Abstract
The detection of epileptic seizures by electroencephalography (EEG) signals has become a standard method for the diagnosis of epilepsy. Accurate and automatic detection of epileptic seizures is needed since manual identification of epileptic seizures by specialist neurologists is a time consuming and labor intensive process, which also leads to various errors. For this purpose, frequency-based features were extracted from the EEG signal and a various classifiers based on ensemble learning was used to detect epileptic seizures automatically. The performance of the proposed method was tested using cross-validation and cross-patient experiments. According to the experimental results, sensitivity, specificity and accuracy rates were 94%, 93% and 93% for cross-validation and 76%, 90% and 90% for cross-patients, respectively.
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