- Title
- A weighted overlook graph representation of eeg data for absence epilepsy detection
- Creator
- Wang, Jialin; Liang, Shen; Wang, Ye; Zhang, Yanchun; Ma, Jiangang
- Date
- 2020
- Type
- Text; Conference proceedings; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/175593
- Identifier
- vital:15026
- Identifier
-
https://doi.org/10.1109/ICDM50108.2020.00067
- Identifier
- ISBN:1550-4786 (ISSN); 9781728183169 (ISBN)
- Abstract
- Absence epilepsy is one of the most common types of epilepsy. The diagnosis of absence epilepsy is among the greatest challenges faced by clinical neurologists due to a lack of easily observable symptoms that are present in conventional epilepsy (e.g. spasm and convulsion), and highly relies on the detection of Spike and Slow Waves (SSWs) in Electroencephalogram (EEG) signals. Recently, graph representations called complex networks have been increasingly applied to characterizing 1D EEG signals. However, existing methods often fail to effectively represent SSWs, struggling to capture the differences between SSW waveforms and their non-SSW counterparts, such as minute differences and distinct shapes. Addressing this issue, in this work, we propose two simple yet effective complex networks, Overlook Graph (OG) and Weighted Overlook Graph (WOG), which have been customized to expressively represent SSWs. Built upon OG and WOG, we then develop a 2D Convolutional Neural Network (2D-CNN) to further learn latent features from the graph representations and accomplish the detection task. Extensive experiments on a real-world absence epilepsy EEG dataset show that the proposed OG/WOG-2D-CNN method can accurately detect SSWs. Additional experiments on the well-known Bonn dataset further show that our method can generalize to the conventional epilepsy seizure detection task with highly competitive performances. © 2020 IEEE. *Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate "Jiangang Ma“ is provided in this record**
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- 20th IEEE International Conference on Data Mining, ICDM 2020 Vol. 2020-November, p. 581-590
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ 2021 IEEE - All rights reserved.
- Subject
- Absence epilepsy; Complex networks; EEG; Spike and Slow Waves
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