Machine learning-based blood pressure estimation using impedance cardiography data.

Journal: Acta physiologica (Oxford, England)
PMID: 39803779

Abstract

OBJECTIVE: Accurate blood pressure (BP) measurement is crucial for the diagnosis, risk assessment, treatment decision-making, and monitoring of cardiovascular diseases. Unfortunately, cuff-based BP measurements suffer from inaccuracies and discomfort. This study is the first to access the feasibility of machine learning-based BP estimation using impedance cardiography (ICG) data.

Authors

  • T L Bothe
    Institute of Physiology, Center for Space Medicine and Extreme Environments Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany.
  • A Patzak
    Institute of Translational Physiology, Charité-Universitätsmedizin Berlin, Berlin, Germany.
  • O S Opatz
    Institute of Physiology, Center for Space Medicine and Extreme Environments Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany.
  • V Heinz
    Institute of Physiology, Center for Space Medicine and Extreme Environments Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany.
  • N Pilz
    Institute of Physiology, Center for Space Medicine and Extreme Environments Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany.

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