Convolutional Neural Network Based Sleep Stage Classification with Class Imbalance
Xu, Q., Zhou, D., Wang, J., Shen, J., Kettunen, L., & Cong, F. (2022). Convolutional Neural Network Based Sleep Stage Classification with Class Imbalance. In IJCNN 2022 : Proceedings of the 2022 International Joint Conference on Neural Networks. IEEE. Proceedings of International Joint Conference on Neural Networks. https://doi.org/10.1109/ijcnn55064.2022.9892741
Julkaistu sarjassa
Proceedings of International Joint Conference on Neural NetworksPäivämäärä
2022Oppiaine
Laskennallinen tiedeSecure Communications Engineering and Signal ProcessingComputing, Information Technology and MathematicsTietotekniikkaTekniikkaComputational ScienceSecure Communications Engineering and Signal ProcessingComputing, Information Technology and MathematicsMathematical Information TechnologyEngineeringTekijänoikeudet
© 2022, IEEE
Accurate sleep stage classification is vital to assess sleep quality and diagnose sleep disorders. Numerous deep learning based models have been designed for accomplishing this labor automatically. However, the class imbalance problem existing in polysomnography (PSG) datasets has been barely investigated in previous studies, which is one of the most challenging obstacles for the real-world sleep staging application. To address this issue, this paper proposes novel methods with signal-driven and image-driven ways of noise addition to balance the imbalanced relationship in the training dataset samples. We evaluate the effectiveness of the proposed methods which are integrated into a convolutional neural network (CNN) based model. Experimental results evaluated on Sleep-EDF-V1, Sleep-EDF and CCSHS databases demonstrate that the proposed balancing approaches with specific tensity Gaussian white noise could enhance the overall or stage N1 recognition to some degree, especially the combination of two types of Data augmentation (DA) strategies shows the superiority of overall accuracy improvement.
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Julkaisija
IEEEEmojulkaisun ISBN
978-1-7281-8671-9Konferenssi
International Joint Conference on Neural NetworksKuuluu julkaisuun
IJCNN 2022 : Proceedings of the 2022 International Joint Conference on Neural NetworksISSN Hae Julkaisufoorumista
2161-4393Asiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/156990634
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Lisätietoja rahoituksesta
This work was supported by National Key R&D Program of China National (No.2021ZD0109803), Natural Science Foundation of China (No.91748105), National Foundation in China (No. JCKY2019110B009, 2020-JCJQ-JJ-252), the Fundamental Research Funds for the Central Universities [DUT20LAB303, DUT20LAB308, DUT21RC(3)091] in Dalian University of Technology in China, Open Research Fund from Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ, No. GML-KF-22-11), CAAI-Huawei Mindspore Open Fund (CAAIXSJLJJ-2021-003A) and the Scholarships from China Scholarship Council (No.201806060164, No.202006060226). ...Lisenssi
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