A heart failure classification model from radial artery pulse wave using LSTM neural networks.

Journal: BMC medical informatics and decision making
Published Date: Aug 27, 2025

Abstract

BACKGROUND: Heart failure (HF) represents a pressing global health issue demanding innovative and accessible approaches for early detection. Non-invasive, rapid, and cost-effective techniques utilizing deep learning (DL) hold significant promise for addressing this challenge.

Authors

  • Yi Lyu
    School of Public Health, Shanxi Medical University, Taiyuan 030000, China.
  • Wen-Yue Huang
    Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, P.R. China.
  • Hai-Mei Wu
    Guangdong Provincial Traditional Chinese Medicine Hospital, Guangzhou, 510120, P.R. China.
  • Jing Hong
    Department of Ophthalmology, Peking University Third Hospital, Beijing, China hongjing196401@163.com.
  • Yi-Qin Wang
    School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, P.R. China.
  • Hai-Xia Yan
    School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, P.R. China.
  • Jin Xu
    Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, and School of Statistics, East China Normal University, Shanghai, China.

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