Yekta Ulgen

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In this paper, classification of Juvenile Myoclonic Epilepsy (JME) patients and healthy volunteers included into Normal Control (NC) groups was established using Feed-Forward Neural Networks (NN), Support Vector Machines (SVM), Decision Trees (DT), and Naïve Bayes (NB) methods by utilizing the data obtained through the scanning EMG method used in a clinical(More)
We describe a new method for frequency down-conversion of MR signals acquired with the radio-frequency projections method for device localization. A low-amplitude, off-center RF pulse applied simultaneously with the echo signal is utilized as the reference for frequency down-conversion. Because of the low-amplitude and large offset from the Larmor(More)
Juvenile myoclonic epilepsy is a genetically inherited disorder characterized by myoclonic jerks and generalized seizures. It has been proposed that patients with juvenile myoclonic epilepsy have larger motor units (MUs) than normals by MU number estimation and macro electromyography techniques. In this study, an experimental setup for scanning(More)
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