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A Bayesian Neural Network Approach for Sleep Apnea Classification

  • Oscar Fontenla-Romero
  • Bertha Guijarro-Berdiñas
  • Amparo Alonso-Betanzos
  • Ana del Rocío Fraga-Iglesias
  • Vicente Moret-Bonillo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2780)

Abstract

In this paper a method for sleep apnea classification is proposed. The method is based on a feedforward neural network trained using a bayesian framework and a cross-entropy error function. The inputs of the neural network are the first level-5-detail coefficients obtained from a discrete wavelet transformation of the samples of the thoracic effort signal corresponding to the apnea. In order to train and validate the presented method, 120 events from 6 different patients were used. The true error rate was estimated using a 10-fold cross validation. The presented results were averaged over 100 different simulations and a multiple comparison procedure was used to model selection. The mean classification accuracy obtained over the test set was 83.78%±1.90.

Keywords

Sleep Apnea Discrete Wavelet Transformation Hide Neuron Radial Basis Function Neural Network Wavelet Decomposition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Oscar Fontenla-Romero
    • 1
  • Bertha Guijarro-Berdiñas
    • 1
  • Amparo Alonso-Betanzos
    • 1
  • Ana del Rocío Fraga-Iglesias
    • 1
  • Vicente Moret-Bonillo
    • 1
  1. 1.Department of Computer ScienceUniversity of A CoruñaA CoruñaSpain

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