Apnea detection based on hidden Markov model Kernel

  • Authors:

  • Carlos M. Travieso;Jesús B. Alonso;Jaime R. Ticay-Rivas;Marcos Del Pozo-Baños

  • Affiliations:

  • Signals and Communications Department, Institute for Technological Development and Innovation in Communications, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain;Signals and Communications Department, Institute for Technological Development and Innovation in Communications, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain;Signals and Communications Department, Institute for Technological Development and Innovation in Communications, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain;Signals and Communications Department, Institute for Technological Development and Innovation in Communications, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

  • Venue:
  • NOLISP'11 Proceedings of the 5th international conference on Advances in nonlinear speech processing
  • Year:
  • 2011

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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%.