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


  • Ayad Baziyad Department of Electrical Engineering, King Saud University, Riyadh 11421, Saudi Arabia


Electrocardiogram signals, Support Vector Machine, Epilepsy Seizure, Phase Space Reconstructions


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%).


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How to Cite

Baziyad, A. (2019). Detection of Epileptic Seizure Based on Phase Space Reconstruction and Support Vector Machine. American Scientific Research Journal for Engineering, Technology, and Sciences, 61(1), 45–53. Retrieved from