Wavelet-based neural network analysis of ophthalmic artery Doppler signals.
In this study, ophthalmic artery Doppler signals were recorded from 115 subjects, 52 of whom had ophthalmic artery stenosis while the rest were healthy controls. Results were classified using a wavelet-based neural network. The wavelet-based neural network model, employing the multilayer perceptron, was used for analysis of ophthalmic artery Doppler signals. A multilayer perceptron neural network (MLPNN) trained with the Levenberg-Marquardt algorithm was used to detect stenosis in ophthalmic arteries. In order to determine the MLPNN inputs, spectral analysis of ophthalmic artery Doppler signals was performed using wavelet transform. The MLPNN was trained, cross validated, and tested with training, cross validation, and testing sets, respectively. All data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from ophthalmic artery stenosis. The correct classification rate was 97.22% for healthy subjects, and 96.77% for subjects having ophthalmic artery stenosis. The classification results showed that the MLPNN trained with the Levenberg-Marquardt algorithm was effective to detect ophthalmic artery stenosis.
Güler
Nihal Fatma
NF
Department of Electronics and Computer Education, Faculty of Technical Education, Gazi University, 06500 Teknikokullar, Ankara, Turkey. fnguler@gazi.edu.tr
Ubeyli
Elif Derya
ED
D000328:Adult; D000368:Aged; D000465:Algorithms; D016893:Carotid Stenosis / Q000000981:diagnostic imaging*; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D009880:Ophthalmic Artery / Q000000981:diagnostic imaging*; D012680:Sensitivity and Specificity; D018608:Ultrasonography, Doppler