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Towards Brain Big Data Classification: Epileptic EEG Identification with a Lightweight VGGNet on Global MIC

Lookup NU author(s): Dr Tejal Shah, Professor Raj Ranjan



This is the final published version of an article that has been published in its final definitive form by IEEE, 2018.

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OAPA Brain big data empowered by intelligent analysis provide an unrivalled opportunity to probe the dynamics of the brain in disorder. A typical example is to identify evolving synchronization patterns from multivariate electroencephalography (EEG) routinely superimposed with intensive noise in epilepsy research and practice. Under the circumstance of insufficient a priori knowledge of subject dependency on domain problem, it becomes even more important to adaptively classify the synchronization dynamics to accurately characterize the intrinsic nature of seizure activities represented by the EEG. This study first measures the global Maximal Information coefficient (MIC) of all EEG data channels to form a time sequence of correlation matrices. A lightweight VGGNet is designed to adapt to the need to prune massive EEG datasets. The VGGNet characterizes the synchronization dynamics captured in the correlation matrices and then automatically identifies the seizure states of the EEG. Experiments are performed over the CHB-MIT scalp EEG dataset to evaluate the proposed approach. Seizure states can be identified with an accuracy, sensitivity, and specificity of 98.13% ± 0.24%, 98.85% ± 0.51%, and 97.47% ± 0.36%, respectively; the resulting performance is superior to those of most existing methods over the same dataset. The approach directly applies to raw EEG analysis, which holds great potential for handling brain big data.

Publication metadata

Author(s): Ke H, Chen D, Li X, Tang Y, Shah T, Ranjan R

Publication type: Article

Publication status: Published

Journal: IEEE Access

Year: 2018

Volume: 6

Pages: 14722-14733

Online publication date: 01/03/2018

Acceptance date: 01/04/2016

Date deposited: 23/03/2018

ISSN (electronic): 2169-3536

Publisher: IEEE


DOI: 10.1109/ACCESS.2018.2810882


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