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    A NEW APPROACH FOR DIAGNOSTIC ESTIMATION OF OBSTRUCTIVE SLEEP APNEA SYNDROME BASED ON ONE DIMENSIONAL LOCAL BINARY PATTERN
    (IEEE, 2014) Kaya, Yilmaz; Sezgin, Necmettin; Tekin, Ramazan
    In this study, a new approach for estimation of Obstructive Sleep Apnea Syndrome (OSAS) was proposed. OSAS is a sleep disorder that affects the life comfortability in human life. Up to now, the OSAS was diagnosed by Polysomnography (PSG) device by connected to the patients via electrodes. This device is expensive and restricted in the clinics. Since OSAS is serious, it should be diagnosed and treated early. For this purpose, the recorded Electroencephalography (EEG), Electromyography (EMG) and snore data were analyzed and features of them extracted by a proposed method called One Dimensional Local Binary Pattern (1D-LBP). The 1D-LBP extracted features from raw data effectively. The features, then, were fed to classifier's input in order to diagnose OSAS. As a result most of tested classifiers have yielded accuracies over 99%. The best results were obtained by using EEG, EMG and snore signal altogether. It was also shown that while the complexity of signal increase the best accuracy was obtained at the output of the classifier. The results have shown that the 1D-LBP method is an acceptable and has advantageous over conventional methods due to its capable of extract significant features from more complex signal. The results can be used in sleep laboratory for help to experts before put patient to the PSG.
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    A new approach for diagnostic estimation of Obstructive Sleep Apnea Syndrome based on One Dimensional Local Binary Pattern
    (IEEE Computer Society, 2014) Kaya, Yilmaz; Sezgin, Necmettin; Tekin, Ramazan
    In this study, a new approach for estimation of Obstructive Sleep Apnea Syndrome (OSAS) was proposed. OSAS is a sleep disorder that affects the life comfortability in human life. Up to now, the OSAS was diagnosed by Polysomnography (PSG) device by connected to the patients via electrodes. This device is expensive and restricted in the clinics. Since OSAS is serious, it should be diagnosed and treated early. For this purpose, the recorded Electroencephalography (EEG), Electromyography (EMG) and snore data were analyzed and features of them extracted by a proposed method called One Dimensional Local Binary Pattern (1D-LBP). The 1D-LBP extracted features from raw data effectively. The features, then, were fed to classifier's input in order to diagnose OSAS. As a result most of tested classifiers have yielded accuracies over 99%. The best results were obtained by using EEG, EMG and snore signal altogether. It was also shown that while the complexity of signal increase the best accuracy was obtained at the output of the classifier. The results have shown that the 1D-LBP method is an acceptable and has advantageous over conventional methods due to its capable of extract significant features from more complex signal. The results can be used in sleep laboratory for help to experts before put patient to the PSG. © 2014 IEEE.
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    A Novel Approach to Diagnosis of Sleep Apnea from Snoring Signals: Ternary Pattern Method
    (IEEE, 2017) Kaya, Yilmaz; Sezgin, Necmettin; Ertugrul, Omer Faruk
    In this study, a new approach for estimation of Obstructive Sleep Apnea Syndrome (OSAS) was proposed. OSAS is a sleep disorder that affects the life comfortability in human life. Diagnosis of OSAS is usually done by expensive devices and specialist physicians. Since OSAS is serious, it should be diagnosed and treated early. In this study, a new feature extraction method is proposed for OSAS diagnosis from snoring signals. With one (1) dimensional ternary pattern method, effective attributes were extracted from raw snoring signals and identification process was performed by classification methods. According to the obtained results, 1D-TP method has shown significant success in diagnosing OSAS from snore signals. The results can be used in sleep laboratory for help to experts before put patient to the Polysomnography (PSG) test.
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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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    Classification of butterfly images with multi-scale local binary patterns
    (IEEE, 2013) Kaya, Yilmaz; Kayci, Lokman; Sezgin, Necmettin
    Butterflies are classified first according to their outer morphological qualities. It is required to analyze their genital characters when classification according to their outer morphological qualities is not possible. The genital characters of butterflies can be obtained using various chemical substances and methods; however, these processes can only be carried out with some certain expenses. Furthermore, the preparation of genital slides is time-consuming since it requires specific processes. In this study, a computer vision system based on local binary patterns was proposed to alternative conventional diagnostic methods for the diagnosis of butterfly species. 140 images of 14 butterfly species belonging to the family of Styridae are used. The butterfly diagnostic process was carried out by using LBPP, R attributes as inputs for the ANN, SVM and LR classification methods. 100% classification was achieved with macro and micro patterns obtained with LBPP, R for different values of parameter R. As a result, it was seen butterfly wings have different types of micro and macro properties, and LBP has a major advantage in identification of butterfly species.
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    Diagnostic estimation of OSAS using binary mixture logistic regression
    (2012) Kaya, Yilmaz; Ta?luk, M. Emin; Sezgin, Necmettin
    Binary (Binomial) Logistic Regression is a statistical model that can be used for classification. Concerning the targeted outcome, if the variance of observations is higher than the variance of expectations, because of overdispersion the success rate of the method in classification goes down. This overdispersion is thought as arising from the unobserved heterogen samples in the data set. In Composite models, the overdispersion is minimized by clustering the data into homogeneous subsets and performing a subset based process. In this study a composite binary logistic regression was used for estimating the sleep apnea. Through this model, snoring signals were classified and with a 98.16% success rate the apnea was diagnosed. © 2012 IEEE.
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    Kortikal spindle salinim aktivitesinin oluşumunda ve senkronizasyonunda talamik projeksiyonlarin rolünün model temelli incelenmesi
    (Institute of Electrical and Electronics Engineers Inc., 2017) Tekin, Ramazan; Kaya, Yilmaz; Sezgin, Necmettin; Ta?luk, Mehmet Emin
    In this study, mechanisms of formation of thalamocortical spindle oscillations, which are critical for various functions of the brain, have been examined on a model basis. For this, both spike activity and spectral characteristics were investigated by isolating the cortex and thalamus and then connecting them with thalamo-cortical projections. In order to determine the spindle activity in the LFP signal of each cell group, it has been tried to determine slow-wave and spindle frequency components which can coexist with each other on the basis of superposition. For this purpose, power spectral densities of LFP signals were analyzed. According to the results of this study, spindle activity can be seen in thalamus without cortex. It can be said that thalamo-cortical projections provided by the thalamic TC cells enable the spindle activity to be transferred into the cortex and thus display itself in LFP / EEG. At the same time, it has been observed that thalamo-cortical projections increase spike activity, equalize the dominant frequency in the whole system, and also cortico-thalamic projections strengthen spindle activity. © 2017 IEEE.