, G., Pack, A.I.: Population and occupational screening for obstructive sleep apnea: are we there yet? American Journal of Respiratory and Critical Care Medicine 155(1), 9–14 (1997)
Guilleminault, C., Hoed, J., Mitler, M.N.: Clinical overview of the sleep apnea syndromes. Eds Sleep Apnea Syndromes, New York (1985)
Kimura, H., Talsumi, K., Masuyama, S., Kuriyama, T.: Diagnosis and treatment of sleep apnea syndrome in Japan comparison with other countries. Nippon-Kyobu-Shikkan-Gakkai-Zasshi (1995)
Moody, G.B., Mark, R.G., Goldberger, A., Penzel, T.: Stimulating rapid research advances via focused competition: the Computers in Cardiology Challenge 2000. Computers in Cardiology, 207–210 (2000)
Shinar, Z., Baharav, A., Akselrod, S.: Obstructive sleep apnea detection based on electrocardiogram analysis. Computers in Cardiology, 757–760 (2000)
McNames, J.N., Fraser, A.M.: Obstructive sleep apnea classification based on spectrogram patterns in the electrocardiogram. Computers in Cardiology, 749–752 (2000)
Drinnan, M., Allen, J., Langley, P., Murray, A.: Detection of sleep apnoea from frequency analysis of heart rate variability. Computers in Cardiology, 259–262 (2000)
de Chazal, P., Heneghan, C., Sheridan, E., Reilly, R., Nolan, P., O’Malley, M.: Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnea. IEEE Transactions on Biomedical Engineering 50(6), 686–696 (2003)
Quiceno-Manrique, A.F., Alonso-Hernandez, J.B., Travieso-Gonzalez, C.M., Ferrer-Ballester, M.A., Castellanos-Dominguez, G.: Detection of obstructive sleep apnea in ECG recordings using time-frequency distributions and dynamic features. In: Annual International Conference of the IEEE Engineering in Medicine and Biology EMBC 2009, pp. 5559–5562 (2009)
Knight, B., Pelosi, F., Michaud, G., Strickberger, S., Morady, F.: Brief Report: Clinical Consequences of Electrocardiographic Artifact Mimicking Ventricular Tachycardia. The New England Journal of Medicine 341(17), 1249–1255 (1999)
Benesty, J., Sondhi, M.M., Huang, Y.: Handbook of Speech Processing. Springer, Heidelberg (2008)
Rabiner, L., Juang, B.H.: Fundamentals of Speech Recognition. Prentice Hall (1993)
Rabiner, L.R.: A tutorial on Hidden Markov models and Selected Applications in Speech Recognition. Proceedings of the IEEE 77(2), 257–286 (1989)
Jaakkola, T., Diekhans, M., Haussler, D.: A discriminative framework for detecting remote protein homologies (1998), http://www.cse.ucsc.edu/research/compbio/research.html (visited on June 14, 2011)
Bin, Z., Yong, L., Shao-Wei, X.: Support vector machine and its application in handwritten numeral recognition. In: Proceedings of the 15th International Conference on Pattern Recognition, vol. 2, pp. 720–723 (2000)
Penzel, T., Moody, G.B., Mark, R.G., Goldberger, A.L., Peter, J.H.: The apnea-ecg database. Proc. Computers in Cardiology, 255–258 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Travieso, C.M., Alonso, J.B., Ticay-Rivas, J.R., del Pozo-Baños, M. (2011). Apnea Detection Based on Hidden Markov Model Kernel. In: Travieso-González, C.M., Alonso-Hernández, J.B. (eds) Advances in Nonlinear Speech Processing. NOLISP 2011. Lecture Notes in Computer Science(), vol 7015. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25020-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-25020-0_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25019-4
Online ISBN: 978-3-642-25020-0
eBook Packages: Computer ScienceComputer Science (R0)