Intrinsic Frequency Analysis and Fast Algorithms
Abstract
Intrinsic Frequency (IF) has recently been introduced as an ample signal processing method for analyzing carotid and aortic pulse pressure tracings. The IF method has also been introduced as an effective approach for the analysis of cardiovascular system dynamics. The physiological significance, convergence and accuracy of the IF algorithm has been established in prior works. In this paper, we show that the IF method could be derived by appropriate mathematical approximations from the Navier-Stokes and elasticity equations. We further introduce a fast algorithm for the IF method based on the mathematical analysis of this method. In particular, we demonstrate that the IF algorithm can be made faster, by a factor or more than 100 times, using a proper set of initial guesses based on the topology of the problem, fast analytical solution at each point iteration, and substituting the brute force algorithm with a pattern search method. Statistically, we observe that the algorithm presented in this article complies well with its brute-force counterpart. Furthermore, we will show that on a real dataset, the fast IF method can draw correlations between the extracted intrinsic frequency features and the infusion of certain drugs.
Additional Information
© 2018 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Received: 21 September 2017; Accepted: 01 March 2018; Published online: 20 March 2018. We would like to thank Mr. Sean Brady for constructive discussions and editorial comments. Data availability: All data generated or analysed during this study are included in the Supplementary Information files. The datasets generated during and analysed during the current study are also available from the corresponding author on reasonable request. Research ethics: All experiments and procedures were reviewed and approved by the MSU All-University Committee on Animal Use and Care. Author Contributions: P.T. conceived of the mathematical and numerical methods of the study, carried out the modeling, programmed the initial code of the method, and drafted the manuscript; H.K. helped with the mathematical and numerical derivations, helped draft and revise the manuscript, and conducted the real data case example; J.K. conducted the brute-force simulations on the real data case example, and helped draft the manuscript. All authors gave final approval for publication. Competing Interests: Dr. Tavallali had employment agreement with Avicena. Dr. Koorehdavoudi has employment agreements with Avicena. Ms. Krupa has employment agreements with Avicena.Attached Files
Published - s41598-018-22907-4.pdf
Submitted - 1708.00465.pdf
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Additional details
- PMCID
- PMC5861104
- Eprint ID
- 85461
- Resolver ID
- CaltechAUTHORS:20180327-132659340
- Created
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2018-03-27Created from EPrint's datestamp field
- Updated
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2022-03-15Created from EPrint's last_modified field