Yazar "Tagluk, M. Emin" seçeneğine göre listele
Listeleniyor 1 - 20 / 20
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe Assessment of myoelectrical signal parameters in estrogen, progesterone, and human chorionic gonadotropin administered in nonpregnant rat myometrium after ovariectomy(Elsevier Science Inc, 2008) Celik, Onder; Hascalik, Seyma; Tagluk, M. Emin; Elter, Koray; Parlakpinar, Hakan; Acet, AhmetObjective: To investigate the correlation of myoelectrical signals with spontaneous contractile events and physiological states in the nonisolated uterine horn of rats. Design: In vivo uterine myoelectrical activity recording study. Setting: Animal and pharmacology laboratory at Inonu University. Animal(s): Thirty-six female Wistar albino rats. Intervention(s): Six animals were not castrated and served as a sham-operated control group; the other 30 were ovariectomized (OVX) and put into groups: unbiased OVX subjects, estrogen (E)-biased OVX subjects, P-biased OVX subjects, E-plus-P-biased OVX subjects, and hCG-biased OVX subjects. An MP100 A-CE was used for data acquisition, and a personal computer was used for processing. Main Outcome Measure(s): Besides the temporal, spectral, and joint time-frequency (spectrotemporal) analysis, some quantitative measures such as standard deviation and mark to space power I ratios of myoelectrical signals were measured. Result(s): Progesterone, E, and hCG administration down-regulated the power and contraction frequency of the uterine electrical signal. The spectral concentrations that occurred around the 0.9, 0.35, and 0.7 Hz frequency ranges may be distinguishing characteristics for P, E, and hCG, respectively. Conclusion(S): Based on the obtained results, uterine contractions change with ovariectomy and administration of hormones. Progesterone, E, and hCG particularly prolong the quiescent periods of the uterus by reducing the frequency of uterine contractions as well as the power of the myoelectrical activity. Individual or combined use of R. or hCG might favor quiescence of the uterine muscle and the maintenance of pregnancy.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 sleep apnea by using wavelet transform and artificial neural networks(Pergamon-Elsevier Science Ltd, 2010) Tagluk, M. Emin; Akin, Mehmet; Sezgin, NemettinThis paper describes a new method to classify sleep apnea syndrome (SAS) by using wavelet transforms and an artificial neural network (ANN) The network was trained and tested for different momentum coefficients. The abdominal respiration signals are separated into spectral components by using multi-resolution wavelet transforms. These spectral components are applied to the inputs of the artificial neural network. Then the neural network was configured to give three outputs to classify the SAS situation of the patient. The apnea can be broadly 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. (C) 2009 Elsevier Ltd. Ail rights reserved.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 Communication in nano devices: Electronic based biophysical model of a neuron(Elsevier Science Bv, 2019) Tagluk, M. Emin; Isik, IbrahimInvestigating new strategies and signaling techniques for nano-devices and systems is quite challenging. The communication systems considered to be feasible in nano-devices are inspired from biophysical systems which communicate with electro-chemical signals organized with respect to excitation. While the electrical pulses transmitted along with the cell membrane the chemical signal transmitted in the synaptic cleft. Developing new chemical signal based communication which termed as the molecular communication with minimum error is now the central deal for the researchers. Strategic approaches to the issue in variety of perspective such as systematic, experimental and electronic circuitry viable for chip based robotic and nano-device design are now available in the literature. Biological signaling pathways, in accordance with the action potentials generated in pre-synaptic neuron some chemical substances called neurotransmitters released into the synaptic cleft and hence the post-synaptic neuron is accordingly triggered. In this way the information transmitted from one cell to another by electro chemical signal carriers. About this process some electronic neuron models have also been introduced to simulate dynamic behavior of neuronal cells. In this study, a novel simple electronic integrate and fire model which has been designed previously was further developed and used to simulate and analyze the communication of neurons. The proposed electronic model not only simulates the neuronal cell's behavior and also can transmit the information to the following neuron. The rate of correct transmission depends on the synaptic channel model. The characteristics of the used semiconductor components with overall structure of the proposed electronic model are very close to the biophysical nature of neuron and can be designed on semiconductor chips which is the advantage of the model. (C) 2019 Elsevier B.V. All rights reserved.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 Detecting Fault Type and Fault Location in Power Transmission Lines by Extreme Learning Machines(Ieee, 2015) Tagluk, M. Emin; Mamis, Mehmet Salih; Arkan, Muslum; Ertugrul, Omer FarukImportance of supplying qualified and undisturbed electricity is increasing day by day. Therefore, detecting fault, fault type and fault location is a major issue in power transmission system in order to prevent power delivery system security. In previous studies, we observed that faults can be easily determined by extreme learning machine (ELM) and the aim of this study is to determine applicability of ELM in fault type, zone and location detection. 8 different feature sets were exacted from fault data that produced by ATP and these features were assessed by 15 different classifier and 5 different regression method. The results showed that ELM can be employed for detecting fault types and locations successfully.Öğe Effects of highly purified urinary FSH and human menopausal FSH on uterine myoelectrical dynamics(Oxford Univ Press, 2010) Hascalik, Seyma; Celik, Onder; Tagluk, M. Emin; Yildirim, Ayse; Aydin, N. EnginThe aim of the study was to investigate the effects of urinary follicle-stimulating hormone (FSH) compounds on the electrical activity of myometrium using signal-processing techniques. Thirty animals were involved in the experiment. After two successive normal estrous cycles, 15 of these animals were put into three equal subgroups. Group 1 was the control; animals were given solvent. Groups 2 and 3 were treated with Urofollitropin and Menotropin, respectively. The other 15 animals were ovariectomized and subjected to the same protocol. Their uterine myoelectrical signals were recorded over a period of at least 3 min at a sampling frequency of 500 Hz, and analyzed through software assisted signal processing. The results show the power and some characteristic spectral components of myoelectrical signal were differentially reduced with the administration of highly purified urinary FSH and human menopausal FSH but significant differences were not detected between their histology. In conclusion, uterine myoelectrical signals change with administration of urinary FSH preparations. Human menopausal FSH and more precisely highly purified FSH suppress the spectral components and modify the power of the myoelectrical signals which provides uterine quiescence.Öğe EMG Signal Classification by Extreme Learning Machine(Ieee, 2013) Ertugrul, Omer Faruk; Tagluk, M. Emin; Kaya, Yilmaz; Tekin, RamazanFrom 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.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 Fault Detection at Power Transmission Lines by Extreme Learning Machine(Ieee, 2013) Ertugrul, Omer Faruk; Tagluk, M. Emin; Kaya, YilmazWith 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.Öğe Fault Location on Series Compensated Power Transmission Lines Using Transient Spectrum(Ieee, 2015) Akmaz, Duzgun; Mamis, Mehmet Salih; Arkan, Muslum; Tagluk, M. EminNowadays sustainability and quality of energy have gained more importance. Power outages due to failures particularly cause interruption of production at industrial facilities may lead to loss of manpower and resources. One of the major causes of power outages in the power system is the short-circuit faults occurring in transmission lines. The most important requirement to clear the fault in a short time is to estimate the fault location quickly and accurately. In this study, fault location is determined in series compensated power transmission lines utilizing transient frequency spectrum. It has been shown that the method is suitable for series compensated lines.Öğe The influence of ion concentrations on the dynamic behavior of the Hodgkin-Huxley model-based cortical network(Springer, 2014) Tagluk, M. Emin; Tekin, RamazanAction potentials (APs) in the form of very short pulses arise when the cell is excited by any internal or external stimulus exceeding the critical threshold of the membrane. During AP generation, the membrane potential completes its natural cycle through typical phases that can be formatted by ion channels, gates and ion concentrations, as well as the synaptic excitation rate. On the basis of the Hodgkin-Huxley cell model, a cortical network consistent with the real anatomic structure is realized with randomly interrelated small population of neurons to simulate a cerebral cortex segment. Using this model, we investigated the effects of Na+ and K+ ion concentrations on the outcome of this network in terms of regularity, phase locking, and synchronization. The results suggested that Na+ concentration does slightly affect the amplitude but not considerably affects the other parameters specified by depolarization and repolarization. K+ concentration significantly influences the form, regularity, and synchrony of the network-generated APs. No previous study dealing directly with the effects of both Na+ and K+ ion concentrations on regularity and synchronization of the simulated cortical network-generated APs, allowing for the comparison of results obtained using our methods, was encountered in the literature. The results, however, were consistent with those obtained through studies concerning resonance and synchronization from another perspective and with the information revealed through physiological and pharmacological experiments concerning changing ion concentrations or blocking ion channels. Our results demonstrated that the regularity and reliability of brain functions have a strong relationship with cellular ion concentrations, and suggested the management of the dynamic behavior of the cellular network with ion concentrations.Öğe Influence of Rewiring on Spike Activity and Phase Coherence in a Small-World Cortical Network(Ieee, 2016) Tekin, Ramazan; Tagluk, M. EminSubthreshold spike activities have a critical importance in synchronization of neuronal activity. In nonlinear systems, low level signals, depending on topologic structure and noise level, can increase the activity of the systems. Whether similar effects are valid for normal brain function or not is an important question need to be addressed. So, the objective of this study was to investigate for the influence of rewiring in a cortical network with Small-World (SW) topology on spike coherency. The investigation showed that SW rewiring rate (p) has an effect on synchronization of subthreshold activities and therefore increasing spike activity and increased discharges synchronized to the input of network.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 Spectrotemporal changes in electrical activity of myometrium due to recombinant follicle-stimulating hormone preparations follitropin alfa and beta(Elsevier Science Inc, 2008) Celik, Onder; Tagluk, M. Emin; Hascalik, Seyma; Elter, Koray; Celik, Nilufer; Aydin, Nasuhi EnginObjective: To investigate the effects of follitropin alfa and beta on the myoelectrical activity of rat myometrium using signal-processing techniques. Design: Prospective, placebo-controlled study. Setting: Animal and pharmacology laboratory at Inonu University. Animal(S): Forty-five female Wistar albino rats. Intervention(s): Thirty of 45 animals involved in the experiment were registered as the superovulation group. After two successive normal estrous cycles, these animals were put into three equal subgroups. Group 1 was the control; animals were given 0.9% saline. Groups 2 and 3 were treated with follitropin alfa (Gonal-f) and follitropin beta (Puregon), respectively. The other 15 animals were ovariectomized (OVX) and subjected to the same protocol. The uterine myoelectrical signals were recorded and analyzed using a Matlab environment. Main Outcome Measure(s): Power/second, variance, and the effects of recombinant human follicle-stimulating hormone (FSH) on myoelectrical signals were assessed through temporal, spectral, and joint time-frequency analysis. The uterine endometrium and ovarian morphology were also assessed concerning primary follicles, antral follicles, and corpora lutea. Result(S): The power and some characteristic spectral components of myoelectrical signal were reduced with the administration of follitropin alfa and beta. No statistically significant difference was detected between endometrial and ovarian histology of the rats treated with these follitropins. Conclusion(s): Uterine myoelectrical signals change with administration of recombinant human FSH preparations. Follitropin beta and, more precisely, follitropin alfa suppress the spectral components and power of the myoelectrical signals, which provides uterine quiescence.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.Öğe Use of porcine small intestinal submucosa to reconstruct an ovarian defect(Elsevier Ireland Ltd, 2009) Celik, Onder; Esrefoglu, Mukaddes; Hascalik, Seyma; Gul, Mehmet; Tagluk, M. Emin; Elter, Koray; Aydin, EnginObjective: To investigate the feasibility of using porcine small intestinal submucosa (SIS) as a scaffold for repairing ovarian defects. Method: Fourteen female New Zealand rabbits undergoing ovarian resection were randomly allocated to 2 equal groups. The unilateral ovarian defects were, repaired with SIS in group I animals and without SIS in group 2 animals (control). The volumes of the ovaries were calculated and the severity of adhesions was assessed in I animal from each group each month. The ovaries were removed and examined under a microscope. Results: The volumes of the SIS-grafted ovaries were larger than those of the operated ovaries of the control animals (P<0.05). The SIS-grafted ovaries had a lower adhesion score than the operated ovaries of the control group (P<0.001). SIS grafts showed hemorrhage and leukocyte infiltration until the 4th week after surgery, but the ovarian tissue appeared to be well organized from the 12th to the 16th week. At the 28th week, primordial follicles were scattered in the SIS graft. Conclusion: SIS graft could be used for repairing the ovary after surgery. (C) 2009 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.Öğe Virtual hysterosalpingography and hysteroscopy: assessment of uterine cavity and fallopian tubes using 64-detector computed tomography data sets(Elsevier Science Inc, 2010) Celik, Onder; Karakas, H. Muammer; Hascalik, Seyma; Tagluk, M. EminHysterosalpingography is the primary technique in providing coarse information on the morphology of endometrial cavity and fallopian tubes. In this preliminary study, 64-detector computed tomography was used for three-dimensional imaging of endometrium and fallopian tubes. (Fertil Steril (R) 2010; 93: 2383-4. (C)2010 by American Society for Reproductive Medicine.)
