Abstract
Epilepsy is a common neurological disease, and it is usually judged based on EEG signals. Automatic detection and classification of epileptic EEG gradually get more and more attention. In this work, we adopt two-step program to implement the automatic classification. Three entropies (approximate entropy, sample entropy, permutation entropy) are extracted as features to prepare for classifying. Then extreme learning machine is utilized to realize feature classification. Experimental results on Bonn epilepsy EEG dataset indicate that the proposed method is capable of recognizing normal, pre-ictal and ictal EEG with an accuracy of 99.31%, which is helpful for doctors to diagnose epilepsy disease.
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Acknowledgments
This work was supported by the project (61374154) of the National Nature Science Foundation of China and the Fundamental Research Funds for the Central Universities (DUT16RC (3)123).
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Cheng, X., Xu, M., Han, M. (2017). Automatic Detection of Epileptic Seizures Based on Entropies and Extreme Learning Machine. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_43
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DOI: https://doi.org/10.1007/978-3-319-70093-9_43
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