Epileptic Seizure Detection using DWT Based Weighted Visibility Graph

Epilepsy is a common neurological disorder of the brain and can be assessed by electroencephalogram (EEG). By combining discrete wavelet transformation (DWT) and weighted visibility graph (WVG), we employed a new approach for detecting epileptic seizure from EEG. First, DWT is applied to decompose E...

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Bibliographic Details
Published in2018 13th World Congress on Intelligent Control and Automation (WCICA) pp. 739 - 743
Main Authors Cai, Lihui, Wang, Jiang, Lu, Meili, Wei, Xile, Yu, Haitao, Wang, Ruofan, Fan, Yaqin
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2018
Subjects
Biomedical signal processing
Continuous wavelet transforms
Discrete wavelet transforms
Electroencephalography
Feature extraction
Sensitivity
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Summary:Epilepsy is a common neurological disorder of the brain and can be assessed by electroencephalogram (EEG). By combining discrete wavelet transformation (DWT) and weighted visibility graph (WVG), we employed a new approach for detecting epileptic seizure from EEG. First, DWT is applied to decompose EEG into different frequency bands, then WVG is constructed from the sub-band signals. Average weighted degree and clustering coefficient are extracted to characterize the topological structure of the networks and distinguish different brain states (healthy, inter-ictal and ictal). Four different test cases are designed to evaluate the performance of our DWT-WVG method. The obtained results demonstrate that both features are effective to discriminate the ictal EEG from interictal EEG or EEG recorded from healthy subjects.
DOI:10.1109/WCICA.2018.8630498