dc.contributor.author | Zhou, Dongdong | |
dc.contributor.author | Wang, Jian | |
dc.contributor.author | Hu, Guoqiang | |
dc.contributor.author | Zhang, Jiacheng | |
dc.contributor.author | Li, Fan | |
dc.contributor.author | Yan, Rui | |
dc.contributor.author | Kettunen, Lauri | |
dc.contributor.author | Chang, Zheng | |
dc.contributor.author | Xu, Qi | |
dc.contributor.author | Cong, Fengyu | |
dc.date.accessioned | 2023-02-21T07:01:46Z | |
dc.date.available | 2023-02-21T07:01:46Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Zhou, D., Wang, J., Hu, G., Zhang, J., Li, F., Yan, R., Kettunen, L., Chang, Z., Xu, Q., & Cong, F. (2022). SingleChannelNet : A model for automatic sleep stage classification with raw single-channel EEG. <i>Biomedical Signal Processing and Control</i>, <i>75</i>, Article 103592. <a href="https://doi.org/10.1016/j.bspc.2022.103592" target="_blank">https://doi.org/10.1016/j.bspc.2022.103592</a> | |
dc.identifier.other | CONVID_117411982 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/85554 | |
dc.description.abstract | In diagnosing sleep disorders, sleep stage classification is a very essential yet time-consuming process. Various existing state-of-the-art approaches rely on hand-crafted features and multi-modality polysomnography (PSG) data, where prior knowledge is compulsory and high computation cost can be expected. Besides, it is a big challenge to handle the task with raw single-channel electroencephalogram (EEG). To overcome these shortcomings, this paper proposes an end-to-end framework with a deep neural network, namely SingleChannelNet, for automatic sleep stage classification based on raw single-channel EEG. The proposed model utilizes a 90s epoch as the textual input and employs two multi-convolution (MC) blocks and several max-average pooling (M-Apooling) layers to learn different scales of feature representations. To demonstrate the efficiency of the proposed model, we evaluate our model using different raw single-channel EEGs (C4/A1 and Fpz-Cz) on two public PSG datasets (Cleveland children’s sleep and health study: CCSHS and Sleep-EDF database expanded: Sleep-EDF). Experimental results show that the proposed architecture can achieve better overall accuracy and Cohen’s kappa (CCSHS: 90.2%–86.5%, Sleep-EDF: 86.1%–80.5%) compared with state-of-the-art approaches. Additionally, the proposed model can learn features automatically for sleep stage classification using different single-channel EEGs with distinct sampling rates and without using any hand-engineered features. | en |
dc.format.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.publisher | Elsevier | |
dc.relation.ispartofseries | Biomedical Signal Processing and Control | |
dc.rights | CC BY-NC-ND 4.0 | |
dc.subject.other | Sleep stage classification | |
dc.subject.other | Raw single-channel EEG | |
dc.subject.other | Contextual input | |
dc.subject.other | Convolutional neural network | |
dc.title | SingleChannelNet : A model for automatic sleep stage classification with raw single-channel EEG | |
dc.type | article | |
dc.identifier.urn | URN:NBN:fi:jyu-202302211813 | |
dc.contributor.laitos | Informaatioteknologian tiedekunta | fi |
dc.contributor.laitos | Faculty of Information Technology | en |
dc.contributor.oppiaine | Tietotekniikka | fi |
dc.contributor.oppiaine | Tekniikka | fi |
dc.contributor.oppiaine | Secure Communications Engineering and Signal Processing | fi |
dc.contributor.oppiaine | Computing, Information Technology and Mathematics | fi |
dc.contributor.oppiaine | Laskennallinen tiede | fi |
dc.contributor.oppiaine | Mathematical Information Technology | en |
dc.contributor.oppiaine | Engineering | en |
dc.contributor.oppiaine | Secure Communications Engineering and Signal Processing | en |
dc.contributor.oppiaine | Computing, Information Technology and Mathematics | en |
dc.contributor.oppiaine | Computational Science | en |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | |
dc.type.coar | http://purl.org/coar/resource_type/c_2df8fbb1 | |
dc.description.reviewstatus | peerReviewed | |
dc.relation.issn | 1746-8094 | |
dc.relation.volume | 75 | |
dc.type.version | acceptedVersion | |
dc.rights.copyright | © 2022 Elsevier Ltd. All rights reserved. | |
dc.rights.accesslevel | openAccess | fi |
dc.subject.yso | unihäiriöt | |
dc.subject.yso | uni (lepotila) | |
dc.subject.yso | EEG | |
dc.subject.yso | signaalianalyysi | |
dc.subject.yso | koneoppiminen | |
dc.subject.yso | signaalinkäsittely | |
dc.subject.yso | neuroverkot | |
dc.format.content | fulltext | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p4600 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p8299 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p3328 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p26805 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p21846 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p12266 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p7292 | |
dc.rights.url | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.relation.doi | 10.1016/j.bspc.2022.103592 | |
jyx.fundinginformation | This work was supported by National Natural Science Foundation of China (Grant No. 91748105), National Foundation in China (No. JCKY2019110B009, 2020-JCJQ-JJ-252) and the Fundamental Research Funds for the Central Universities [DUT20LAB303, DUT20LAB308, DUT21RC(3)091] in Dalian University of Technology in China and the Scholarships from China Scholarship Council (No. 201806060164, No. 202006060226) and CAAI-Huawei Mindspore Open Fund (No. CAAIXSJLJJ-2021-003A). | |
dc.type.okm | A1 | |