Literature DB >> 28613159

Noninvasive Blood Pressure Estimation Using Ultrasound and Simple Finite Element Models.

Aaron M Zakrzewski, Brian W Anthony.   

Abstract

OBJECTIVE: Many commercially available arterial blood pressure measurement devices suffer from a range of weaknesses. For example, common weaknesses include being inaccurate, invasive, and ad hoc; many also require explicit user calibration or cut off blood flow to a limb. A novel algorithmic approach is presented to accurately estimate systolic and diastolic blood pressure in a way that does not require any explicit user calibration, is noninvasive, and does not cut off blood flow.
METHODS: The approach uses ultrasound images of the arterial wall and corresponding contact force data to obtain blood pressure estimates. To acquire data, an ultrasound probe was placed on the patient's carotid artery and the contact force was increased from 1.5 to 12 N. The artery was then algorithmically segmented from the recorded DICOM B-Mode data. The segmentation data and the contact force were used as input into the Levenberg-Marquardt optimization method to solve for the parameters, including blood pressure, of a simple finite element model of the carotid artery.
RESULTS: The algorithm was validated on 24 healthy volunteers. Algorithm arterial blood pressure predictions were compared to oscillometric blood pressure cuff readings. Regression and Bland-Altman analyses were performed on the data.
CONCLUSION: Both systolic pressure and diastolic pressure can be estimated using this novel noninvasive ultrasound-based method (systolic accuracy/precision: $-$ 2.36 mmHg/10.21 mmHg; diastolic accuracy/precision: $-$ 0.32/8.23 mmHg). SIGNIFICANCE: The method occupies a clinical middle ground between the arterial catheter and cuff-based techniques. It has the potential to give accurate results for patients with hypertension and atherosclerosis.

Entities:  

Mesh:

Year:  2017        PMID: 28613159     DOI: 10.1109/TBME.2017.2714666

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  6 in total

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Review 4.  Recent Advances in Non-Invasive Blood Pressure Monitoring and Prediction Using a Machine Learning Approach.

Authors:  Siti Nor Ashikin Ismail; Nazrul Anuar Nayan; Rosmina Jaafar; Zazilah May
Journal:  Sensors (Basel)       Date:  2022-08-18       Impact factor: 3.847

Review 5.  Human Vital Signs Detection Methods and Potential Using Radars: A Review.

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6.  Measurement of Blood Pressure by Ultrasound-The Applicability of Devices, Algorithms and a View in Local Hemodynamics.

Authors:  Moritz Meusel; Philipp Wegerich; Berit Bode; Elena Stawschenko; Kristina Kusche-Vihrog; Horst Hellbrück; Hartmut Gehring
Journal:  Diagnostics (Basel)       Date:  2021-12-02
  6 in total

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