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    Bispectral Analysis of Epileptic EEG signals
    (Ieee, 2013) Sezgin, Necmettin; Tagluk, M. Emin; Ertugrul, O. Faruk; Kaya, Yilmaz
    Epilepsy is a neurologic disorder emerged with an abnormal discharge of a population of neurons within brain. It can be diagnosed from evaluation of EEG signals. From this motivation, in this study, in order to estimate the potency of disease the phase relation emerged between the components of epileptic EEC were investigated through bi-spectrum analysis. As the result of analysis the quantity of Quadratic Phase Coupling (QPC) come out between EEG components before, after and at the time of seizure were calculated and comparatively evaluated.
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    EMG Signal Classification by Extreme Learning Machine
    (Ieee, 2013) Ertugrul, Omer Faruk; Tagluk, M. Emin; Kaya, Yilmaz; Tekin, Ramazan
    From disease detection to action assessment EMG signals are used variety of field. Miscellaneous studies have been conducted toward analysis of EMG signals. In this study some statistical features of signal were derived, the best evocative features were selected via Linear Discriminant Analysis (LDA) and feature vectors were constructed. This analytic feature vectors were classified through Extreme Learning Machine (ELM). 8 channel EMG signals recorded from 10 normal and 10 aggressive actions were used as an example. By cross-comparison of the obtained results to the ones obtained via various feature identifying methods (AR coefficients, wavelet energy and entropy) and classification methods (NB, SVM, LR, ANN, PART, Jrip, J48 and LMT) the success of the proposed method was determined.
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    Fault Detection at Power Transmission Lines by Extreme Learning Machine
    (Ieee, 2013) Ertugrul, Omer Faruk; Tagluk, M. Emin; Kaya, Yilmaz
    With the increase of energy demand continuous energy transmission gained considerable attention. For a continuous energy transmission, the faulty power transmission line needs to be quickly isolated from the system. In this study, Extreme Learning Machine (ELM) possessing fast learning and high generalization capacity was used for this purpose and it was found as showing a good performance in detecting the faulty transmission line. In the study real fault signals recorded from transmission lines were used. A feature vector was formed from a cycle of the energy signal using relative entropy and classified via ELM. The obtained results were compared with the ones obtained through SVM, YSA, NB, J48 and PART learning techniques and the ones obtained in the previous studies. According the obtained results ELM both in terms of speed and performance was found superior.