Classification of epileptic cerebral activity using robust features and support vector machines

Epileptic seizure detection requires the study of electroencephalogram (EEG) data. Visual marking of seizure onset in such EEG recordings is quite tedious, naturally subjective, extremely time consuming, and it may lead to inaccurate detection. Thus, the development of a robust framework for automatic seizure classification is necessary and can be very useful in epilepsy investigation. In this paper, a classical method has been improved. Our contribution includes the use of linear and non linear features which have been incorporated into the Support Vector Machines (SVM) classifier. Accordingly, the detection performance has been compared using both radial basis functions (RBF) and linear SVM kernels. Our main finding reveals that the system can correctly classify the EEG data with an average sensitivity of 99.68%, an average specificity of 99.81% and an average accuracy of 99.75%, while 100% of sensitivity, specificity and accuracy are also achieved in single-trial classification. A final comparison between the performance levels obtained with our method and those obtained with previous techniques is undertaken to prove the effectiveness of our method for seizure detection.