An Efficient Adaptive Segmentation Algorithm on EEG Signals to Discriminate between Subject with Epilepsy and Normal Control

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In this study, an effective approach was presented to help distinguish between patients with Epilepsy Disease (ED) and the control group using their electroencephalogram (EEG) signals. The closer examination of these signals clearly showed the non-stationary behavior over long periods. In most real-time researches, the window function was applied to segment the non-stationary signal into stationary signal with the short window length. However, the purpose of this study was to find the stationary intervals using the instantaneous frequency (IF) concept. Several time-frequency transforms were applied to segment the EEG signals in the ED group and control group. The kernel principal component analysis (KPCA), full-rank KPCA and low-rank KPCA were applied to increase the accuracy rate and decrease the complexity. Then, the projected features were used in the artificial neural network (ANN) classifier. The results showed that the novel technique was able to distinguish between the subjects with epilepsy and the control group with an accuracy rate of 93%.
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EEG; Seizure; Kernel PCA; Full-Rank KPCA; Low-Rank KPCA; IF; Time-Frequency Transforms; ANN

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