title: The effect of generalized discriminate analysis (GDA) to the classification of optic nerve disease from VEP signals.
abstract: In this paper, we have investigated the effect of generalized discriminate analysis (GDA) on classification performance of optic nerve disease from visual evoke potentials (VEP) signals. The GDA method has been used as a pre-processing step prior to the classification process of optic nerve disease. The proposed method consists of two parts. First, GDA has been used as pre-processing to increase the distinguishing of optic nerve disease from VEP signals. Second, we have used the C4.5 decision tree classifier, Levenberg Marquart (LM) back propagation algorithm, artificial immune recognition system (AIRS), linear discriminant analysis (LDA), and support vector machine (SVM) classifiers. Without GDA, we have obtained 84.37%, 93.75%, 75%, 76.56%, and 53.125% classification accuracies using C4.5 decision tree classifier, LM back propagation algorithm, AIRS, LDA, and SVM algorithms, respectively. With GDA, 93.75%, 93.86%, 81.25%, 93.75%, and 93.75% classification accuracies have been obtained using the above algorithms, respectively. These results show that the GDA pre-processing method has produced very promising results in diagnosis of optic nerve disease from VEP signals.
authors: Department of Electronics, Civil Aviation College, Erciyes University, 38039 Kayseri, Turkey. aguven@erciyes.edu.tr
authors: Ayşegül
authors: A
authors: Güven
authors: Kemal
authors: K
authors: Polat
authors: Sadik
authors: S
authors: Kara
authors: Salih
authors: S
authors: Güneş
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D016002:Discriminant Analysis; D004594:Electrophysiology; D005074:Evoked Potentials, Visual / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D009901:Optic Nerve Diseases / Q000175:diagnosis*; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted