Robust Deep Network with Maximum Correntropy Criterion for Seizure Detection

Joint Authors

Zhang, Jianmin
Wang, Yueming
Zheng, Xiao-xiang
Zhu, Junming
Qi, Yu

Source

BioMed Research International

Issue

Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2014-07-03

Country of Publication

Egypt

No. of Pages

10

Main Subjects

Medicine

Abstract EN

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.

American Psychological Association (APA)

Qi, Yu& Wang, Yueming& Zhang, Jianmin& Zhu, Junming& Zheng, Xiao-xiang. 2014. Robust Deep Network with Maximum Correntropy Criterion for Seizure Detection. BioMed Research International،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-491856

Modern Language Association (MLA)

Qi, Yu…[et al.]. Robust Deep Network with Maximum Correntropy Criterion for Seizure Detection. BioMed Research International No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-491856

American Medical Association (AMA)

Qi, Yu& Wang, Yueming& Zhang, Jianmin& Zhu, Junming& Zheng, Xiao-xiang. Robust Deep Network with Maximum Correntropy Criterion for Seizure Detection. BioMed Research International. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-491856

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-491856