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BioMed Research International
Volume 2014 (2014), Article ID 703816, 10 pages
Research Article

Robust Deep Network with Maximum Correntropy Criterion for Seizure Detection

1Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China
2Department of Computer Science, Zhejiang University, Hangzhou 310027, China
3Second Affiliated Hospital of Zhejiang University, College of Medicine, Hangzhou 310000, China
4Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China

Received 27 March 2014; Accepted 4 June 2014; Published 6 July 2014

Academic Editor: Ting Zhao

Copyright © 2014 Yu Qi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Effective seizure detection from long-term EEG is highly important for seizure diagnosis. Existing methods usually design the feature and classifier individually, while little work has been done for the simultaneous optimization of the two parts. This work proposes a deep network to jointly learn a feature and a classifier so that they could help each other to make the whole system optimal. To deal with the challenge of the impulsive noises and outliers caused by EMG artifacts in EEG signals, we formulate a robust stacked autoencoder (R-SAE) as a part of the network to learn an effective feature. In R-SAE, the maximum correntropy criterion (MCC) is proposed to reduce the effect of noise/outliers. Unlike the mean square error (MSE), the output of the new kernel MCC increases more slowly than that of MSE when the input goes away from the center. Thus, the effect of those noises/outliers positioned far away from the center can be suppressed. The proposed method is evaluated on six patients of 33.6 hours of scalp EEG data. Our method achieves a sensitivity of 100% and a specificity of 99%, which is promising for clinical applications.