Adaptive neuro-fuzzy inference system for analysis of Doppler signals.
In this study, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) was presented for detection of ophthalmic artery stenosis. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. The ophthalmic arterial Doppler signals were recorded from 128 subjects that 62 of them had suffered from ophthalmic artery stenosis and the rest of them had been healthy subjects. Some conclusions concerning the impacts of features on the detection of ophthalmic artery stenosis were obtained through analysis of the ANFIS. The performance of the ANFIS classifier was evaluated in terms of training performance and classification accuracies (total classification accuracy was 97.59%) and the results confirmed that the proposed ANFIS classifier has potential in detecting the ophthalmic artery stenosis.
Ubeyli
Elif Derya
ED
Dept. of Electr. & Electron. Eng., TOBB Ekonomi ve Teknoloji Univ., Ankara, Turkey. edubeyli@etu.edu.tr
D001157:Arterial Occlusive Diseases / Q000000981:diagnostic imaging*; D003251:Constriction, Pathologic / Q000000981:diagnostic imaging; D017143:Fuzzy Logic; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016571:Neural Networks, Computer; D009880:Ophthalmic Artery / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D018608:Ultrasonography, Doppler / Q000379:methods*