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Review
. 2025 Feb;39(1):95-106.
doi: 10.1007/s10877-024-01221-7. Epub 2024 Sep 21.

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

Ravi Pal  1 , Joshua Le  2 , Akos Rudas  3 , Jeffrey N Chiang  4 , Tiffany Williams  3 , Brenton Alexander  5 , Alexandre Joosten  3 , Maxime Cannesson  3
Affiliations
Review

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

Ravi Pal et al. J Clin Monit Comput. 2025 Feb.

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.

Keywords: Hypertension; Hypotension; Machine learning; Non-invasive blood pressure.

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Conflict of interest statement

Declarations. Competing interests: Dr. Cannesson and Joosten are consultant for Edwards Lifesciences (Irvine, USA), and have funded research from Edwards Lifesciences. Dr. Cannesson is also the founder of Sironis and Perceptive Medical and he owns patents and receives royalties for closed loop hemodynamic management technologies that have been licensed to Edwards Lifesciences.

Figures

Fig. 1
Fig. 1
Timeline distribution of selected machine learning methods for non-invasive blood pressure estimation. Blue items represent intermittent methods, while green items signify continuous methods
See this image and copyright information in PMC

References

    1. Brekke IJ, Puntervoll LH, Pedersen PB, Kellett J, Brabrand M. The value of vital sign trends in predicting and monitoring clinical deterioration: a systematic review. PLoS ONE. 2019;14(1). 10.1371/journal.pone.0210875. - DOI - PMC - PubMed
    1. Meidert AS, Saugel B. Techniques for non-invasive monitoring of arterial blood pressure. 10.3389/fmed.2017.00231 - DOI - PMC - PubMed
    1. Ren Z. Rao B, Xie S, Li A, Wang L,Cui G, et al. Anovelpredictedmodel for hypertensionbasedon a large cross-sectional study. Sci Rep. 2020;10(1). 10.1038/s41598-020-64980-8. - DOI - PMC - PubMed
    1. Salmasi V, Maheshwari K, Yang D. Relationship between intra-operative hypotension, defined by either reduction from base-line or absolute thresholds, and acute kidney and myocardial injury after noncardiac surgery: a retrospective cohort analysis. Anesthesiology. 2017;126:47–65. 10.1097/ALN.0000000000001432. - DOI - PubMed
    1. Botto F, Alonso-Coello P, Chan MT. Myocardial Injury after noncardiac surgery: a large, international, prospective cohort study establishing diagnostic criteria, characteristics, predictors, and 30-day outcomes. Anesthesiology. 2014;120:564–78. 10.1097/ALN.0000000000000113. - DOI - PubMed

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