dc.contributor.author | Wang, Xiaoshuang | |
dc.contributor.author | Ristaniemi, Tapani | |
dc.contributor.author | Cong, Fengyu | |
dc.date.accessioned | 2022-12-12T07:27:30Z | |
dc.date.available | 2022-12-12T07:27:30Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Wang, X., Ristaniemi, T., & Cong, F. (2020). One and Two Dimensional Convolutional Neural Networks for Seizure Detection Using EEG Signals. In <i>EUSIPCO 2020 : 28th European Signal Processing Conference</i> (pp. 1387-1391). IEEE. European Signal Processing Conference. <a href="https://doi.org/10.23919/Eusipco47968.2020.9287640" target="_blank">https://doi.org/10.23919/Eusipco47968.2020.9287640</a> | |
dc.identifier.other | CONVID_47521556 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/84277 | |
dc.description.abstract | Deep learning for the automated detection of epileptic seizures has received much attention during recent years. In this work, one dimensional convolutional neural network (1D-CNN) and two dimensional convolutional neural network (2D-CNN) are simultaneously used on electroencephalogram (EEG) data for seizure detection. Firstly, using sliding windows without overlap on raw EEG to obtain the definite one-dimension time EEG segments (1D-T), and continuous wavelet transform (CWT) for 1D-T signals to obtain the two-dimension time-frequency representations (2D-TF). Then, 1D-CNN and 2D-CNN model architectures are used on 1D-T and 2D-TF signals for automatic classification, respectively. Finally, the classification results from 1D-CNN and 2D-CNN are showed. In the two-classification and three-classification problems of seizure detection, the highest accuracy can reach 99.92% and 99.55%, respectively. It shows that the proposed method for a benchmark clinical dataset can achieve good performance in terms of seizure detection. | en |
dc.format.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.publisher | IEEE | |
dc.relation.ispartof | EUSIPCO 2020 : 28th European Signal Processing Conference | |
dc.relation.ispartofseries | European Signal Processing Conference | |
dc.rights | In Copyright | |
dc.subject.other | electroencephalogram (EEG) | |
dc.subject.other | seizure detection | |
dc.subject.other | convolutional neural networks (CNN) | |
dc.subject.other | deep learning | |
dc.subject.other | time-frequency representationti | |
dc.title | One and Two Dimensional Convolutional Neural Networks for Seizure Detection Using EEG Signals | |
dc.type | conferenceObject | |
dc.identifier.urn | URN:NBN:fi:jyu-202212125535 | |
dc.contributor.laitos | Informaatioteknologian tiedekunta | fi |
dc.contributor.laitos | Faculty of Information Technology | en |
dc.contributor.oppiaine | Tietotekniikka | fi |
dc.contributor.oppiaine | Mathematical Information Technology | en |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | |
dc.relation.isbn | 978-1-7281-5001-7 | |
dc.type.coar | http://purl.org/coar/resource_type/c_5794 | |
dc.description.reviewstatus | peerReviewed | |
dc.format.pagerange | 1387-1391 | |
dc.relation.issn | 2219-5491 | |
dc.type.version | acceptedVersion | |
dc.rights.copyright | © Authors, 2020 | |
dc.rights.accesslevel | openAccess | fi |
dc.relation.conference | European Signal Processing Conference | |
dc.subject.yso | neuroverkot | |
dc.subject.yso | signaalianalyysi | |
dc.subject.yso | signaalinkäsittely | |
dc.subject.yso | koneoppiminen | |
dc.subject.yso | epilepsia | |
dc.subject.yso | EEG | |
dc.format.content | fulltext | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p7292 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p26805 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p12266 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p21846 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p9413 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p3328 | |
dc.rights.url | http://rightsstatements.org/page/InC/1.0/?language=en | |
dc.relation.doi | 10.23919/Eusipco47968.2020.9287640 | |
jyx.fundinginformation | This work was supported by the National Natural Science Foundationof China (Grant No. 91748105&81471742), the Fundamental ResearchFunds for the Central Universities [DUT2019] in Dalian University ofTechnology in China and the scholarships from China Scholarship Council(No. 201806060166). | |
dc.type.okm | A4 | |