Epileptic Seizure Prediction With Multi-View Convolutional Neural Networks
The unpredictability of seizures is often considered by patients to be the most problematic aspect of epilepsy, so this work aims to develop an accurate epilepsy seizure predictor, making it possible to enable devices to warn patients of impeding seizures. To develop a model for seizure prediction,...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8910555/ |
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| author | Chien-Liang Liu Bin Xiao Wen-Hoar Hsaio Vincent S. Tseng |
| author_facet | Chien-Liang Liu Bin Xiao Wen-Hoar Hsaio Vincent S. Tseng |
| author_sort | Chien-Liang Liu |
| collection | DOAJ |
| description | The unpredictability of seizures is often considered by patients to be the most problematic aspect of epilepsy, so this work aims to develop an accurate epilepsy seizure predictor, making it possible to enable devices to warn patients of impeding seizures. To develop a model for seizure prediction, most studies relied on Electroencephalograms (EEGs) to capture physiological measurements of epilepsy. This work uses the two domains of EEGs, including frequency domain and time domain, to provide two different views for the same data source. Subsequently, this work proposes a multi-view convolutional neural network framework to predict the occurrence of epilepsy seizures with the goal of acquiring a shared representation of time-domain and frequency-domain features. By conducting experiments on Kaggle data set, we demonstrated that the proposed method outperforms all methods listed in the Kaggle leader board. Additionally, our proposed model achieves average area under the curve (AUCs) of 0.82 and 0.89 on two subjects of CHB-MIT scalp EEG data set. This work serves as an effective paradigm for applying deep learning approaches to the crucial topic of risk prediction in health domains. |
| format | Article |
| id | doaj-art-97ff43fe4e1f490c87417cda4e4b6ceb |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-97ff43fe4e1f490c87417cda4e4b6ceb2025-08-20T03:06:30ZengIEEEIEEE Access2169-35362019-01-01717035217036110.1109/ACCESS.2019.29552858910555Epileptic Seizure Prediction With Multi-View Convolutional Neural NetworksChien-Liang Liu0https://orcid.org/0000-0002-2724-7199Bin Xiao1https://orcid.org/0000-0003-1992-4214Wen-Hoar Hsaio2https://orcid.org/0000-0002-4439-676XVincent S. Tseng3https://orcid.org/0000-0001-5449-7700Department of Industrial Engineering and Management, National Chiao Tung University, Hsinchu, TaiwanDepartment of Computer Science, National Chiao Tung University, Hsinchu, TaiwanInformation Management Center, National Chung-Shan Institute of Science and Technology, Taoyuan City, TaiwanDepartment of Computer Science, National Chiao Tung University, Hsinchu, TaiwanThe unpredictability of seizures is often considered by patients to be the most problematic aspect of epilepsy, so this work aims to develop an accurate epilepsy seizure predictor, making it possible to enable devices to warn patients of impeding seizures. To develop a model for seizure prediction, most studies relied on Electroencephalograms (EEGs) to capture physiological measurements of epilepsy. This work uses the two domains of EEGs, including frequency domain and time domain, to provide two different views for the same data source. Subsequently, this work proposes a multi-view convolutional neural network framework to predict the occurrence of epilepsy seizures with the goal of acquiring a shared representation of time-domain and frequency-domain features. By conducting experiments on Kaggle data set, we demonstrated that the proposed method outperforms all methods listed in the Kaggle leader board. Additionally, our proposed model achieves average area under the curve (AUCs) of 0.82 and 0.89 on two subjects of CHB-MIT scalp EEG data set. This work serves as an effective paradigm for applying deep learning approaches to the crucial topic of risk prediction in health domains.https://ieeexplore.ieee.org/document/8910555/Electroencephalograms (EEG)seizure predictionconvolutional neural network (CNN)multi-view CNNrepresentation learning |
| spellingShingle | Chien-Liang Liu Bin Xiao Wen-Hoar Hsaio Vincent S. Tseng Epileptic Seizure Prediction With Multi-View Convolutional Neural Networks IEEE Access Electroencephalograms (EEG) seizure prediction convolutional neural network (CNN) multi-view CNN representation learning |
| title | Epileptic Seizure Prediction With Multi-View Convolutional Neural Networks |
| title_full | Epileptic Seizure Prediction With Multi-View Convolutional Neural Networks |
| title_fullStr | Epileptic Seizure Prediction With Multi-View Convolutional Neural Networks |
| title_full_unstemmed | Epileptic Seizure Prediction With Multi-View Convolutional Neural Networks |
| title_short | Epileptic Seizure Prediction With Multi-View Convolutional Neural Networks |
| title_sort | epileptic seizure prediction with multi view convolutional neural networks |
| topic | Electroencephalograms (EEG) seizure prediction convolutional neural network (CNN) multi-view CNN representation learning |
| url | https://ieeexplore.ieee.org/document/8910555/ |
| work_keys_str_mv | AT chienliangliu epilepticseizurepredictionwithmultiviewconvolutionalneuralnetworks AT binxiao epilepticseizurepredictionwithmultiviewconvolutionalneuralnetworks AT wenhoarhsaio epilepticseizurepredictionwithmultiviewconvolutionalneuralnetworks AT vincentstseng epilepticseizurepredictionwithmultiviewconvolutionalneuralnetworks |