Yazar "Tezel G." seçeneğine göre listele

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    The classification performance comparison of ANN and PSO-NN on the heart diseases diagnosis
    (2013) Yucelbas S.; Tezel G.
    Artificial neural networks, used for practices of engineering, are strong tool for useful relationships between data. Particle swarm optimization is successfully carried out to train feedforward neural networks. In this study, heart diseases diagnosis was realized by using Electrocardiography (ECG) records that taken from MIT-BIH ECG database, which were used to classify 6 different arrhythmias for training and testing. These are normal sinus rhythm (NSR), ventricular tachycardia (VTK), sinus arrhythmia (SA), atrial premature contraction (APC), atrial fibrillation (AF) and ventricular trigeminy (VTI). Artificial Neural Networks (ANN) and Artificial Neural Networks trained by particle swarm optimization (PSO-NN) are used as a classifier and they are compared. Experimental results have revealed that PSO-NN model is better for classification of ECG signals than traditional ANN. © 2013 IADIS.
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    Detection of REM in sleep EOG signals
    (Indian Society for Education and Environment, 2016) Coskun A.; Ozsen S.; Yucelbas S.; Yucelbas C.; Tezel G.; Kuccukturk S.; Yosunkaya S.
    Background/Objectives: Sleep staging is very important phase for diagnosing respiration and sleep diseases. Nowadays, Electroencephalogram (EEG), Electromyogram (EMG), Electrooculogram (EOG) signals are particularly used together in studies on sleep staging. Methods/Statistical Analysis: Associating only EOG signals to sleep staging was distinctly purposed. So, this paper deals with extraction features and classifying for determining REM-NREM states from the EOG signals. In this study, left eye (LEOG) and the right eye (REOG) signals were used. After EOG signals were obtained, 21 different features were extracted from LEOG and REOG in time and frequency domain according to rules of American Academy of Sleep Medicine (AASM). Findings: Artificial Neural Networks (ANN) was adopted on features as method of classification with 3-fold cross validation technique and reached conclusion with the maximum test classification accuracy as 88.05%. To obtain higher classification accuracies, Sequential Backward Selection (SBS) method was used. According to results of SBS, number of the best features combination was determined as 13 and the maximum classification accuracy was obtained as 89.62%. The optimum value of hidden layer node number of ANN was determined as 15 for the best features. Application/Improvements: When looking from the viewpoint of percentage of classification accuracy of this study, a result can be seen that is non-negligible value for literature.
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    Detection of sleep spindles in sleep EEG by using the PSD methods
    (Indian Society for Education and Environment, 2016) Yucelbas C.; Yucelbas S.; Ozsen S.; Tezel G.; Kuccukturk S.; Yosunkaya S.
    Background/Objectives: In this study, Fast Fourier Transform (FFT), Welch, Autoregressive (AR) and MUSIC methods were implemented to detect sleep spindles (SSs) in Electroencephalogram (EEG) signals by extracting features in frequency space. Methods/Statistical Analysis: A database from these signals of five subjects which were recorded at sleep laboratory of Necmettin Erbakan University in Turkey was ready for use. The database consisted of 600 EEG epochs in total. The number of epochs was 300 for both with and without SSs in this database. Comparison of the performances of these methods on SS determination process was performed by using Artificial Neural Networks (ANN) classifier. Findings: According to the test classification results, notable difference was obtained between the applied PSD methods. By using the extracted all features, maximum test classification accuracies were achieved as 84.83%, 80.67%, 80.83% and 80.33% with use of FFT, Welch, AR and MUSIC, respectively. To determine the SSs, Principal Component Analysis (PCA) also was utilized in this study. When PCA was applied, the results were 89.50%, 82.00%, 93.00% and 94.83% by use of the same PSD methods, respectively. Application/Improvements: As a result, the performance of PCA and MUSIC is better than the others. Hence, these methods can be used safely for automatic detection of SSs.
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    Detection of the electrode disconnection in sleep signals [Uyku Sinyallerindeki Elektrod Kopuklu?unun Tespit Edilmesi]
    (Institute of Electrical and Electronics Engineers Inc., 2015) Yücelbaş C.; Özşen S.; Yücelbaş S.; Tezel G.; Dursun M.; Yosunkaya S.; Küççüktürk S.
    Sleep staging process that is performed in sleep laboratories in hospitals has an important role in diagnosing some of the sleep disorders and disturbances which are seen in sleep. And also it is an indispensable method. It is usually performed by a sleep expert through examining during the night of the patients (6-8 hours) recorded Electroencephalogram (EEG), Electrooculogram (EOG), Electromyogram (EMG), electrocardiogram (ECG) and other some signals of the patients and determining the stages of sleep in different time sections named as epochs. Manual sleep staging is preferred among the sleep experts but because it is rather tiring and time consuming task, automatic sleep stage scoring studies has come to the fore. However, none of the so far made automatic sleep staging was not accepted by the experts. The most important reason is that the results of the automated systems are not desired accuracy. There are many factors that affecting the accuracy of the systems, such as noise, the inter-channel interference, excessive body movements and disconnection of electrodes. In this study, we examined the written an algorithm to be able to determine to what extent the disconnection of electrodes in EEG signal that obtained one healthy person at the sleep laboratory of Meram Medicine Faculty of Necmettin Erbakan University. According to the obtained application results, the electrodes disconnection in EEG signal could be detected maximum of 100% and minimum of 99.12% accuracy. Accordingly, based on the success achieved in the study, this algorithm is thought to contribute positively to the researchers that the work on and will work on sleep staging problems and increase the success of automatic sleep staging systems. © 2015 IEEE.
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    Effect of EEG time domain features on the classification of sleep stages
    (Indian Society for Education and Environment, 2016) Yucelbas S.; Ozsen S.; Yucelbas C.; Tezel G.; Kuccukturk S.; Yosunkaya S.
    Background/Objectives: Studies on the field of automatic sleep stage classification have been taking more attention of researchers day by day. Noise in the recordings, nonlinear dynamic feature of EEG signals and some other reasons affect the performance of proposed systems in negative manner. Methods/Statistical Analysis: Sleep can be divided five main stages as Wake, Non-REM1, Non-REM2, Non-REM3 and REM. Almost every proposed method can successfully classify some evident stages like Non-REM2 and REM. But when it comes to the transitions between stages, the systems are not very good in their performances. Thus a different classification strategy was proposed in this study. Five different classifiers were designed especially for transitions between stages using time domain features of EEG, EOG and EMG signals and evaluated these features for each classifier. Sequential backward feature selection process was applied in each classifier to find out which features are dominant in each classification procedure. Artificial Neural Networks was used in designed classifiers. Findings: The highest classification accuracy was obtained as 91.03% for Classifier-3 which predicts stages coming after Non-REM II. The lowest accuracy was recorded as 75.42% for Classifier-2 in which stages are determined after the Non-REM I epochs. Comparatively good results were reached especially if it is taken into account that only used time-domain features of signals. Application/Improvements: The obtain results show that the designed classifiers can be used in automatic sleep staging system, confidently.
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    Effect of the Hilbert-Huang transform method on sleep staging [Hilbert-Huang Dönüşümü Metodunun Uyku Evreleme Üzerindeki Etkisi]
    (Institute of Electrical and Electronics Engineers Inc., 2017) Yucelbas C.; Yucelbas S.; Ozsen S.; Tezel G.; Yosunkaya S.
    Sleep scoring is performed by examining the recorded electroencephalogram (EEG) and some other signals recorded by a polysomnography (PSG) device. This process is considered more reliable as it is done manually by experts. However, due to the fact that experts may also be mistaken, it has led to an increase in the importance given to automatic sleep staging studies. Many methods have been tested on the signals in order to increase the success of these systems. In this study, an automatic sleep staging system was implemented using the Hilbert-Huang transformation method which is a new time-frequency analysis type. In the study, EEG signals from 5 subjects were used in the sleep laboratory. In the 5-class (Alpha, Beta, Theta, Delta and Spindle bands) applications, the highest classification success was 84.75% as a result of sequential feature selection method. © 2017 IEEE.
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    Elimination of EMG artifacts from EEG signal in sleep staging [Uyku evrelemede EMG Gürültülerinin EEG Sinyalinden Elenmesi]
    (Institute of Electrical and Electronics Engineers Inc., 2016) Ozsen S.; Yucelbas C.; Yucelbas S.; Tezel G.; Yosunkaya S.; Kuccukturk S.
    Sleep staging is a tiring and time-consuming process for the experts. Thus, attention given to automatic sleep staging studies is increasing gradually. Many factors such as effects of EOG and EKG signals to EEG result in contaminated signals rather than clear recorded signals. EMG contamination to EEG is among that kind of factors. In this study, some filters and Discrete Wavelet Transform based EMG artifact elimination process were evaluated on the performance of sleep staging process. Features were extracted from cleaned EEG signals and subjected to a classifier to conduct sleep staging. By using test classification accuracy as a measure of performance, the method giving highest accuracy was tried to be found. Artificial Neural Networks was used in the applications and Discrete Wavelet Transform was found to be the method giving the highest accuracy. © 2016 IEEE.
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    The modelling of [Fe(CN)6]4- adsorption onto sepiolite with artificial neural network [Sepiyolitin [Fe(CN)6]4- kompleksi adsorpsiyonunun yapay sinir a?lari{dotless} i?le modellenmesi]
    (Chamber of Mining Engineers of Turkey, 2013) Önen V.; Yel E.; Tezel G.
    Artificial neural network (ANN) is the modeling method which has been succesfully used for adsorption processes in recent years. In this study, effectiveness of sepiolite in the adsorption of a strong metal-cyanide complex, [Fe(CN)6]4-, from aqueous solution was investigated. The experimental results was used as the database in forecasting model developed in ANN. Fe and CN adsorption capacities of sepiolite were forecasted by two hidden layer ANN model. Concentration, particle size, time and activation conditions were input independent variables while the capacity was forecasted as output depended variable. Total 324 data was randomly separated to two subsets as 232 training and 92 test data. Tansig-tansig-logsig functions and 8 and 7 neurons in the first and second hidden layers, respectively, resulted in the best model configuration. The highest correlation and the lowest errors achieved with this configuration were 0.99401-0.015 at training and 0.98983- 0.020 at test. Cross validation was applied to the best configuration. A nine fold cross validation resulted in 0.99442-0.015 correlation-error values for training and 0.98088-0.026 for testing. The achieved close correlation and error values after cross valudation indicated the success and confidency of the established model.
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    A new approach for classification of EEG signals [EEG sinyallerinin siniflandirilmasinda yeni bir yaklaşim]
    (2007) Tezel G.; Özbay Y.
    This study presents a comparative study of the classification accuracy and speed of performance of epileptic Electroensefalogram (EEG) signals using a traditional neural network architecture based on backpropagation training algorithm, and a new neural network. The proposed network is called adaptive neural network with activation function (AAF-NN) in which adjustable parameters, It is used two different activation functions for developed study. One of theese adaptive activation functions is sigmoid function with free parameters and the other one is sum of sinusoidal function with free parameters and sigmoid function with free parameters. The adaptive activation function with free parameters is used in the hidden layer for the proposed structures based on the feed-forward neural network Experimental results have revealed that neural network with adaptive activation function is more suitable for classification EEG signals and training speed is much faster than traditional neural network with fixed sigmoid activation function.