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Öğe Classification of carotid artery Doppler signals in the early phase of atherosclerosis using complex-valued artificial neural network(PERGAMON-ELSEVIER SCIENCE LTD, 2007) Ceylan, Murat; Ceylan, Rahime; Dirgenali, Fatma; Kara, Sadik; Ozbay, YukselIn this study, carotid arterial Doppler ultrasound signals were acquired from left carotid arteries of 38 patients and 40 healthy volunteers. The patient group had an established diagnosis of the early phase of atherosclerosis through coronary or aortofemoropopliteal angiographies. Results were classified using complex-valued artificial neural network (CVANN). Principal component analysis (PCA) and fuzzy c-means clustering (FCM) algorithm were used to make a CVANN system more effective. For this aim, before classifying with CVANN, PCA method was used for feature extraction in PCA-CVANN architecture and FCM algorithm was used for data set reduction in FCM-CVANN architecture. Training and test data were selected randomly using 10-fold cross validation. PCA-CVANN and FCM-CVANN architectures classified healthy and unhealthy subjects for training and test data with about 100% correct classification rate. These results shown that PCA-CVANN and FCM-CVANN classified Doppler signals successfully. (c) 2005 Elsevier Ltd. All rights reserved.Öğe Classification of Dementia with ANN using Multiple Variable(IEEE, 2015) Ozic, Muhammet Usame; Ozbay, Yuksel; Ekmekci, Ahmet HakanDementia is a stage that identifies all of the symptoms that led to the weakening of multiple cognitive functions. It is usually expressed as "bunama" among people. Dementia is not a disease itself is known as a transition period that defines the initial stages of many diseases. Proportional change in mental function of many variables that influence causes our body causing this stage, textural and volume losses which may occur in the brain, the person may be counted as demographic and clinical variables. Prediction of disease with a combination of variables is one of the popular sizes in recent topics in the literature. In this study received the demented patients from Open Access Series of Imaging Studies (OASIS) database structural magnetic resonance (MR) imaging features and use in combination with the demographic data of the patients was investigated in artificial neural networks classification performance.Öğe Comparison of complex-valued neural network and fuzzy clustering complex-valued neural network for load-flow analysis(SPRINGER-VERLAG BERLIN, 2006) Ceylan, Murat; Cetinkaya, Nurettin; Ceylan, Rahime; Ozbay, YukselNeural networks (NNs) have been widely used in the power industry for applications such as fault classification, protection, fault diagnosis, relaying schemes, load forecasting, power generation and optimal power flow etc. Most of NNs are built upon the environment of real numbers. However, it is well known that in computations related to electric power systems, such as load-flow analysis and fault level estimation etc., complex numbers are extensively involved. The reactive power drawn from a substation, the impedance, busbar voltages and currents are all expressed in complex numbers. Hence, NNs in the complex domain must be adopted for these applications. This paper proposes the complexvalued neural network (CVNN) and a new fuzzy clustering complex-valued neural network (FC-CVNN) to estimate busbar voltages in a load-flow problem. The aim of this paper is to present a comparative study of estimation busbar voltages in load-flow analysis using the conventional neural network (real-valued neural network, RVNN), the CVNN and the new FC-CVNN. The results suggest that a new proposed FC-CVNN and CVNN architecture can generalize better than ordinary RVNN and the FC-CVNN is also learn faster.Öğe Comparison of FCM, PCA and WT techniques for classification ECG arrhythmias using artificial neural network(PERGAMON-ELSEVIER SCIENCE LTD, 2007) Ceylan, Rahime; Ozbay, YukselPrincipal component analysis (PCA) and wavelet transform (WT) are two powerful techniques for feature extraction. In addition, fuzzy c-means clustering (FCM) is among considerable techniques for data reduction. In other words, the aim of using FCM is to decrease the number of segments by grouping similar segments in training data. In this paper, four different structures, FCM-NN, PCA-NN, FCM-PCA-NN and WT-NN, are formed by using these two techniques and fuzzy c-means clustering. In addition, FCM-PCA-NN is the new method proposed in this paper for classification of ECG. This paper presents a comparative study of the classification accuracy of ECG signals by using these four structures for computationally efficient early diagnosis. Neural network used in this study is a well-known neural network architecture named as multi-layered perceptron (MLP) with backpropagation training algorithm. The ECG signals taken from MIT-BIH ECG database, are used in training to classify 10 different arrhythmias. These are normal sinus rhythm, sinus bradycardia, ventricular tachycardia, sinus arrhythmia, atrial premature contraction, paced beat, right bundle branch block, left bundle branch block, atrial fibrillation and atrial flutter. Before testing, the proposed structures are trained by backpropagation algorithm. All of the structures are tested by using experimental ECG records of 92 patients (40 male and 52 female, average age is 39.75 +/- 19.06). The test results suggest that FCM-PCA-NN structure can generalize better than PCA-NN and is faster than NN, FCM-NN and WT-NN. (c) 2006 Elsevier Ltd. All rights reserved.Öğe Complex-valued wavelet artificial neural network for Doppler signals classifying(ELSEVIER SCIENCE BV, 2007) Ozbay, Yuksel; Kara, Sadik; Latifoglu, Fatma; Ceylan, Rahime; Ceylan, MuratObjective: In this paper, the new complex-valued wavelet artificial neural network (CVWANN) was proposed for classifying Doppler signals recorded from patients and healthy volunteers. CVWANN was implemented on four different structures (CVWANN-1, -2, -3 and -4). Materials and methods: In this study, carotid arterial Doppler ultrasound signals were acquired from left carotid arteries of 38 patients and 40 healthy volunteers. The patient group had an established diagnosis of the early phase of atherosclerosis through coronary or aortofemoropopliteal angiographies. In implemented structures in this paper, Haar wavelet and Mexican hat wavelet functions were used as real and imaginary parts of activation function on different sequence in hidden layer nodes. CVWANN-1, -2 -3 and -4 were implemented by using Haar-Haar, Mexican hat-Mexican hat, Haar-Mexican hat, Mexican hat-Haar as real-imaginary parts of activation function in hidden layer nodes, respectively. Results and conclusion: In contrast to CVWANN-2, which reached classification rates of 24.5%, CVWANN-1, -3 and -4 classified 40 healthy and 38 unhealthy subjects for both training and test phases with 100% correct classification rate using leave-one-out cross-validation. These networks have 100% sensitivity, 100% specifity and average detection rate is calculated as 100%. In addition, positive predictive value and negative predictive value were obtained as 100% for these networks. These results shown that CVWANN-1, -3 and -4 succeeded to classify Doppler signals. Moreover, training time and processing complexity were decreased considerable amount by using CVWANN-3. As conclusion, using of Mexican hat wavelet function in real and imaginary parts of hidden Layer activation function (CVWANN-2) is not suitable for classifying healthy and unhealthy subjects with high accuracy rate. The cause of unsuitability (obtaining the poor results in CVWANN-2) is tack of harmony between type of activation function in hidden layer and type of input signals in neural network. (c) 2007 Elsevier B.V. All rights reserved.Öğe Contrast enhancement using linear image combinations algorithm (CEULICA) for enhancing brain magnetic resonance images(TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, 2014) Yilmaz, Burak; Ozbay, YukselBrain magnetic resonance imaging (MRI) images support important information about brain diseases for physicians. Morphological alterations in brain tissues indicate the probable existence of a disease in many cases. Proper estimation of these tissues, measuring their sizes, and analyzing their image patterns are parts of the diagnosis process. Therefore, the interpretability and perceptibility level of the MRI image is valuable for physicians. In this paper, a new image contrast enhancement algorithm based on linear combinations is presented. The proposed algorithm is focused on improving the interpretability and perceptibility of the image information. An MRI image is presented to the algorithm, which generates a set of images from this MRI image. After this step, the algorithm uses the linear combination technique for combining the image set to generate a final image. Linear combination coefficients are generated using the artificial bee colony algorithm. The algorithm is evaluated by 4 different global image enhancement evaluation techniques: contrast improvement ratio (CIR), enhancement measurement error (EME), absolute mean brightness error (AMBE), and peak-signal-to-noise ratio (PSNR). During the evaluation process, 2 case studies are performed. The first case study is performed with 3 different image sets (T1, T2, and proton density) presented to the algorithm. These sets are obtained from the Brainweb simulated MRI database. The algorithm shows the best performance on the T1 image set with 5.844 CIR, 6.217 EME, 15.045 AMBE, and 22.150 dB PSNR scores as average values. The second case study is also performed with 3 different image sets (T1-fast low-angle shot sequence, T1-magnetization-prepared rapid acquired gradient-echoes (MP-RAGE), and T2) obtained from the The Multimedia Digital Archiving System public community database. The algorithm performs best with the T1-MP-RAGE modality images with 6.983 CIR, 17.326 EME, 3.514 AMBE, and 30.157 dB PSNR scores as average values. In addition, this algorithm can be used for classification tasks with proper linear combination coefficients, for instance, segmentation of the white matter regions in brain MRI images.Öğe Detection of microcalcification in digitized mammograms with multistable cellular neural networks using a new image enhancement method: automated lesion intensity enhancer (ALIE)(TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, 2015) Civcik, Levent; Yilmaz, Burak; Ozbay, Yuksel; Emlik, Ganime DilekMicrocalcification detection is a very important issue in early diagnosis of breast cancer. Generally physicians use mammogram images for this task; however, sometimes analyzing these images become a hard task because of problems in images such as high brightness values, dense tissues, noise, and insufficient contrast level. In this paper, we present a novel technique for the task of microcalcification detection. This technique consists of three steps. The first step is focused on removing pectoral muscle and unnecessary parts from the mammogram images by using cellular neural networks (CNNs), which makes this a novel process. In the second step, we present a novel image enhancement technique focused on enhancing lesion intensities called the automated lesion intensity enhancer (ALIE). In the third step, we use a special CNN structure, named multistable CNNs. After applying the combination of these methods on the MIAS database, we achieve 82.0% accuracy, 90.9% sensitivity, and 52.2% specificity values.Öğe Detection of Tumor With Otsu-PSO Method On Brain MR Image(IEEE, 2014) Ozic, Muhammet Usame; Ozbay, Yuksel; Baykan, Omer KaanMultiple image thresholding is a popular method used to separate homogeneous subsets of gray level images. To find the optimum threshold in the image in the literature is still a research topic. Many image thresholding method uses the histogram of the image. In this study, the objective function of Otsu method which is a statistical process, Particle Swarm Optimization with an intuitive algorithm (PSO) by maximizing, the optimal threshold values on a medical image were studied to find. The values obtained were tested with a standard test image and brain magnetic resonance (MR) image exposed on the tumor region in segmentation, Otsu-PSO method performance was monitored.Öğe Effects of window types on classification of carotid artery Doppler signals in the early phase of atherosclerosis using complex-valued artificial neural network(PERGAMON-ELSEVIER SCIENCE LTD, 2007) Ozbay, Yuksel; Ceylan, MuratIn this study. carotid artery Doppler ultrasound signals were acquired from left carotid arteries of 38 patients and 40 healthy volunteers. The patient group had an established diagnosis of the early phase of atherosclerosis through coronary or aortofemoropopliteal angiographies. Doppler signals were processed using fast Fourier transform (FFT) with different window types, Hilbert transform and Welch methods. After these processes. Doppler signals were classified using complex-valued artificial neural network (CVANN). Effects of window types in classification were interpreted. Results for three methods and five window types (Bartlett, Blackman, Boxcar, Hamming, Harming) were presented as comparatively. CVANN is a new technique for solving classification problems in Doppler signals. Furthermore, examining the effects of window types in addition to CVANN in this classification problem is also the first study in literature related with this subject. Results showed that CVANN, whose input data were processed by Welch method for each window types stated above, had classified all training and test patterns, which consist of 36 healthy, 34 unhealthy and four healthy, four unhealthy subjects, respectively, with 100% classification accuracy for both training and test phases. (c) 2006 Elsevier Ltd. All rights reserved.Öğe The Evaluation of the Muscle Fatigue Using Frequency Features in the Cervical Region(IEEE, 2014) Ozmen, Guzin; Ozbay, Yuksel; Ekmekci, Ahmet HakanSurface electromyography is a method which is for the evaluation of the electrical activity of superficial muscles. Moreover, muscle fatigue can also be detected using surface electromyography. In this study, on 20 volunteers, the muscle fatigue in cervical region was examined using surface electromyogram signals obtained from the trapezius and sternocleidomastoid muscle. Median frequency, mean frequency and mode frequency values were calculated by Welch method to investigate the muscle fatigue. When the Trapezius and sternocleidomastoid muscles are gone from study status to fatigue status the frequency parameters have shifted towards low frequencies. In practice, the median and mean frequency values are reliable parameters for muscle fatigue. According to the results; while the muscle fatigue was observed in 27 records for the median frequency, it was occurred in 22 records for the average frequency.Öğe Fuzzy clustering complex-valued neural network to diagnose cirrhosis disease(PERGAMON-ELSEVIER SCIENCE LTD, 2011) Ceylan, Rahime; Ceylan, Murat; Ozbay, Yuksel; Kara, SadikIn this study, fuzzy clustering complex-valued neural network (FCCVNN) was proposed to classify portal vein Doppler signals recorded from 54 patients with cirrhosis and 36 healthy subjects. This proposed neural network is a new model for biomedical pattern classification. The FCCVNN was composed of three phases: fuzzy clustering, calculation of FFT values and complex-valued neural network (CVNN). In first phase, fuzzy clustering was done to reduce the number of segments in training pattern. After that, FFT values of Doppler signals were calculated for pre-processing and then obtained values, which include real and imaginary components, were used as the inputs of the CVNN for classification of Doppler signals. Classification results of FCCVNN were evaluated by the different performance evaluation criterion in literature. It shows that Doppler signals were classified successfully with 100% correct classification rate using the proposed method. Moreover, the rates of sensitivity and specificity were calculated as 100% using FCCVNN method. These results were seen to be appropriate with the expected results that are derived from physician's direct diagnosis. This method would be assisted the physician to make the final decision. (C) 2011 Elsevier Ltd. All rights reserved.Öğe Fuzzy clustering complex-valued neural network to diagnose cirrhosis disease (vol 38, pg 9744, 2011)(PERGAMON-ELSEVIER SCIENCE LTD, 2011) Ceylan, Rahime; Ceylan, Murat; Ozbay, Yuksel; Kara, Sadik[Abstract not Available]Öğe Hyperemesis Gravidarum and Cerebral Electrophysiology Determination of Cerebral Localization through Electroencephalography Signal Processing(TURKISH NEUROLOGICAL SOC, 2016) Ekmekci, Hakan Ahmet; Yilmaz, Arzu Setenay; Ozic, Muhammet Usame; Ozbay, Yuksel; Kerimoglu, Ozlem Secil; Celik, Cetin; Ozturk, SerefnurObjective: Hyperemesis gravidarum (HG) is a disease characterized by excessive vomiting and nausea during pregnancy. It differs from normal pregnancy where simple nausea and vomiting are seen frequently with unknown cause. The place and role of the brain in HG is unknown. Materials and Methods: Thirty-three healthy pregnant women and 30 patients diagnosed with HG admitted to Selcuk University Faculty of Medicine, Obstetrics and Gynecology Department were included and electroencephalograph (EEG) signals of all patients obtained at Neurology Department were examined. These signals were evaluated with high math and examined with developed engineering methods. The sampling frequency of the EEG was 200 Hz. Data were obtained in the frequency-power axis using 0.1 Hz frequency resolution, Hamming windowing, and 0.5 overlap ratio with signals on the time axis on all channels. All sub-bands have formed with unearthed power spectral density as delta, theta, alpha, and beta and after being created was calculated spectral densities. Results: As a result, while showing significant changes as delta band for Fp1F3, theta band for C3P3, F3C3, Fp1F3, P3O1, T5O1, for other channels and sub-bands has not seen any significant changes with regard to average power spectral density. Conclusion: HG and normal pregnancies, when examined in terms of power spectral density, abnormalities were observed in the EEG signals in the left hemisphere frontal area of the delta band, fronto-centro-parietal, and parietal-occipital areas of the theta band. In light of the literature, neither cerebral abnormalities in HG could be displayed nor the place of abnormality could be shown. However, this study is the first to clearly show abnormalities of theta-delta band activity and differences of locations in the left cerebral hemisphere.Öğe Integration of type-2 fuzzy clustering and wavelet transform in a neural network based ECG classifier(PERGAMON-ELSEVIER SCIENCE LTD, 2011) Ozbay, Yuksel; Ceylan, Rahime; Karlik, BekirThis paper presents a new automated diagnostic system to classification of electrocardiogram (ECG) arrhythmias. The diagnostic system is executed using type-2 fuzzy c-means clustering (T2FCM) algorithm, wavelet transform (WT) and neural network. Method of combining T2FCM and WT is used to improve performance of neural network. We aimed high accuracy rate to classification of ECG beats and constituted the automated diagnostic system to improve of classifier's performance. Ten types of ECG beats selected from MIT-BIH database were used to train the system. Then, this system was tested by the ECG signals of patients. The classification accuracy of the proposed classifier, type-2 fuzzy clustering wavelet neural network (T2FCWNN), is compared with the structures formed by type-1 FCM and WT. Process of T2FCWNN architecture is realized on three stages. First stage is formed the new training set obtained by selection of the best segments for each arrhythmia class using T2FCM. Second stage is feature extraction by WT on the new training set. Third stage is classification of the extracted features using neural network. The research showed that accuracy rate was found as 99% using this system. (C) 2010 Elsevier Ltd. All rights reserved.Öğe Model based analysis of the effects of respiration signal parameters on heart rate variability(IEEE, 2006) Yildiz, Metin; Ozbay, Yuksel; Ider, Y. ZiyaIn this study, Ursino and Magosso model that includes respiration effect on cardiovascular system is implemented using Matlab. The simulations are performed to investigate the effects of respiration rate, tidal volume and expiration-inspiration time ratio on Heart Rate Variability (HRV) signals. Power Spectral Density (PSD) of HRV signals that are obtained from model simulation was determined by Periodogram and Yule-Walker methods. There is not a significant difference between the PSDs, obtained by the two methods. The simulation results that are obtained by changing respiration rate and tidal volume, are consistent with previous experimental studies reported in the literature. However the model does not include a mechanism that accounts for the effect on HRV of the rate of change of lung volume. This is conjectured to be the reason for why model results and experimental observations are not in complete conformity when the effect of inspiration-expiration time ratio on HRV is studied.Öğe A new approach for epileptic seizure detection using adaptive(PERGAMON-ELSEVIER SCIENCE LTD, 2009) Tezel, Guelay; Ozbay, YukselThis paper presents new neural network models with adaptive activation function (NNAAF) to detect epileptic seizure. Our NNAAF models included three types named as NNAAF-1, NNAAF-2 and NNAAF-3. The activation function of hidden neuron in the model of NNAAF-1 is sigmoid function with free parameters. In the second model, NNAAF-2, activation function of hidden neuron is sum of sigmoid function with free parameters and sinusoidal function with free parameters. In the third model, NNAAF-3, hidden neurons' activation function is Morlet Wavelet function with free parameters. In addition, we implemented traditional multilayer perceptron (MLP) neural network (NN) model with. fixed sigmoid activation function in the hidden layer to compare NNAAF models. The proposed models were trained and tested using 5-fold cross-validation to prove robustness of these models and to. find the best model. We achieved 100% average sensitivity, average specificity, and approximately 100% average classification rate in all the models. It was seen that their speeds and the number of maximum iteration were changed for each model. The training time and the number of maximum iteration were reduced on about 50% using NNAAF-3 model. Hence it can be remarkable that NNAAF-3 is more suitable than the other models for real-time application. (C) 2007 Elsevier Ltd. All rights reserved.Öğe A New Method for 3D Thinning of Hybrid Shaped Porous Media Using Artificial Intelligence. Application to Trabecular Bone(SPRINGER, 2012) Jennane, Rachid; Aufort, Gabriel; Benhamou, Claude Laurent; Ceylan, Murat; Ozbay, Yuksel; Ucan, Osman NuriCurve and surface thinning are widely-used skeletonization techniques for modeling objects in three dimensions. In the case of disordered porous media analysis, however, neither is really efficient since the internal geometry of the object is usually composed of both rod and plate shapes. This paper presents an alternative to compute a hybrid shape-dependant skeleton and its application to porous media. The resulting skeleton combines 2D surfaces and 1D curves to represent respectively the plate-shaped and rod-shaped parts of the object. For this purpose, a new technique based on neural networks is proposed: cascade combinations of complex wavelet transform (CWT) and complex-valued artificial neural network (CVANN). The ability of the skeleton to characterize hybrid shaped porous media is demonstrated on a trabecular bone sample. Results show that the proposed method achieves high accuracy rates about 99.78%-99.97%. Especially, CWT (2nd level)-CVANN structure converges to optimum results as high accuracy rate-minimum time consumption.Öğe A new method for diagnosis of cirrhosis disease: Complex-valued artificial neural network(SPRINGER, 2008) Ozbay, YukselIn this study, complex-valued artificial neural network (CVANN) that is a new technique for biomedical pattern classification was proposed for classifying portal vein Doppler signals recorded from 54 patients with cirrhosis and 36 healthy subjects. Fast Fourier transform values of Doppler signals were calculated for pre-processing and obtained values, which include real and imaginary components, were used as the inputs of the CVANN for classification of Doppler signals. Classification results of CVANN show that Doppler signals were classified successfully with 100% correct classification rate using leave-one-out cross-validation. Besides, CVANN has 100% sensitivity and 100% specificity. These results were found to be compliant with the expected results that are derived from physician's direct diagnosis. This method would be to assist the physician to make the final decision.Öğe A new method for segmentation of microscopic images on activated sludge(TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, 2015) Boztoprak, Halime; Ozbay, YukselActivated sludge samples were taken from the Konya Wastewater Treatment Plant. Two hundred images for each sample were captured by a systematic examination of the slides. Segmentation of microscopic images is a challenging process due to lack of focus. Therefore, adjustment of the focus is required for every movement of the mobile stage. Because the mobile stage does not have the z axis, the focus cannot be adjusted. A new method that uses automatic segmentation of the captured images is developed for solving this problem. The proposed method is not dependent on image content, has minimal computation complexity, and is robust to noise. This method uses a cellular neural network (CNN) in which an adaptive iterative value is calculated by wavelet transform and spatial frequency. A model is fixed in the system in order to estimate the iterative value of the CNN. Integrated automatic image capture and automatic analysis of large numbers of images by using evaluation software are improved in our system. Approximately 1000 microscopic images are processed in this experiment. The proposed method is compared with the traditional threshold method and the CNN through constant iteration. The experimental results are given.Öğe A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network(PERGAMON-ELSEVIER SCIENCE LTD, 2009) Ceylan, Rahime; Ozbay, Yuksel; Karlik, BekirThis paper presents an improved classifier for automated diagnostic systems of electrocardiogram (ECG) arrhythmias. This diagnostic system consists of a combined Fuzzy Clustering Neural Network Algorithm for Classification of ECG Arrhythmias using type-2 fuzzy c-means clustering (T2FCM) algorithm and neural network. Type-2 fuzzy c-means clustering is used to improve performance of neural network. The aim of improving classifier's performance is to constitute the best classification system with high accuracy rate for ECG beats. Ten types of ECG arrhythmias (normal beat, sinus bradycardia, ventricular tachycardia, sinus arrhythmia, atrial premature contraction, paced beat, right bundle branch block, left bundle branch block, atrial fibrillation and atrial flutter) obtained from MIT-BIH database were analyzed. However, the presented structure was tested by experimental ECG records of 92 patients (40 male and 52 female, average age is 39.75 +/- 19.06). The classification accuracy of an improved classifier in training and testing, namely Type-2 Fuzzy Clustering Neural Network (T2FCNN), was compared with neural network (NN) and fuzzy clustering neural network (FCNN). In T2FCNN architecture, decision making has two stages: forming of the new training set obtained by selection of the best arrhythmia for each arrhythmia class using T2FCM and classification using neural network trained on the new training set. The results are demonstrated that the proposed diagnostic systems achieved high (99%) accuracy rate. (C) 2008 Elsevier Ltd. All rights reserved.
