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Automatic Apnea Identification by Transformation of the Cepstral Domain

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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.

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Acknowledgments

This work was supported by funds from “Cátedra Telefónica 2009/10—ULPGC,” in Spain, under the reference ARUCAS.

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Authors and Affiliations

  1. Signals and Communications Department, Institute for Technological Development and Innovation in Communications (IDETIC), University of Las Palmas de Gran Canaria, Campus de Universitario Tafira s/n, 35017, Las Palmas de Gran Canaria, Spain

    Carlos M. Travieso, Jesús B. Alonso, Marcos del Pozo-Baños & Jaime R. Ticay-Rivas

  2. System Engineering and Automation Department, University of the Basque Country, 20008, Donostia, Spain

    Karmele Lopez-de-Ipiña

Authors
  1. Carlos M. Travieso
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  2. Jesús B. Alonso
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  3. Marcos del Pozo-Baños
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  4. Jaime R. Ticay-Rivas
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  5. Karmele Lopez-de-Ipiña
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Correspondence to Carlos M. Travieso.

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Travieso, C.M., Alonso, J.B., del Pozo-Baños, M. et al. Automatic Apnea Identification by Transformation of the Cepstral Domain. Cogn Comput 5, 558–565 (2013). https://doi.org/10.1007/s12559-012-9184-x

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