Classification of sleep apnea by using wavelet transform and artificial neural networks

TAĞLUK M. E., AKIN M., Sezgin N.

EXPERT SYSTEMS WITH APPLICATIONS, vol.37, no.2, pp.1600-1607, 2010 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 37 Issue: 2
  • Publication Date: 2010
  • Doi Number: 10.1016/j.eswa.2009.06.049
  • Journal Name: EXPERT SYSTEMS WITH APPLICATIONS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1600-1607
  • Inonu University Affiliated: Yes

Abstract

This paper describes a new method to classify sleep apnea syndrome (SAS) by using wavelet transforms and an artificial neural network (ANN) The network was trained and tested for different momentum coefficients. The abdominal respiration signals are separated into spectral components by using multi-resolution wavelet transforms. These spectral components are applied to the inputs of the artificial neural network. Then the neural network was configured to give three outputs to classify the SAS situation of the patient.