Epilepsy EEG Seizure Prediction Based on the Combination of Graph Convolutional Neural Network Combined with Long- and Short-Term Memory Cell Network Article Swipe
Zhejun Kuang
,
Simin Liu
,
Jian Zhao
,
Liu Wang
,
Yunkai Li
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/app142411569
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/app142411569
With the increasing research of deep learning in the EEG field, it becomes more and more important to fully extract the characteristics of EEG signals. Traditional EEG signal classification prediction neither considers the topological structure between the electrodes of the signal collection device nor the data structure of the Euclidean space to accurately reflect the interaction between signals. Graph neural networks can effectively extract features of non-Euclidean spatial data. Therefore, this paper proposes a feature selection method for epilepsy EEG classification based on graph convolutional neural networks (GCNs) and long short-term memory (LSTM) cells. While enriching the input of LSTM, it also makes full use of the information hidden in the EEG signals. In the automatic detection of epileptic seizures based on neural networks, due to the strong non-stationarity and large background noise of the EEG signal, the analysis and processing of the EEG signal has always been a challenging research. Therefore, experiments were conducted using the preprocessed Boston Children’s Hospital epilepsy EEG dataset, and input it into the GCN-LSTM model for deep feature extraction. The GCN network built by the graph convolution layer learns spatial features, then LSTM extracts sequence information, and the final prediction is performed by fully connected and softmax layers. The introduced method has been experimentally proven to be effective in improving the accuracy of epileptic EEG seizure detection. Experimental results show that the average accuracy of binary classification on the CHB-MIT dataset is 99.39%, and the average accuracy of ternary classification is 98.69%.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app142411569
- https://www.mdpi.com/2076-3417/14/24/11569/pdf?version=1733921344
- OA Status
- gold
- Cited By
- 6
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405283909
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- OpenAlex ID
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https://openalex.org/W4405283909Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app142411569Digital Object Identifier
- Title
-
Epilepsy EEG Seizure Prediction Based on the Combination of Graph Convolutional Neural Network Combined with Long- and Short-Term Memory Cell NetworkWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-12-11Full publication date if available
- Authors
-
Zhejun Kuang, Simin Liu, Jian Zhao, Liu Wang, Yunkai LiList of authors in order
- Landing page
-
https://doi.org/10.3390/app142411569Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/14/24/11569/pdf?version=1733921344Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2076-3417/14/24/11569/pdf?version=1733921344Direct OA link when available
- Concepts
-
Computer science, Pattern recognition (psychology), Electroencephalography, Artificial intelligence, Convolutional neural network, Feature extraction, Graph, Binary classification, Epilepsy, Softmax function, Support vector machine, Neuroscience, Theoretical computer science, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 6Per-year citation counts (last 5 years)
- References (count)
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35Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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