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Öğe Bispectral Analysis of Epileptic EEG signals(Ieee, 2013) Sezgin, Necmettin; Tagluk, M. Emin; Ertugrul, O. Faruk; Kaya, YilmazEpilepsy 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.Öğe Classification of Hand Opening/Closing and Fingers by Using Two Channel Surface EMG Signal(Ieee, 2017) Sezgin, Necmettin; Ertugrul, Omer Faruk; Tekin, Ramazan; Tagluk, Mehmet EminIn this study, two-channel surface electromyogram (sEMG) signals were used to classify hand open/close with fingers. The bispectrum analysis of the sEMG signal recorded with surface electrodes near the region of the muscle bundles on the front and back of the forearm was classified by extreme learning machines (ELM) based on phase matches in the EMG signal. EMG signals belonging to 17 persons, 8 males and 9 females, with an average age of 24 were used in the study. The fingers were classified using ELM algorithm with 94.60% accuracy in average. From the information obtained through this study, it seems possible to control finger movements and hand opening/closing by using muscle activities of the forearm which we hope to lead to control of intelligent prosthesis hands with high degree of freedom.Öğe Classification of Sleep Apnea through Sub-band Energy of Abdominal Effort Signal Using Wavelets plus Neural Networks(Springer, 2010) Tagluk, M. Emin; Sezgin, NecmettinDetection and classification of sleep apnea syndrome (SAS) is a critical problem. In this study an efficient method for classification sleep apnea through sub-band energy of abdominal effort using a particularly designed hybrid classifier as Wavelets + Neural Network is proposed. The Abdominal respiration signals were separated into spectral sub-band energy components with multi-resolution Discrete Wavelet Transform (DWT). The energy content of these spectral components was applied to the input of the artificial neural network (ANN). The ANN was configured to give three outputs dedicated to SAS cases; obstructive sleep apnea (OSA), central sleep apnea (CSA) and mixed sleep apnea (MSA). Through the network, satisfactory results that rewarding 85.62% mean accuracy in classifying SAS were obtained.Öğe Complexity and Irregularity Analysis of the Output Data of a Cortical Network(Ieee, 2013) Tekin, Ramazan; Tagluk, M. Emin; Ertugrul, Omer Faruk; Sezgin, NecmettinDepending on the complex interconnection of billions of neurons forming cortical network excitation times and the emergence of action potentials or spike trains becomes complex and irregular. The effect of various parameters such as synaptic connections, conductivity and voltage dependent channels on the output of the network has become of research issues. In this study, based on Hodgkin-Huxley neuron model an artificial cortical network that simulates a local region of cortex was designed and the effect of probabilistic values of network parameters used in this model on irregularity and complexity of the spike trains at the neurons' output were investigated. Approximation Entropy, Spectral Entropy and Magnitude Squared Coherence methods were used for irregularity analysis.Öğe Determining relevant features in estimating short-term power load of a small house via feature selection by extreme learning machine(Ieee, 2017) Ertugrul, Omer Faruk; Sezgin, Necmettin; Oztekin, Abdulkerim; Tagluk, Mehmet EminEstimating short-term power load is a fundamental issue in the power distribution system. Since short-term power load is related to many parameters such as weather conditions, and time. The aim of this study is to determine the relevant parameters in estimating short-term power load not only in order to decrease the computational cost, but also to achieve higher success rates. Furthermore, by using selected features the required memory, equipment and communication costs are also decreased in real time applications. Feature selection by extreme learning machine method was used in determining relevant features. The short-term power loads of two houses (one of them has a power generation capability) were used in tests and achieved results showed lower error rates were obtained by using less number of features.Öğe Energy based feature extraction for classification of sleep apnea syndrome(Pergamon-Elsevier Science Ltd, 2009) Sezgin, Necmettin; Tagluk, M. EminIn this paper it is aimed to classify sleep apnea syndrome (SAS) by using discrete wavelet transforms (DWT) and an artificial neural network (ANN). The abdominal and thoracic respiration signals are separated into spectral components by using multi-resolution DWT. Then the energy of these spectral components are applied to the inputs of the ANN. The neural network was configured to give three outputs to classify the SAS situation of the subject. The apnea can be mainly classified into three types: obstructive sleep apnea (OSA), central sleep apnea (CSA) and mixed sleep apnea (MSA). During OSA, the airway is blocked while respiratory efforts continue. During CSA the airway is open, however, there are no respiratory efforts. In this paper we aim to classify sleep apnea in one of three basic types: obstructive, central and mixed. A significant result was obtained. (C) 2009 Elsevier Ltd. All rights reserved.Öğe Estimation of Sleep Stages by an Artificial Neural Network Employing EEG, EMG and EOG(Springer, 2010) Tagluk, M. Emin; Sezgin, Necmettin; Akin, MehmetAnalysis and classification of sleep stages is essential in sleep research. In this particular study, an alternative system which estimates sleep stages of human being through a multi-layer neural network (NN) that simultaneously employs EEG, EMG and EOG. The data were recorded through polisomnography device for 7 h for each subject. These collective variant data were first grouped by an expert physician and the software of polisomnography, and then used for training and testing the proposed Artificial Neural Network (ANN). A good scoring was attained through the trained ANN, so it may be put into use in clinics where lacks of specialist physicians.Öğe Geliştirilen önişlemeli sinir ağı modelleri ile yetişkin insanlarda uyku apne ve çeşitlerinin teşhisi(İnönü Üniversitesi, 2010) Sezgin, NecmettinUyku apnesi sendromu (UAS) dünyanın ciddi sağlık problemlerinden biridir. Bu sendromun tedavisinde erken teşhis oldukça önemli bir faktördür. Bu çalışmada, UAS'nin teşhisi ve sınıflandırılması için farklı yöntemler araştırıldı. Birinci metot, hastanın horlama ses işaretinin, zaman?frekans analizi ile ortaya çıkan apne ile ilintili belirli yapıdaki bileşenlerin enerjisi, tasarlanan YSA'ya verilerek UAS teşhisini ele almıştır. İkinci metot, hastalardan kayıtlanan EEG işaretlerini ele almıştır. EEG işaretinin delta, theta, alfa, beta, ve gamma altbantlarının ikiz?spektrumu ile ortaya çıkan kuadratik faz eşleşmeleri gibi karakteristik özellikler quantifiye edilerek YSA'ya verilmiştir. Üçüncü metot, hastadan alınan karın ve göğüs hareketi işaretlerini ele almıştır. Bu veriler sonra Ayrık Dalgacık Dönüşümü (ADD) kullanarak 7. seviyeye kadar dalgacık katsayılarına ayrıştırılmıştır. UAS sınıflandırması için bir ADD?YSA tasarlanmıştır. Bu dalgacıkların 1. seviyeden 7. seviyeye kadar detay katsayılarının enerjisi ile 7. seviyedeki yaklaşık katsayılarının enerjisi hesaplanarak bu ağa verilmiştir. YSA ile değerlendirilen veriler aynı zamanda Adaptif Ağ Yapısına Dayalı Bulanık Çıkarım Sistemi (ANFIS) ile de değerlendirildi ve elde edilen sonuçlar karşılaştırıldı. Önerilen bu metotlar ile UAS'yi teşhis ve sınıflandırmada yüksek başarım oranları elde edildi. Bu şekildeki veri analizinin nöroloji ve uyku bozuklukları alanlarında da kullanılması mümkündür. Hem hastaya hem de uzman hekime kolaylık sağlaması için geliştirilen SDD YSA ve ADD YSA modelleri PSG cihazına entegre edilebileceği düşünülmektedir.Öğe A new approach for estimation of obstructive sleep apnea syndrome(Pergamon-Elsevier Science Ltd, 2011) Tagluk, M. Emin; Sezgin, NecmettinObstructive sleep apnea syndrome (OSAS) is a situation where repeatedly upper airway stops off while the respiratory effort continues during sleep at least for 10 s. Apart from polysomnography, many researchers have concentrated on exploring alternative methods for OSAS detection. However, not much work has been done on using non-Gaussian and nonlinear behavior of the electroencephalogram (EEG) signals. Bispectral analysis is an advanced signal processing technique particularly used for exhibiting quadratic phase-coupling that may arise between signal components with different frequencies. From this perspective, in this study, a new technique for recognizing patients with OSAS was introduced using bispectral characteristics of EEG signal and an artificial neural network (ANN). The amount of Quadratic phase coupling (QPC) in each subband of EEG (namely; delta, theta, alpha, beta and gamma) was calculated over bispectral density of EEG. Then, these QPCs were fed to the input of the designed ANN. The neural network was configured with two outputs: one for OSAS and one for estimation of normal situation. With this technique a global accuracy of 96.15% was achieved. The proposed technique could be used in designing automatic OSAS identification systems which will improve medical service. (C) 2010 Elsevier Ltd. All rights reserved.Öğe Time-Frequency analysis of Snoring Sounds in Patients With Simple Snoring And OSAS(Ieee, 2009) Tagluk, M. Emin; Akin, Mehmet; Sezgin, NecmettinIn recent years variety of studies has been conducted towards the identification of correlation between Obstructive Sleep Apnea Syndrome (OSAS) and snoring. The features defected from time and frequency domain analysis of snores showed the differences between simple and OSAS patients. In this study the total episodes of 1500 snore records taken from 7 simple and 14 OSAS patients were evaluated through time-frequency analysis. From the time-frequency analysis the differences, particularly from the spectral bandwidth point of view, between the two groups were identified, and using this data the method was suggested as a cost effective and simple technique to be widely used in defection of OSAS from simple patients.
