A system to diagnose atherosclerosis via wavelet transforms, principal component analysis and artificial neural networks

Kara S., Dirgenali F.

EXPERT SYSTEMS WITH APPLICATIONS, cilt.32, sa.2, ss.632-640, 2007 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 32 Sayı: 2
  • Basım Tarihi: 2007
  • Doi Numarası: 10.1016/j.eswa.2006.01.043
  • Dergi Adı: EXPERT SYSTEMS WITH APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.632-640
  • Anahtar Kelimeler: atherosclerosis, carotid artery, wavelet transform, power spectral density, principal component analysis, artificial neural network, DOPPLER ULTRASOUND, ALGORITHMS, DISEASE, SPIKES
  • Erciyes Üniversitesi Adresli: Hayır

Özet

In this study, Doppler ultrasound signals were acquired from carotid arteries of 82 patients with atherosclerosis and 95 healthy volunteers. We have employed discrete wave transform (DWT) of Doppler signals and power spectral density graphics of these decomposed signals using Welch method. After that, we have performed Principal component analysis (PCA) for data reduction and ANN in order to distinguish between atherosclerosis and healthy subjects.