Novel Imaging Revealing Inner Dynamics for Cardiovascular Waveform Analysis via Unsupervised Manifold Learning.

Journal: Anesthesia and analgesia
Published Date:

Abstract

BACKGROUND: Cardiovascular waveforms contain information for clinical diagnosis. By learning and organizing the subtle change of waveform morphology from large amounts of raw waveform data, unsupervised manifold learning helps delineate a high-dimensional structure and display it as a novel 3-dimensional (3D) image. We hypothesize that the shape of this structure conveys clinically relevant inner dynamics information.

Authors

  • Shen-Chih Wang
    From the Department of Anesthesiology, Taipei Veterans General Hospital, Taipei, Taiwan.
  • Hau-Tieng Wu
    Mathematics, University of Toronto, Toronto, Ontario, Canada.
  • Po-Hsun Huang
    Department of Critical Care Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.
  • Cheng-Hsi Chang
    Department of Anesthesiology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan.
  • Chien-Kun Ting
    From the Department of Anesthesiology, Taipei Veterans General Hospital, Taipei, Taiwan.
  • Yu-Ting Lin
    From the Department of Anesthesiology, Taipei Veterans General Hospital, Taipei, Taiwan.