Determining variability of ophthalmic arterial Doppler signals using Lyapunov exponents.
The new method presented in this study was directly based on the consideration that ophthalmic arterial Doppler signals are chaotic signals. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Multilayer perceptron neural network (MLPNN) architecture was formulated and used as a basis for determining variabilities such as stenosis, ocular Behcet disease, and uveitis disease in the physical state of ophthalmic arterial Doppler signals. The computed Lyapunov exponents of the ophthalmic arterial Doppler signals were used as inputs of the MLPNN. Receiver operating characteristic (ROC) curve was used to assess the performance of the detection process. The ophthalmic arterial Doppler signals were classified with the accuracy varying from 93.75% to 97.06%. The results confirmed that the proposed MLPNN trained with Levenberg-Marquardt algorithm has potential in detecting stenosis, Behcet disease and uveitis disease in ophthalmic arteries.
Ubeyli
Elif Derya
ED
Department of Electronics and Computer Education, Faculty of Technical Education, Gazi Univeresity, 06500 Teknikokullar, Ankara, Turkey.
Güler
Inan
I
D000328:Adult; D000368:Aged; D000465:Algorithms; D001528:Behcet Syndrome / Q000175:diagnosis / Q000000981:diagnostic imaging; D016022:Case-Control Studies; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012372:ROC Curve; D012161:Retinal Artery / Q000000981:diagnostic imaging*; D015356:Retinal Artery Occlusion / Q000175:diagnosis / Q000000981:diagnostic imaging; D018608:Ultrasonography, Doppler; D014605:Uveitis / Q000175:diagnosis / Q000000981:diagnostic imaging