Title
Simultaneous adaption of the gain and phase of a generalized transfer function for aortic pressure waveform estimation
Abstract
Goal: This paper proposes and validates a completely adaptive transfer function (CATF) based on an autoregressive exogenous (ARX) model which adjusts the gain and phase of a generalized transfer function (GTF) simultaneously to estimate the aortic pressure waveform from a brachial pressure waveform. Methods: Invasive aortic and brachial pressure waveforms were recorded from 34 subjects for the validation of the proposed method. Individual transfer functions (ITFs) were trained based on the pressure waveforms using an ARX model. The GTF was derived by averaging the ITFs. CATF was then obtained by adjusting both the gain and phase of the GTF using regression formulas calculated from the ITFs and brachial hemodynamic parameters. Meanwhile the quantitative contributions of the adaption of gain and phase of the GTF were investigated respectively. The root-mean-square-error of the total waveform and absolute errors of common hemodynamic indices including systolic and diastolic blood pressures (SBP and DBP, respectively), pulse pressure (PP) and augmentation index were used to evaluate the performance of the proposed method in the data divided into low, middle and high PP amplification groups. Results: The CATF achieved lower errors for DBP and PP in the low PP amplification group (1.79 versus 2.10 mmHg and 5.08 versus 6.23 mmHg, respectively, both P < 0.05) and PP in the middle amplification group (1.43 versus 1.92 mmHg, P < 0.05) compared with the GTF. Significance: The proposed method provides a step towards the development of an improved and clinically useful non-invasive approach for estimating the aortic pressure waveform from a peripheral pressure waveform.
Year
DOI
Venue
2022
10.1016/j.compbiomed.2021.105187
COMPUTERS IN BIOLOGY AND MEDICINE
Keywords
DocType
Volume
Adaptive transfer function, Generalized transfer function, Aortic pressure waveform, Brachial pressure waveform
Journal
141
ISSN
Citations 
PageRank 
0010-4825
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Shuo Du100.34
Yang Yao200.34
Guozhe Sun300.34
Ramakrishna Mukkamala400.34
Lisheng Xu517839.09