Abstract
AbstractThe Electroencephalogram (EEG) signal is made up of several frequency bands that describe human behaviours such as emotion, attention, sleep state, and so on. It is necessary to do categorization on the basis of distinct EEG segments in order to detect epileptic seizures. Short-Time Fourier Transform (STFT) is used to analyze the performance of the gamma band in an EEG signal. It also compares various classification approaches and shows that some classification algorithms attain very high accuracy. The analysis was carried out in stages, including STFT, gamma frequency band extraction, statistical feature extraction, and at last classification is performed using an ANN classifier. This work uses STFT to extract statistical properties from collected two-dimensional data and classify epilepsy in the high-frequency region. The proposed Artificial Neural Network (ANN) classifier got 91.3% accuracy rate.KeywordsANN classifierEEGGamma bandShort-time fourier transformSeizures
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