title: Detection of ophthalmic artery stenosis by least-mean squares backpropagation neural network.
abstract: Doppler ultrasound is a noninvasive technique that allows the examination of the direction, velocity, and volume of blood flow. In this study, ophthalmic artery Doppler signals were obtained from 105 subjects, 48 of whom had suffered from ophthalmic artery stenosis. A least-mean squares backpropagation neural network was used to detect the presence or absence of ophthalmic artery stenosis. Spectral analysis of ophthalmic artery Doppler signals was done by the Welch method for determining the neural network inputs. The network was trained, cross validated and tested with subject records from the database. Performance indicators and statistical measures were used for evaluating the neural network. Ophthalmic artery Doppler signals were classified with the accuracy varying from 88.9% to 90.6%.
authors.affiliation: Department of Electronics and Computer Education, Faculty of Technical Education, Gazi University, 06500 Teknikokullar, Ankara, Turkey. iguler@tref.gazi.edu.tr
authors.forename: Inan
authors.initials: I
authors.lastname: Güler
authors.forename: Elif
authors.initials: E
authors.lastname: Derya Ubeyli
mesh_terms: D000293:Adolescent; D000328:Adult; D003251:Constriction, Pathologic / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D016018:Least-Squares Analysis; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D009880:Ophthalmic Artery / Q000473:pathology*; D018608:Ultrasonography, Doppler