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Epileptic Seizure Detection with EEG Textural Features and Imbalanced Classification Based on EasyEnsemble Learning.

  • Sun, Chengfa1
  • Cui, Hui2
  • Zhou, Weidong3
  • Nie, Weiwei4
  • Wang, Xiuying5
  • Yuan, Qi1
  • 1 Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan 250358, P. R. China. , (China)
  • 2 Department of Computer Science and Information Technology, La Trobe University, Bundoora, VIC 3083, Australia. , (Australia)
  • 3 School of Microelectronics, Shandong University, Jinan 250101, P. R. China. , (China)
  • 4 Shandong Provincial Qianfoshan Hospital, Shandong First Medical University, Jinan 250014, P. R. China. , (China)
  • 5 School of Computer Science, The University of Sydney, Sydney, NSW 2006, Australia. , (Australia)
Published Article
International journal of neural systems
Publication Date
Jul 29, 2019
DOI: 10.1142/S0129065719500217
PMID: 31505978


Imbalance data classification is a challenging task in automatic seizure detection from electroencephalogram (EEG) recordings when the durations of non-seizure periods are much longer than those of seizure activities. An imbalanced learning model is proposed in this paper to improve the identification of seizure events in long-term EEG signals. To better represent the underlying microstructure distributions of EEG signals while preserving the non-stationary nature, discrete wavelet transform (DWT) and uniform 1D-LBP feature extraction procedure are introduced. A learning framework is then designed by the ensemble of weakly trained support vector machines (SVMs). Under-sampling is employed to split the imbalanced seizure and non-seizure samples into multiple balanced subsets where each of them is utilized to train an individual SVM classifier. The weak SVMs are incorporated to build a strong classifier which emphasizes seizure samples and in the meantime analyzing the imbalanced class distribution of EEG data. Final seizure detection results are obtained in a multi-level decision fusion process by considering temporal and frequency factors. The model was validated over two long-term and one short-term public EEG databases. The model achieved a G-mean of 97.14% with respect to epoch-level assessment, an event-level sensitivity of 96.67%, and a false detection rate of 0.86/h on the long-term intracranial database. An epoch-level G-mean of 95.28% and event-level false detection rate of 0.81/h were yielded over the long-term scalp database. The comparisons with 14 published methods demonstrated the improved detection performance for imbalanced EEG signals and the generalizability of the proposed model.

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