Heart failure classification using deep learning to extract spatiotemporal features from ECG.

Journal: BMC medical informatics and decision making
Published Date: Jan 15, 2024

Abstract

BACKGROUND: Heart failure is a syndrome with complex clinical manifestations. Due to increasing population aging, heart failure has become a major medical problem worldwide. In this study, we used the MIMIC-III public database to extract the temporal and spatial characteristics of electrocardiogram (ECG) signals from patients with heart failure.

Authors

  • Chang-Jiang Zhang
    Taizhou Central Hospital, Affiliated Hospital of Taizhou University, Taizhou, China.
  • Yuan-Lu
    School of Electronic and Information Engineering (School of Big Data Science), Taizhou University, Taizhou, China.
  • Fu-Qin Tang
    Taizhou Central Hospital, Affiliated Hospital of Taizhou University, Taizhou, China. tfq_taizhou@163.com.
  • Hai-Peng Cai
    Taizhou Central Hospital, Affiliated Hospital of Taizhou University, Taizhou, China.
  • Yin-Fen Qian
    Taizhou Central Hospital, Affiliated Hospital of Taizhou University, Taizhou, China.
  • Chao-Wang
    Taizhou Central Hospital, Affiliated Hospital of Taizhou University, Taizhou, China.

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