Probabilistic neural networks employing Lyapunov exponents for analysis of Doppler ultrasound signals.
The implementation of probabilistic neural networks (PNNs) with the Lyapunov exponents for Doppler ultrasound signals classification is presented. This study is directly based on the consideration that Doppler ultrasound signals are chaotic signals. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Decision making was performed in two stages: computation of Lyapunov exponents as representative features of the Doppler ultrasound signals and classification using the PNNs trained on the extracted features. The present research demonstrated that the Lyapunov exponents are the features which well represent the Doppler ultrasound signals and the PNNs trained on these features achieved high classification accuracies.
Ubeyli
Elif Derya
ED
Department of Electrical and Electronics Engineering, TOBB Ekonomi ve Teknoloji Universitesi, 06530 Sögütözü, Ankara, Turkey. edubeyli@etu.edu.tr
D000465:Algorithms; D001157:Arterial Occlusive Diseases / Q000000981:diagnostic imaging; D001528:Behcet Syndrome / Q000000981:diagnostic imaging; D002339:Carotid Arteries / Q000000981:diagnostic imaging; D002340:Carotid Artery Diseases / Q000000981:diagnostic imaging; D016893:Carotid Stenosis / Q000000981:diagnostic imaging; D003251:Constriction, Pathologic / Q000000981:diagnostic imaging; D005128:Eye Diseases / Q000000981:diagnostic imaging; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D009880:Ophthalmic Artery / Q000000981:diagnostic imaging; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D018608:Ultrasonography, Doppler / Q000379:methods*; D014652:Vascular Diseases / Q000000981:diagnostic imaging*