Energy based feature extraction for classification of sleep apnea syndrome

SEZGİN N., TAĞLUK M. E.

COMPUTERS IN BIOLOGY AND MEDICINE, cilt.39, sa.11, ss.1043-1050, 2009 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 39 Sayı: 11
  • Basım Tarihi: 2009
  • Doi Numarası: 10.1016/j.compbiomed.2009.08.005
  • Dergi Adı: COMPUTERS IN BIOLOGY AND MEDICINE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1043-1050
  • İnönü Üniversitesi Adresli: Evet

Özet

In this paper it is aimed to classify sleep apnea syndrome (SAS) by using discrete wavelet transforms (DWT) and an artificial neural network (ANN). The abdominal and thoracic respiration signals are separated into spectral components by using multi-resolution DWT. Then the energy of these spectral components are applied to the inputs of the ANN. The neural network was configured to give three outputs to classify the SAS situation of the subject.