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Browsing Fakülteler / Faculties by Subject "Mathematical & Computational Biology"

Now showing items 1-4 of 6

Comparison of subspace-based methods with AR parametric methods in epileptic seizure detection

Köklükaya, Etem (PERGAMON-ELSEVIER SCIENCE LTD, 2006)

Electroencephalography is an important clinical tool for the evaluation and treatment of neurophysiologic disorders related to epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable ...
Deep learning approaches in electron microscopy imaging for mitochondria segmentation

Öztel, İsmail; Yolcu Öztel, Gözde; Ilker Ersoy; Tommi A. White; Filiz Bunyak (INDERSCIENCE ENTERPRISES LTD, 2018)

Deep neural networks provide outstanding classification and detection accuracy in biomedical imaging applications. We present a study for mitochondria segmentation in electron microscopy (EM) images. Mitochondria play a ...
Diagnostic Accuracy Comparison of Artificial Immune Algorithms for Primary Headaches

Ufuk Celik; Yurtay, Nilüfer; Emine Rabia Koc; Nermin Tepe; Halil Gulluoglu; Mustafa Ertaş (HINDAWI PUBLISHING CORPORATION, 2015)

The present study evaluated the diagnostic accuracy of immune system algorithms with the aim of classifying the primary types of headache that are not related to any organic etiology. They are divided into four types: ...
Investigation of the relationship between anxiety and heart rate variability in fibromyalgia: A new quantitative approach to evaluate anxiety level in fibromyalgia syndrome

Suleyman Bilgin; Arslan, Evren; Onur Elmas; Sedat Yildiz; Omer H. Colak; Gurkan Bilgin; Hasan Rifat Koyuncuoglu; Selami Akkus; Selcuk Comlekci; Köklükaya, Etem (PERGAMON-ELSEVIER SCIENCE LTD, 2015)

Conclusion: Doctor-rated or self-rated anxiety tests should be supported with quantitative and more objective methods. Our results show that the HRV parameters will be able to support the anxiety tests in the clinical ...