Abstract
Epileptic seizure detection is the most important part in the diagnosis of epilepsy. The automatic detection and classification of epileptic EEG signals has great clinical significance. This paper proposes a novel method for epileptic seizure detection using empirical mode decomposition (EMD) and sparse representation based classification (SRC). Firstly, EMD was used to decompose EEG into multiple Intrinsic Mode Function (IMF) components. Secondly, the features like variation coefficient, fluctuation index, relative energy and relative amplitude were extracted from the IMFs. Finally, in the framework of sparse representation based classification (SRC), the feature vectors of test sample were represented as a linear combination of the feature vector of training samples with sparse coefficients. Experimental results show that the time consumed by one epileptic EEG test sample is not more than 5.9 s, and the accuracy is up to 97.5%. In SRC, the raw EEG signals were replaced by extracted features, which could reduce data dimension and computational cost. The algorithm has a good performance in the recognition of ictal EEG. The higher recognition rate and fast speed make the method suit for the diagnosis of epilepsy in clinical application.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant No. 61671220, 61640218, 61201428), the Shandong Distinguished Middle-aged and Young Scientist Encourage and Reward Foundation, China (Grant No. ZR2016FB14), the Project of Shandong Province Higher Educational Science and Technology Program, China (Grant No. J16LN07), the Shandong Province Key Research and Development Program, China (Grant No. 2016GGX101022).
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Meng, Q., Chen, S., Liu, H., Liu, Y., Wang, D. (2017). Detection of Epileptic Seizure in EEG Using Sparse Representation and EMD. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10262. Springer, Cham. https://doi.org/10.1007/978-3-319-59081-3_60
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DOI: https://doi.org/10.1007/978-3-319-59081-3_60
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