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01 · ABSTRACT

Abstract

Purpose: Previous studies have suggested that minimally invasive peripheral venous and arterial pressure waveforms provide a greater ability to detect acute changes in blood volume than traditional vital signs. Many of these studies are using Fast Fourier Transforms and power spectral densities to evaluate changes in the power at the heart rate frequency. Using the frequency domain requires a segment of the time domain to be converted into the frequency domain, which means that the heart rate derived from frequency domain analysis is an average of the segment used. However, in clinical settings the heart rate is changing continuously.

Methods: This study evaluates the changing heart rate frequency power under varying time segments and compares the heart rate obtained from Fast Fourier Transform and power spectral density analysis with the instantaneous heart rate to gain a better insight into how a changing heart rate may influence the heart rate frequency power.

Results: Spectral analysis revealed non-linear trends in heart rate frequency power, with changes that correspond to changes in the heart rate. We found the time segment chosen and the absolute difference between the instantaneous heart rate and the average heart rate obtained from power spectral density analysis influences the heart rate frequency power, such that as the instantaneous heart rate approaches the average heart rate, the heart rate frequency power increases.

Conclusion: These results suggest that the time segment chosen for frequency domain analysis influences the power spectrum of the pressure waveforms. Furthermore, this study emphasizes the importance of utilizing a continuously changing heart rate in pressure waveform analysis.

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02 · PUBLICATION RECORD

Article details

JournalMedical Research Archives
IssueVol 13 No 8 (2025): Vol.13, Issue 8, August 2025
SectionResearch Articles
Published25 August 2025
DOI10.18103/mra.v13i8.6824
ISSN2375-1924
03 · RIGHTS & REUSE

Rights & reuse

This article is published under a Creative Commons Attribution License (CC BY 3.0) and may be shared or distributed by anyone as long as attribution is given to the journal.

Authors & affiliations

GB

Gabriel P. Bonvillain, B.S.

Department of Biomedical Engineering, University of Arkansas, Fayetteville, Arkansas

LP

Lauren D. Pierce, M.D.

Department of Biomedical Engineering, University of Arkansas, Fayetteville, Arkansas

AV

Adria Abella Villafranca, B.S.

Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas

SS

Sam E. Stephens, M.S.

Department of Biomedical Engineering, University of Arkansas, Fayetteville, Arkansas

LF

Luke E. Ferguson

Department of Biomedical Engineering, University of Arkansas, Fayetteville, Arkansas

HJ

Hanna K. Jensen, M.D., Ph.D.

Department of Surgery, University of Arkansas for Medical Sciences, Little Rock, Arkansas

JS

Joseph A. Sanford, M.D.

Department of Anesthesiology, University of Arkansas for Medical Sciences, Little Rock, Arkansas

JW

Jingxian Wu, Ph.D.

Department of Electrical Engineering, University of Arkansas, Fayetteville, Arkansas

KS

Kevin Sexton, M.D.

Department of Biomedical Engineering, University of Arkansas, Fayetteville, Arkansas; Department of Surgery, University of Arkansas for Medical Sciences, Little Rock, Arkansas

MJ

Morten Jensen, Ph.D, Dr.Med.

Department of Biomedical Engineering, University of Arkansas, Fayetteville, Arkansas;  Department of Surgery, University of Arkansas for Medical Sciences, Little Rock, Arkansas

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