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• W2591830448 abstract "Epilepsy is increasingly occurred disease in the modern world, and the use of automatic detection technology of epileptic Electroencephalogram (EEG) signals is more and more important. In this essay, a novel approach named CC-ELM of automatic epileptic EEG signals detection is proposed. Unlike traditional dimension reducing methods of most current automatic detection, the proposed approach adopts biclustering to perform an unsupervised dimension reduction, which is more suitable to the characteristic of EEG signals. To verify the performance of the presented approach, experiments have been carried out in the epileptic EEG data. The average sensitivity, specificity and recognition accuracy obtained by our method are 96.67%, 100.00% and 98.00%. The study might be meaningful for improving the diagnostic accuracy of epileptic disease, relieving the workload of doctors and reducing the medical cost." @default.
• W2591830448 created "2017-03-16" @default.
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• W2591830448 date "2016-07-01" @default.
• W2591830448 modified "2023-09-23" @default.
• W2591830448 title "A novel approach for epileptic EEG signals classification based on biclustering technique" @default.
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• W2591830448 doi "https://doi.org/10.1109/icmlc.2016.7872982" @default.
• W2591830448 hasPublicationYear "2016" @default.
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