Epilepsy and seizure detection using statistical features in the Complete Ensemble Empirical Mode Decomposition domain

In this paper, we introduce Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to devise an effective feature extraction scheme for physiological signal analysis. Unlike its predecessors- Empirical Mode Decomposition and Ensemble Empirical Mode Decomposition, CEEMDAN resolves mode mixing problem and gives better spectral separation of the modes. To demonstrate the effectiveness of CEEMDAN based features, we apply CEEMDAN to propose an automatic epileptic seizure detection algorithm. In this work, various statistical features are extracted from the EEG signal segments decomposed by CEEMDAN and seizure classification is performed using artificial neural network. The efficacy of our feature extraction scheme is validated by statistical and graphical analyses. The overall performance of our seizure detection scheme as compared to the state-of-the-art ones is also promising.