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

Kara S., Dirgenali F.

EXPERT SYSTEMS WITH APPLICATIONS, vol.32, no.2, pp.632-640, 2007 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 32 Issue: 2
  • Publication Date: 2007
  • Doi Number: 10.1016/j.eswa.2006.01.043
  • Journal Name: EXPERT SYSTEMS WITH APPLICATIONS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.632-640
  • Keywords: atherosclerosis, carotid artery, wavelet transform, power spectral density, principal component analysis, artificial neural network, DOPPLER ULTRASOUND, ALGORITHMS, DISEASE, SPIKES
  • Erciyes University Affiliated: No

Abstract

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.