Automated detection of epileptic EEGs using a novel fusion feature and extreme learning machine

Automated seizure detection using EEG has gained increasing attraction in recent years and appeared more and more helpful in both diagnosis and treatment. How to design an appropriate feature extraction method and how to select an efficient classifier are recognized to be crucial in the successful realization. This paper first proposes a new Mahalanobis-similarity-based feature extraction method on the basis of the Mahalanobis distance and discrete wavelet transformation (DWT). Then in order to further improve the performance, this paper designs a fusion feature (MS-SE-FF) in the feature-fusion level, where the Mahalanobis-similarity-based feature characterizing the similarity between signals and the sample-entropy-based feature characterizing the complexity of signals are combined together. Finally, an automated seizure detection method FF-ELM-SD has been built, which is integrated between the novel fusion feature MS-SE-FF and extreme learning machine (ELM). Experimental results demonstrate that the proposed method FF-ELM-SD does a good job in the epileptic seizure detection while preserving the efficiency and simplicity.