Go to Header Go to Navigation Go to Content Go to Footer
  • Guide
  • Login (ext. users)
  • Login (JyU account)
  • Accessibility
  • Finnish (Finland)
Converis - Home
    List of people >  Dongdong Zhou
    • Home
    • Persons
    • Organisations
    • Projects
    • Publications
    • Research datasets
    • Fields of science
    • Funding programs
    • Follow-up groups
    • Keywords (YSO)

    Dongdong Zhou


    ORCID link: https://orcid.org/0000-0002-8726-4855


    No active affiliation


    Previous, inactive or other affiliations

    • Faculty of Information Technology (University of Jyväskylä), Doctoral Student, Ended
    • Faculty of Information Technology (University of Jyväskylä), Grant Researcher, Ended
    • Faculty of Information Technology (University of Jyväskylä), Grant Researcher (JYU funding), Ended


    Publications and other outputs

    • Automatic sleep stage classification based on single-channel EEG (2023) Zhou, Dongdong; G5; OA; 978-951-39-9303-0
    • Alleviating Class Imbalance Problem in Automatic Sleep Stage Classification (2022) Zhou, Dongdong; et al.; A1; OA
    • Convolutional Neural Network Based Sleep Stage Classification with Class Imbalance (2022) Xu, Qi; et al.; A4; OA; 978-1-7281-8671-9
    • SingleChannelNet : A model for automatic sleep stage classification with raw single-channel EEG (2022) Zhou, Dongdong; et al.; A1; OA
    • Automatic sleep scoring : a deep learning architecture for multi-modality time series (2021) Yan, Rui; et al.; A1; OA
    • LightSleepNet : A Lightweight Deep Model for Rapid Sleep Stage Classification with Spectrograms (2021) Zhou, Dongdong; et al.; A4; OA; 978-1-7281-1179-7
    • A Deep Learning Model for Automatic Sleep Scoring using Multimodality Time Series (2020) Yan, Rui; et al.; A4; OA; 978-9-0827-9705-3

    Last updated on 2023-01-04 at 10:33

       
     


    University of Jyväskylä | Research and Innovation Services

    Services to support research (HelpJYU) | converis-support@jyu.fi

    About research information systems | Data Protection Description