Multivariate Sleep Stage Classification using Hybrid Self-Attentive Deep Learning Networks

Ye Yuan, Kebin Jia, Fenglong Ma, Guangxu Xun, Yaqing Wang, Lu Su, Aidong Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Recently, significant efforts have been made to explore comprehensive sleep monitoring to prevent sleep-related disorders. Multivariate sleep stage classification has garnered great interest among researchers in health informatics. In this paper, we propose HybridAtt, a unified hybrid self-attentive deep learning network, to classify sleep stages from multivariate polysomnography (PSG) records. HybridAtt is an end-to-end model that explicitly captures the complex correlations among biomedical channels and the dynamic relationships over time. By constructing a new multi-view convolutional representation module, HybridAtt is able to extract hidden features from both channel-specific and global views of the heterogeneous PSG inputs. In order to enhance feature representation, a new fusion-based attention mechanism is also proposed to integrate the complementary information carried by each feature view. To evaluate the performance of our model, we carry out experiments on a benchmark PSG dataset. Experimental results show that the proposed HybridAtt model achieves better performance compared to ten baseline methods, demonstrating the effectiveness of HybridAtt in the task of sleep stage classification.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
EditorsHarald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages963-968
Number of pages6
ISBN (Electronic)9781538654880
DOIs
StatePublished - Jan 21 2019
Event2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain
Duration: Dec 3 2018Dec 6 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
CountrySpain
CityMadrid
Period12/3/1812/6/18

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All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Health Informatics

Cite this

Yuan, Y., Jia, K., Ma, F., Xun, G., Wang, Y., Su, L., & Zhang, A. (2019). Multivariate Sleep Stage Classification using Hybrid Self-Attentive Deep Learning Networks. In H. Schmidt, D. Griol, H. Wang, J. Baumbach, H. Zheng, Z. Callejas, X. Hu, J. Dickerson, & L. Zhang (Eds.), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 (pp. 963-968). [8621146] (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2018.8621146