Epileptic seizure detection using Multivariate Empirical Mode Decomposition and Support Vector Machines

Automatic detection of epileptic seizures is a very crucial step for diagnosing patients with drug-resistant epilepsies. If visual analysis of long-term electroencephalographic signals is the most reliable technique, automatic seizures detection can help the physicians in comparing seizures and extracting common patterns. In this paper, a new approach to classify background activity and pre-ictal stereoelectroencephalographic signals is proposed. Linear and nonlinear features are extracted directly from the derived intrinsic mode functions of multivariate empirical mode decomposition technique and the classification is performed using support vector machines. The effectiveness of the proposed approach is evaluated using real datasets. Our results show good performance of the proposed approach since an accuracy of 100
^%
is achieved using the first intrinsic mode function and a window size of 1024 samples.