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dc.contributor.authorLiu, Chien-Liangen_US
dc.contributor.authorXiao, Binen_US
dc.contributor.authorHsaio, Wen-Hoaren_US
dc.contributor.authorTseng, Vincent S.en_US
dc.date.accessioned2020-10-05T02:01:56Z-
dc.date.available2020-10-05T02:01:56Z-
dc.date.issued2019-01-01en_US
dc.identifier.issn2169-3536en_US
dc.identifier.urihttp://dx.doi.org/10.1109/ACCESS.2019.2955285en_US
dc.identifier.urihttp://hdl.handle.net/11536/155357-
dc.description.abstractThe 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.en_US
dc.language.isoen_USen_US
dc.subjectElectroencephalographyen_US
dc.subjectFeature extractionen_US
dc.subjectBrain modelingen_US
dc.subjectTime-domain analysisen_US
dc.subjectEpilepsyen_US
dc.subjectTime-frequency analysisen_US
dc.subjectElectroencephalograms (EEG)en_US
dc.subjectseizure predictionen_US
dc.subjectconvolutional neural network (CNN)en_US
dc.subjectmulti-view CNNen_US
dc.subjectrepresentation learningen_US
dc.titleEpileptic Seizure Prediction With Multi-View Convolutional Neural Networksen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ACCESS.2019.2955285en_US
dc.identifier.journalIEEE ACCESSen_US
dc.citation.volume7en_US
dc.citation.spage170352en_US
dc.citation.epage170361en_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.department資訊工程學系zh_TW
dc.contributor.department工業工程與管理學系zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.contributor.departmentDepartment of Industrial Engineering and Managementen_US
dc.identifier.wosnumberWOS:000560454900090en_US
dc.citation.woscount0en_US
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