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dc.contributor.authorWang, Xiaoshuang
dc.contributor.authorWang, Xiulin
dc.contributor.authorLiu, Wenya
dc.contributor.authorChang, Zheng
dc.contributor.authorKärkkäinen, Tommi
dc.contributor.authorCong, Fengyu
dc.date.accessioned2021-08-18T06:37:54Z
dc.date.available2021-08-18T06:37:54Z
dc.date.issued2021
dc.identifier.citationWang, X., Wang, X., Liu, W., Chang, Z., Kärkkäinen, T., & Cong, F. (2021). One Dimensional Convolutional Neural Networks for Seizure Onset Detection Using Long-term Scalp and Intracranial EEG. <i>Neurocomputing</i>, <i>459</i>, 212-222. <a href="https://doi.org/10.1016/j.neucom.2021.06.048" target="_blank">https://doi.org/10.1016/j.neucom.2021.06.048</a>
dc.identifier.otherCONVID_97894903
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/77411
dc.description.abstractEpileptic seizure detection using scalp electroencephalogram (sEEG) and intracranial electroencephalogram (iEEG) has attracted widespread attention in recent two decades. The accurate and rapid detection of seizures not only reflects the efficiency of the algorithm, but also greatly reduces the burden of manual detection during long-term electroencephalogram (EEG) recording. In this work, a stacked one-dimensional convolutional neural network (1D-CNN) model combined with a random selection and data augmentation (RS-DA) strategy is proposed for seizure onset detection. Firstly, we segmented the long-term EEG signals using 2-sec sliding windows. Then, the 2-sec interictal and ictal segments were classified by the stacked 1D-CNN model. During model training, a RS-DA strategy was applied to solve the problem of sample imbalance, and the patient-specific model was trained with event-based K-fold (K is the number of seizures per patient) cross validation for detecting all seizures of each patient. Finally, we evaluated the performances of the proposed approach in the two levels: the segment-based level and the event-based level. The proposed method was tested on two long-term EEG datasets: the CHB-MIT sEEG dataset and the SWEC-ETHZ iEEG dataset. For the CHB-MIT sEEG dataset, we achieved 88.14 sensitivity, 99.62 specificity and 99.54 accuracy in the segment-based level. From the perspective of the event-based level, 99.31 sensitivity, 0.2/h false detection rate (FDR) and mean 8.1-sec latency were achieved. For the SWEC-ETHZ iEEG dataset, in the segment-based level, 90.09 sensitivity, 99.81 specificity and 99.73 accuracy were obtained. In the event-based level, 97.52 sensitivity, 0.07/h FDR and mean 13.2-sec latency were attained. From these results, we can see that our method can effectively use both sEEG and iEEG data to detect epileptic seizures, and this may provide a reference for the clinical application of seizure onset detection.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofseriesNeurocomputing
dc.rightsCC BY 4.0
dc.subject.otherepilepsy
dc.subject.otherseizure detection
dc.subject.otherscalp electroencephalogram (sEEG)
dc.subject.otherintracranial electroencephalogram (iEEG)
dc.subject.otherconvolutional neural networks (CNN).
dc.titleOne Dimensional Convolutional Neural Networks for Seizure Onset Detection Using Long-term Scalp and Intracranial EEG
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202108184574
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange212-222
dc.relation.issn0925-2312
dc.relation.volume459
dc.type.versionpublishedVersion
dc.rights.copyright© 2021 the Authors
dc.rights.accesslevelopenAccessfi
dc.subject.ysoneuroverkot
dc.subject.ysoepilepsia
dc.subject.ysoEEG
dc.subject.ysosignaalinkäsittely
dc.subject.ysosignaalianalyysi
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p7292
jyx.subject.urihttp://www.yso.fi/onto/yso/p9413
jyx.subject.urihttp://www.yso.fi/onto/yso/p3328
jyx.subject.urihttp://www.yso.fi/onto/yso/p12266
jyx.subject.urihttp://www.yso.fi/onto/yso/p26805
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1016/j.neucom.2021.06.048
jyx.fundinginformationThis work was supported by National Natural Science Foundation of China (Grant No. 91748105), National Foundation in China (No. JCKY2019110B009 & 2020-JCJQ-JJ-252), the scholarship from China Scholarship Council (No. 201806060166) and the Fundamental Research Funds for the Central Universities [DUT20LAB303 & DUT20LAB308] in Dalian University of Technology in China.
dc.type.okmA1


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