PREDICTION OF EPILEPSY BASED ON EEMD AND LSSVM DOUBLE CLASSIFICATION

Abstract

Epilepsy seizures are caused by abnormal, excessive, or synchronized neuronal activity in the brain, which is difficult to treat and is extremely stubborn. Therefore, studying the activity of epilepsy can greatly contribute to its diagnosis and treatment. The original signal is decomposed into IMFs and residual by ensemble empirical mode decomposition (EEMD), and then the first three intrinsic mode functions (IMF) are selected to replace the original signal, and the nonlinear and non-stationary problems of the original signal are solved. The Least Squares Support Vector Machine (LSSVM) was used as the classifier, its parameters (gam and sig2) are optimized by Particle Swarm Optimization (PSO). The experiment used the EEG database published by the University of Bonn (UoB) to realize the classification of normal, interictal and ictal periods. When PSO was employed, the recognition accuracy of the test set was 93.33%, with a classification time of 0.035 s and the Information Transfer Rate (ITR) of 3.77 bpm in training 70 classes with 100 samples each. In contrast, without PSO, the recognition accuracy of the test set was 92%, with a classification time of 0.039 s and the ITR of 2.88 bpm without PSO in training 70 classes with 100 samples each. The experimental results show that EEMD and LSSVM can effectively implement the three-classification problem and provide an effective means for the onset prediction of epilepsy patients.