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Araştırmacılar

Epilepsy diagnosis using probability density functions of EEG signals
Epileptic seizure detection using artificial neural network and a new feature extraction approach based on equal width discretization
EEG signals classification using the K-means clustering and a multilayer perceptron neural network model
Discretization approach to EEG signal classification using multilayer perceptron neural network model [EEG i·şaretlerinin çok-katmanli algilayici yapay sinir agi modeli ile siniflandirilmasinda ayriklaştirma yaklaşimi]
Epileptic seizure detection using probability distribution based on equal frequency discretization

- Mühendislik Fakültesi 5
- Elektrik - Elektronik Mühendisliği Bölümü 5
- Fakülteler 5
- Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed | SOBİAD 5
- Scopus İndeksli Yayınlar Koleksiyonu 5
- Makale Koleksiyonu (Elektrik - Elektronik Mühendisliği Bölümü) 3
- WoS İndeksli Yayınlar Koleksiyonu 3
- Bildiri Koleksiyonu (Elektrik - Elektronik Mühendisliği Bölümü) 2 Daha fazlası Daha az

- EEG signals 4
- Multilayer perceptron neural network (MLPNN) 2
- Classification 1
- Curve fitting 1
- Discrete wavelet transform (DWT) 1
- Epilepsy 1
- Epileptic seizure detection 1
- Equal frequency discretization (EFD) 1
- K-means clustering 1
- Mean square error (MSE) 1
- Probability distribution 1
- biomedical signal processing 1
- curve fitting 1
- epilepsy 1
- epileptic seizure detection 1
- equal frequency discretization 1
- equal width discretization 1
- histogram 1
- mean square error 1
- multilayer perceptron neural network 1 Daha fazlası Daha az

- 2010 15th National Biomedical Engineering Meeting, BIYOMUT2010 1
- Expert Systems with Applications 1
- INISTA 2011 - 2011 International Symposium on INnovations in Intelligent SysTems and Applications 1
- JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 1
- Journal of Medical Systems 1

Orhan, Umut | Hekim, Mahmut | Özer, Mahmut | Provaznik, Ivo

Proceedings | 2011 | INISTA 2011 - 2011 International Symposium on INnovations in Intelligent SysTems and Applications , pp.626 - 630

In this paper, the equal frequency discretization (EFD) based probability density approach was proposed to be used in the diagnosis of epilepsy from electroencephalogram (EEG) signals. For this aim, EEG signals were decomposed by using the discrete wavelet discretization (DWT) method into subbands, the coefficients in each subband were discretized to several intervals by EFD method, and the probability density of each subband of each EEG segment was computed according to the number of coefficients in discrete intervals. Then, two probability density functions were defined by means of the curve fitting over the probability densities . . .of the sets of both healthy subjects and epilepsy patients. EEG signals were classified by applying the mean square error (MSE) criterion to these functions. The result of the classification was evaluated by using the ROC analysis, which indicated 82.50% success in the diagnosis of epilepsy. As a result, the EFD based probability density approach may be considered as an alternative way to diagnose epilepsy disease on EEG signals. © 2011 IEEE Daha fazlası Daha az

Orhan, Umut | Hekim, Mahmut | Özer, Mahmut

Article | 2011 | JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY26 ( 3 ) , pp.575 - 580

In this study, we proposed a new feature extraction approach based on equal width discretization (EWD) method and used the statistical features obtained by means of this approach as the inputs of multilayer perceptron neural network (MLPNN) model in the detection of epileptic seizure from Electroencephalogram (EEG) signals. For this aim, EEG signals were discretized by EWD method, histograms of the signals were obtained according to the density of each discrete interval, and finally these histograms were used as the inputs of MLPNN models both without any hidden layer and with a hidden layer which has 5 neurons. Both of them detecte . . .d epileptic seizures from EEG signals with high classification success ratios. This result showed that a linear classifier can also solve the problem of epileptic seizure detection by means of the offered feature extraction approach. Consequently, EWD approach may be used as a new feature extraction method in the biomedical signal processing Daha fazlası Daha az

Orhan, Umut | Hekim, Mahmut | Özer, Mahmut

Article | 2011 | Expert Systems with Applications38 ( 10 ) , pp.13475 - 13481

We introduced a multilayer perceptron neural network (MLPNN) based classification model as a diagnostic decision support mechanism in the epilepsy treatment. EEG signals were decomposed into frequency sub-bands using discrete wavelet transform (DWT). The wavelet coefficients were clustered using the K-means algorithm for each frequency sub-band. The probability distributions were computed according to distribution of wavelet coefficients to the clusters, and then used as inputs to the MLPNN model. We conducted five different experiments to evaluate the performance of the proposed model in the classifications of different mixtures of . . . healthy segments, epileptic seizure free segments and epileptic seizure segments. We showed that the proposed model resulted in satisfactory classification accuracy rates. © 2010 Elsevier Ltd. All rights reserved Daha fazlası Daha az

Orhan, Umut | Hekim, Mahmut | Özer, Mahmut

Proceedings | 2010 | 2010 15th National Biomedical Engineering Meeting, BIYOMUT2010 , pp.13475 - 13481

Electroencephalogram (EEG) recording systems have been frequently used as the sources of information in diagnosis of epilepsy by several researchers. In this study, rearranged EEG signals were classified by Multilayer Perceptron Neural Network (MLPNN) model. Used data consists of five groups (A, B, C, D, and E) each containing 100 EEG segments. In this study, center points with equal interval were selected on amplitude axis of each EEG segment. EEG signals were rearranged by way of that each amplitude value was shifted to the center point closest to itself. Equal width discretization (EWD) method was used for rearrangement process. . . .Wavelet coefficients of each segment of EEG signals were computed by using discrete wavelet transform (DWT). The mean, the standard deviation and the entropy of these coefficients was used as the inputs of MLPNN model. The model was protected from the overfitting by cross validation. Two different classification experiments were implemented by the same MLPNN model: 1) the classification of healthy volunteers, epilepsy patients during seizure and epilepsy patients during a seizure-free interval, 2) the classification of epilepsy patients during seizure and seizure-free interval. MLPNN model classified EEG signals with the accuracy of 99.60% in first experiment and 100% in second experiment. It is observed that MLPNN classification of EEG signals after rearrangement in amplitude axis provides better results. ©2010 IEEE Daha fazlası Daha az

Orhan, Umut | Hekim, Mahmut | Özer, Mahmut

Article | 2012 | Journal of Medical Systems36 ( 4 ) , pp.2219 - 2224

In this study, we offered a new feature extraction approach called probability distribution based on equal frequency discretization (EFD) to be used in the detection of epileptic seizure from electroencephalogram (EEG) signals. Here, after EEG signals were discretized by using EFD method, the probability densities of the signals were computed according to the number of data points in each interval. Two different probability density functions were defined by means of the polynomial curve fitting for the subjects without epileptic seizure and the subjects with epileptic seizure, and then when using the mean square error criterion for . . .these two functions, the success of epileptic seizure detection was 96.72%. In addition, when the probability densities of EEG segments were used as the inputs of a multilayer perceptron neural network (MLPNN) model, the success of epileptic seizure detection was 99.23%. This results show that non-linear classifiers can easily detect the epileptic seizures from EEG signals by means of probability distribution based on EFD. © 2011 Springer Science+Business Media, LLC Daha fazlası Daha az

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