Detection of ophthalmic arterial doppler signals with Behcet disease using multilayer perceptron neural network.
Doppler ultrasound is known as a reliable technique, which demonstrates the flow characteristics and resistance of ophthalmic arteries. In this study, ophthalmic arterial Doppler signals were obtained from 106 subjects, 54 of whom suffered from ocular Behcet disease while the rest were healthy subjects. Multilayer perceptron neural network (MLPNN) employing delta-bar-delta training algorithm was used to detect the presence of ocular Behcet disease. Spectral analysis of the ophthalmic arterial Doppler signals was performed by least squares (LS) autoregressive (AR) method for determining the MLPNN inputs. The MLPNN was trained with training set, cross validated with cross validation set and tested with testing set. All these data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from ocular Behcet disease. Performance indicators and statistical measures were used for evaluating the MLPNN. The correct classification rate was 96.43% for healthy subjects and 93.75% for unhealthy subjects suffering from ocular Behcet disease. The classification results showed that the MLPNN employing delta-bar-delta training algorithm was effective to detect the ophthalmic arterial Doppler signals with Behcet disease.
Güler
Inan
I
Department of Electronics and Computer Education, Faculty of Technical Education, Teknik Egitim Fakultesi, Gazi University, Teknikokullar, Ankara 06500, Turkey. iguler@gazi.edu.tr
Ubeyli
Elif Derya
ED
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001528:Behcet Syndrome / Q000503:physiopathology*; D001783:Blood Flow Velocity / Q000502:physiology; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D009880:Ophthalmic Artery / Q000000981:diagnostic imaging*; D012815:Signal Processing, Computer-Assisted; D018608:Ultrasonography, Doppler