Towards Brain Big Data Classification: Epileptic EEG Identification With a Lightweight VGGNet on Global MIC
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...
| Main Authors: | , , , , , |
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| Format: | Article |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8305463/ |
| _version_ | 1818558632857436160 |
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| author | Hengjin Ke Dan Chen Xiaoli Li Yunbo Tang Tejal Shah Rajiv Ranjan |
| author_facet | Hengjin Ke Dan Chen Xiaoli Li Yunbo Tang Tejal Shah Rajiv Ranjan |
| author_sort | Hengjin Ke |
| collection | DOAJ |
| description | 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 paper first measures the global maximal information coefficient (MIC) of all EEG data channels to form a time sequence of correlation matrices. A lightweight VGGNet (Visual Geometry Group) 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 Children's Hospital Boston-Massachusetts Institute of Technology (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. |
| first_indexed | 2024-12-14T00:14:50Z |
| format | Article |
| id | doaj.art-d52129cc941e4999885df6e9f8952282 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T00:14:50Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d52129cc941e4999885df6e9f89522822022-12-21T23:25:35ZengIEEEIEEE Access2169-35362018-01-016147221473310.1109/ACCESS.2018.28108828305463Towards Brain Big Data Classification: Epileptic EEG Identification With a Lightweight VGGNet on Global MICHengjin Ke0Dan Chen1https://orcid.org/0000-0002-7055-141XXiaoli Li2Yunbo Tang3Tejal Shah4Rajiv Ranjan5Computer School, Wuhan University, Wuhan, ChinaComputer School, Wuhan University, Wuhan, ChinaNational Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, ChinaComputer School, Wuhan University, Wuhan, ChinaNewcastle University, Newcastle upon Tyne, U.K.Newcastle University, Newcastle upon Tyne, U.K.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 paper first measures the global maximal information coefficient (MIC) of all EEG data channels to form a time sequence of correlation matrices. A lightweight VGGNet (Visual Geometry Group) 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 Children's Hospital Boston-Massachusetts Institute of Technology (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.https://ieeexplore.ieee.org/document/8305463/Brain big datapattern classificationVGGNetsynchronization measurementEEGepilepsy |
| spellingShingle | Hengjin Ke Dan Chen Xiaoli Li Yunbo Tang Tejal Shah Rajiv Ranjan Towards Brain Big Data Classification: Epileptic EEG Identification With a Lightweight VGGNet on Global MIC IEEE Access Brain big data pattern classification VGGNet synchronization measurement EEG epilepsy |
| title | Towards Brain Big Data Classification: Epileptic EEG Identification With a Lightweight VGGNet on Global MIC |
| title_full | Towards Brain Big Data Classification: Epileptic EEG Identification With a Lightweight VGGNet on Global MIC |
| title_fullStr | Towards Brain Big Data Classification: Epileptic EEG Identification With a Lightweight VGGNet on Global MIC |
| title_full_unstemmed | Towards Brain Big Data Classification: Epileptic EEG Identification With a Lightweight VGGNet on Global MIC |
| title_short | Towards Brain Big Data Classification: Epileptic EEG Identification With a Lightweight VGGNet on Global MIC |
| title_sort | towards brain big data classification epileptic eeg identification with a lightweight vggnet on global mic |
| topic | Brain big data pattern classification VGGNet synchronization measurement EEG epilepsy |
| url | https://ieeexplore.ieee.org/document/8305463/ |
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