Estimation of Stroke Volume Variance from Arterial Blood Pressure: Using a 1-D Convolutional Neural Network.

Journal: Sensors (Basel, Switzerland)
Published Date: Jul 29, 2021

Abstract

BACKGROUND: We aimed to create a novel model using a deep learning method to estimate stroke volume variation (SVV), a widely used predictor of fluid responsiveness, from arterial blood pressure waveform (ABPW).

Authors

  • Hye-Mee Kwon
    Asan Medical Center, Department of Anesthesiology and Pain Medicine, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Seoul 05505, Korea.
  • Woo-Young Seo
    Biomedical Engneering Research Center, Asan Medical Center, Seoul 05505, Korea.
  • Jae-Man Kim
    Biomedical Engneering Research Center, Asan Medical Center, Seoul 05505, Korea.
  • Woo-Hyun Shim
    Asan Medical Center, Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Seoul 05505, Korea.
  • Sung-Hoon Kim
    Asan Medical Center, Department of Anesthesiology and Pain Medicine, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Seoul 05505, Korea.
  • Gyu-sam Hwang
    2 Department of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine , Seoul, Korea.

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