فهرست مقالات Epileptic Seizure

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  1 - Noise elimination in automatic detection of epileptic seizures by wavelet transform using feature selection algorithm
  akram asghari govar
  One of the most important symptoms of epilepsy is convulsions, whose detailed analysis is performed by electroencephalography (EEG) signal. Electroencephalogram, as a clinical tool to illustrate the electrical activities of the brain accurately, provides an appropriate چکیده کامل

  One of the most important symptoms of epilepsy is convulsions, whose detailed analysis is performed by electroencephalography (EEG) signal. Electroencephalogram, as a clinical tool to illustrate the electrical activities of the brain accurately, provides an appropriate method for diagnosing epilepsy disorders, which plays an important role in identifying this disease, especially seizures. Seizures resulting from epilepsy may have negative physical, psychological, and social consequences such as loss of consciousness and sudden death. With timely and correct identification of epilepsy, its effect can be treated with medicine or surgery. In this thesis, a brief review of the methods of identifying epilepsy using EEG signal analysis along with the separation of epileptic signals from healthy and normal signals has been done. Methods based on EEG analysis, from non-linear methods of signal processing, provide much better results due to the properties of signal dynamics پرونده مقاله
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  2 - A New Approach in Epilepsy Diagnosis using Discrete Wavelet Transformation and Analysis of Variance
  Tayebeh Iloon Ramin Barati Hamid Azad
  Epilepsy is a chronic disorder and outbreak of brain function, caused by the abnormal and intermittent electric discharge of brain neurons. Electroencephalogram signals represent brain activities, and one of the methods of diagnosing epilepsy is using EEG brain signals. چکیده کامل

  Epilepsy is a chronic disorder and outbreak of brain function, caused by the abnormal and intermittent electric discharge of brain neurons. Electroencephalogram signals represent brain activities, and one of the methods of diagnosing epilepsy is using EEG brain signals. In this article, a new method for diagnosing epilepsy using EEG signal processing is presented. At first, the EEG signal is divided into five frequency sub-bands using Discrete Wavelet Transformation (DWT). Then, the features are extracted from five frequency sub-bands, and the best features are selected by the analysis of variance (ANOVA) method. Finally, by using the Support Vector Machine (SVM) algorithm, these features are used to classify seizure and non-seizure EEG signals. The simulation results from the Bonn university dataset affirm the suggested approach's advantage in comparison with some other basic classical methods in terms of accuracy, sensitivity, and specificit. پرونده مقاله