Shape Analysis of Coronary Flow Waveforms using Singular Value Decomposition
- 1University of Michigan
Abstract
Coronary flow waveforms have a distinct diastolic-dominant shape with periods of low or retrograde flow during systole. While the general waveform shape has been attributed to complex interactions between cardiac and vascular mechanics, there is limited research into the variability in coronary flow waveforms and what this variability may reveal about cardiac function. This work presents a shape analysis of left anterior descending artery (LAD) flow waveforms using Fourier transforms and Singular Value Decomposition (SVD) performed on baseline data collected from 32 pigs. Pigs included in the study reflect two breeds (Ossabaw and Yorkshire) and three different experimental conditions (lean-control, lean-paced, and obese-paced). Fourier transforms were used to decompose the waveforms into 15 harmonics for each pig. An SVD analysis is then used to extract temporal patterns of the waveforms. Correlations between pig-specific coefficients for the SVD modes and clinical metrics were used to investigate physiological explanations of LAD waveform variability. Temporal LAD flow patterns of the second SVD mode are significantly correlated with heart rate. The third SVD mode significantly correlates with mean blood pressure and maximum hyperemic flow. Furthermore, the fourth SVD mode is weakly correlated with left-ventricular end diastolic pressure and endocardial-epicardial flow ratios. This work demonstrates that LAD flow waveforms can be broken down into temporal patterns that correlate with physiological features. Furthermore, this shape-analysis method allows for waveform reconstruction and simplifies visualization of the temporal patterns identified using SVD, an advantage over existing methods that focus on characterizing flow waveforms by points of interest.
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BibTeX
@misc{sturgess2026shape,
title = {{Shape Analysis of Coronary Flow Waveforms using Singular Value Decomposition}},
author = {V. E. Sturgess and N. A. Schenk and J. W. Ziegele and S. I. Essajee and J. D. Tune and I. Rajapakse and C. A. Figueroa and D. A. Beard},
howpublished = {bioRxiv},
year = {2026},
month = aug,
doi = {10.64898/2026.08.26.743980},
url = {https://www.biorxiv.org/content/10.64898/2026.08.26.743980v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - Shape Analysis of Coronary Flow Waveforms using Singular Value Decomposition
AU - V. E. Sturgess
AU - N. A. Schenk
AU - J. W. Ziegele
AU - S. I. Essajee
AU - J. D. Tune
AU - I. Rajapakse
AU - C. A. Figueroa
AU - D. A. Beard
PY - 2026
DA - 2026/08/26
PB - bioRxiv
DO - 10.64898/2026.08.26.743980
UR - https://www.biorxiv.org/content/10.64898/2026.08.26.743980v1
AB - Coronary flow waveforms have a distinct diastolic-dominant shape with periods of low or retrograde flow during systole. While the general waveform shape has been attributed to complex interactions between cardiac and vascular mechanics, there is limited research into the variability in coronary flow waveforms and what this variability may reveal about cardiac function. This work presents a shape analysis of left anterior descending artery (LAD) flow waveforms using Fourier transforms and Singular Value Decomposition (SVD) performed on baseline data collected from 32 pigs. Pigs included in the study reflect two breeds (Ossabaw and Yorkshire) and three different experimental conditions (lean-control, lean-paced, and obese-paced). Fourier transforms were used to decompose the waveforms into 15 harmonics for each pig. An SVD analysis is then used to extract temporal patterns of the waveforms. Correlations between pig-specific coefficients for the SVD modes and clinical metrics were used to investigate physiological explanations of LAD waveform variability. Temporal LAD flow patterns of the second SVD mode are significantly correlated with heart rate. The third SVD mode significantly correlates with mean blood pressure and maximum hyperemic flow. Furthermore, the fourth SVD mode is weakly correlated with left-ventricular end diastolic pressure and endocardial-epicardial flow ratios. This work demonstrates that LAD flow waveforms can be broken down into temporal patterns that correlate with physiological features. Furthermore, this shape-analysis method allows for waveform reconstruction and simplifies visualization of the temporal patterns identified using SVD, an advantage over existing methods that focus on characterizing flow waveforms by points of interest.
ER -