The fundamental mechanisms of the Korotkoff sounds generation

Author:

Baranger Jerome1ORCID,Villemain Olivier1ORCID,Goudot Guillaume12ORCID,Dizeux Alexandre1,Le Blay Heiva1,Mirault Tristan2ORCID,Messas Emmanuel2ORCID,Pernot Mathieu1ORCID,Tanter Mickael1ORCID

Affiliation:

1. Physics for Medicine Paris, Inserm, ESPCI PSL Paris, CNRS, Paris, France.

2. Université Paris Cité, Inserm UMR 970, PARCC, Vascular Medicine Department, Hôpital Européen Georges-Pompidou, Assistance Publique Hôpitaux de Paris, Paris, France.

Abstract

Blood pressure measurement is the most widely performed clinical exam to predict mortality risk. The gold standard for its noninvasive assessment is the auscultatory method, which relies on listening to the so-called “Korotkoff sounds” in a stethoscope placed at the outlet of a pneumatic arm cuff. However, more than a century after their discovery, the origin of these sounds is still debated, which implies a number of clinical limitations. We imaged the Korotkoff sound generation in vivo at thousands of images per second using ultrafast ultrasound. We showed with both experience and theory that Korotkoff sounds are paradoxically not sound waves emerging from the brachial artery but rather shear vibrations conveyed in surrounding tissues by the nonlinear pulse wave propagation. When these shear vibrations reached the stethoscope, they were synchronous, correlated, and comparable in intensity with the Korotkoff sounds. Understanding this mechanism could ultimately improve blood pressure measurement and provide additional understanding of arterial mechanical properties.

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. a-STEP: Oscillometry-Augmented Auscultation Method for Improved Blood Pressure Measurement;2024 IEEE International Symposium on Medical Measurements and Applications (MeMeA);2024-06-26

2. Photoplethysmogram (PPG)-Based Blood Pressure Estimation using Vision Transformer Networks: A Deep Learning Approach;2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT);2024-05-03

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