Cognitive Computation

, Volume 5, Issue 4, pp 558–565

Automatic Apnea Identification by Transformation of the Cepstral Domain

  • Carlos M. Travieso
  • Jesús B. Alonso
  • Marcos del Pozo-Baños
  • Jaime R. Ticay-Rivas
  • Karmele Lopez-de-Ipiña
Article

DOI: 10.1007/s12559-012-9184-x

Cite this article as:
Travieso, C.M., Alonso, J.B., del Pozo-Baños, M. et al. Cogn Comput (2013) 5: 558. doi:10.1007/s12559-012-9184-x

Abstract

A new approach based on the transformation of the Cepstral domain is developed on this work. This approach reaches an automatic diagnosis for the syndrome of obstructive sleep apnea that includes a specific block for the removal of electrocardiogram (ECG) artifacts and the R wave detection. The system is modeled by a transformation of the Cepstral domain sequence using hidden Markov model (HMM). The final decision is done with two different approaches: one based on HMM as a classifier and a second one based on support vector machines classification and a parameterization based on the transformation of HMM by a kernel. The later approach reached results up to 99.23 %, using all test samples from Physionet Apnea-ECG Database.

Keywords

Automatic apnea detection Artifacts removal Hidden Markov model Kernel building Machine learning Pattern recognition Nonlinear processing 

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Carlos M. Travieso
    • 1
  • Jesús B. Alonso
    • 1
  • Marcos del Pozo-Baños
    • 1
  • Jaime R. Ticay-Rivas
    • 1
  • Karmele Lopez-de-Ipiña
    • 2
  1. 1.Signals and Communications Department, Institute for Technological Development and Innovation in Communications (IDETIC)University of Las Palmas de Gran CanariaLas Palmas de Gran CanariaSpain
  2. 2.System Engineering and Automation DepartmentUniversity of the Basque CountryDonostiaSpain

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