A review of machine learning methods for non-invasive blood pressure estimation.

Journal: Journal of clinical monitoring and computing
PMID: 39305449

Abstract

Blood pressure is a very important clinical measurement, offering valuable insights into the hemodynamic status of patients. Regular monitoring is crucial for early detection, prevention, and treatment of conditions like hypotension and hypertension, both of which increasing morbidity for a wide variety of reasons. This monitoring can be done either invasively or non-invasively and intermittently vs. continuously. An invasive method is considered the gold standard and provides continuous measurement, but it carries higher risks of complications such as infection, bleeding, and thrombosis. Non-invasive techniques, in contrast, reduce these risks and can provide intermittent or continuous blood pressure readings. This review explores modern machine learning-based non-invasive methods for blood pressure estimation, discussing their advantages, limitations, and clinical relevance.

Authors

  • Ravi Pal
    Department of Anesthesiology and Perioperative Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA.
  • Joshua Le
    Larner College of Medicine, University of Vermont, Burlington, USA.
  • Akos Rudas
    Department of Computational Medicine, UCLA, Los Angeles, California, United States of America.
  • Jeffrey N Chiang
    Department of Computational Medicine, UCLA, Los Angeles, California, United States of America.
  • Tiffany Williams
    Department of Anesthesiology & Perioperative Medicine, David Geffen School of Medicine, University of California Los Angeles, Ronald Reagan UCLA Medical Center, 757 Westwood Plaza, Los Angeles, CA, 90095, USA.
  • Brenton Alexander
    Department of Anesthesiology & Perioperative Medicine, University of California San Diego, San Diego, CA, USA.
  • Alexandre Joosten
    Department of Anesthesiology & Intensive Care, Hôpital De Bicêtre, Assistance Publique Hôpitaux de Paris (AP-HP), Paris, France.
  • Maxime Cannesson
    Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.

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