Advertisement

Cepstrum Coefficients of the RR Series for the Detection of Obstructive Sleep Apnea Based on Different Classifiers

  • Antonio Ravelo-García
  • Juan L. Navarro-Mesa
  • Sofía Martín-González
  • Eduardo Hernández-Pérez
  • Pedro Quintana-Morales
  • Iván Guerra-Moreno
  • Javier Navarro-Esteva
  • Gabriel Juliá-Serdá
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8112)

Abstract

Two automatic statistical methods for the classification of the obstructive sleep apnoea syndrome based on the cepstrum coefficients of the RR series obtained from the Electrocardiogram (ECG) are presented. We study the effect of working with Linear Discriminant Analysis (LDA) and compare its performance with a reference detector based on Support Vector Machines (SVM). These classifications methods require two previous stages: preprocessing and feature extraction. Firstly, R instants are detected previous to the feature extraction phase thanks to a preprocessing over the ECG. Secondly, Cepstrum Coefficients over the RR signal is applied to extract the relevant characteristics specially those related to the system modelled by the filter-type elements concentrated in the low time lag region.

Keywords

Sleep apnea RR Series Cepstrum Linear Discriminant Analysis Support Vector Machines 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    Penzel, T., McNames, J., De Chazal, P., Raymond, B., Murray, A., Moody, G.: Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings. Medical & Biological Engineering & Computing 40, 402–407 (2002)CrossRefGoogle Scholar
  3. 3.
    Ravelo, A.G., Travieso, C.M., Lorenzo, F.D., Navarro, J.L., Martín, S., Alonso, J.B., Ferrer, M.A.: Application of Support Vector Machines and Gaussian Mixture Models for the Detection of Obstructive Sleep Apnea based on the RR Series. In: Proceedings of the 8th WSEAS International Conference on Applied Mathematics, pp. 139–143 (2005)Google Scholar
  4. 4.
    Ravelo, A.G., Navarro, J.L., Murillo, M.J., Juliá, G.: Application of RR Series and Oximetry to a Statistical Classifier for the Detection of Sleep Apnoea/Hipopnoea. In: CINC 2004, pp. 305–308 (2004)Google Scholar
  5. 5.
    La Rovere, M.T., Pinna, G.D., Maestri, R., Mortara, A., Capomolla, S., Febo, O., Ferrari, R., Franchini, M., Gnemmi, M., Opasich, C., Riccardi, P., Traversi, E., Corbelli, F.: Short-term heart variability predicts sudden cardiac death in chronic heart failure patients. Circulation 107, 565–570 (2003)CrossRefGoogle Scholar
  6. 6.
    Oppenheim, A.V., Schafer, R.W.: Discrete -Time Signal Processing. Prentice Hall (1989)Google Scholar
  7. 7.
    Cristianini, N., Shaew-Taylor, J.: An Introduction to Support Vector Machines and other Kernel-based Learning Methods. Cambridge University Press (2000)Google Scholar
  8. 8.

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Antonio Ravelo-García
    • 1
  • Juan L. Navarro-Mesa
    • 1
  • Sofía Martín-González
    • 1
  • Eduardo Hernández-Pérez
    • 1
  • Pedro Quintana-Morales
    • 1
  • Iván Guerra-Moreno
    • 1
  • Javier Navarro-Esteva
    • 2
  • Gabriel Juliá-Serdá
    • 2
  1. 1.Institute for Technological Development and Innovation in Communications (IDeTIC)Universidad de Las Palmas de Gran CanariaSpain
  2. 2.Unidad del Sueño, Hospital General de Gran Canaria Dr. NegrínSpain

Personalised recommendations