Detection of Epileptic Seizure Based on Phase Space Reconstruction and Support Vector Machine

Electroencephalogram (EEG) is an important brain signal for disease diagnosis. Automated detection of epilepsy is still an open field for research. In this study, a simulation of epilepsy detection approach is achieved by a combination of feature extraction and classification algorithms. The features were extracted using phase space reconstruction, and classified by support vector machine.  The performance evaluation was tested using dataset available by University of Bonn. The results of our experiments showed excellent classification accuracy (100%), sensitivity (100%) and specificity (99%)