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EEG seizure detection by integrating slantlet transform with sparse coding

EEG signals, recording both normal and abnormal activities of neurons in the brain, are widely used in epilepsy detection. In this paper, an EEG signal classification method based on Slantlet Transform and sparse coding is proposed to greatly reduce number of false alarms and improve speed of detection. With Slantlet Transform, salient information of EEG signals is mapped into a sparse space. In order to ensure good detection rates from the EEG signals, a Sparse Representation Classifier is adopted. Experimental results demonstrate that the in terms of detection rate, the proposed method, which integrates Slantlet Transform with sparse coding, outperforms the current state-of-the-art EEG seizure detection methods.