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- Title
EEG İŞARETLERİNİN DALGACIK SİNİR AĞI İLE SINIFLANDIRILMASI.
- Authors
Subaşi, Abdulhamit; Alkan, Ahmet; Koklükaya, Etem
- Abstract
Since EEG is one of the most important sources of information in therapy of epilepsy, several researchers tried to address the issue of decision support for such a data. We present a novel method for classifying epilepsy of full spectrum EEG recordings. This novel method uses autoregressive (AR) model by using maximum likelihood estimation (MLE) of EEG signals as an input to a wavelet neural networks (WNNs) with two discrete outputs: epileptic seizure or non-epileptic seizure. By identifying features in the signal we want to provide an automatic system that will support a physician in the diagnosing process. By applying AR with MLE in connection with wavelet neural network, a novel and reliable classifier architecture is obtained. The network is constructed by the error backpropagation neural network using Morlet mother wavelet basic function as node activation function. It is observed that, WNN classification of EEG signals gives better results and these results can also be used for diagnosis of diseases.
- Subjects
ELECTROENCEPHALOGRAPHY; THERAPEUTICS; EPILEPSY; SPASMS; PHYSICIANS; DISEASES; BACK propagation; ERRORS; MODELS &; modelmaking
- Publication
Teknoloji, 2004, Vol 7, Issue 1, p71
- ISSN
1302-0056
- Publication type
Article