{"title":"Is It Feasible to Reconstruct Aortic Pressure Waveform Based On a 1d Uniform Model of the Arterial Tree?","authors":"Z. Hao","doi":"10.1115/1.4062468","DOIUrl":null,"url":null,"abstract":"\n Based on a 1D uniform model of the arterial tree, various machine-learning techniques have been explored to reconstruct aortic pressure waveform (APW) from peripheral pressure waveform (PPW). This study aims to examine the feasibility of such reconstruction. Based on a 1D uniform vibrating-string model, transfer function (TF) of PPW to APW contains four harmonics-dependent parameters: value and phase of reflection coefficient (i.e., load impedance) at periphery and transmission parameter and transmission loss in the aorta-periphery section, and they are all harmonics-dependent. Pressure waveforms and blood velocity waveforms at the ascending aorta (AA), the carotid artery (CA), and the radial artery (RA) at different ages in a database are analyzed to calculate 1) reflection coefficient at the CA and the RA as two peripheries, 2) TF for the AA-CA and AA-RA sections, and 3) transmission parameter and transmission loss in the two sections. Harmonics-dependence of the four parameters varies with aging for both sections, revealing unpracticality of any mathematical model for harmonics-dependence of load impedance. Compared with fluid-loading, arterial non-uniformity significantly affects wave transmission. Transmission loss dramatically alters reconstructed APW, relative to higher harmonics. A 1D uniform model allows accurate reconstruction of APW from PPW, with a caveat that baseline values for the four parameters at different harmonics under different cardiovascular (CV) conditions need to be established a priori. Alternatively, based on the baseline values, PPW can be directly utilized for inferring the CV conditions.","PeriodicalId":73734,"journal":{"name":"Journal of engineering and science in medical diagnostics and therapy","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of engineering and science in medical diagnostics and therapy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/1.4062468","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Based on a 1D uniform model of the arterial tree, various machine-learning techniques have been explored to reconstruct aortic pressure waveform (APW) from peripheral pressure waveform (PPW). This study aims to examine the feasibility of such reconstruction. Based on a 1D uniform vibrating-string model, transfer function (TF) of PPW to APW contains four harmonics-dependent parameters: value and phase of reflection coefficient (i.e., load impedance) at periphery and transmission parameter and transmission loss in the aorta-periphery section, and they are all harmonics-dependent. Pressure waveforms and blood velocity waveforms at the ascending aorta (AA), the carotid artery (CA), and the radial artery (RA) at different ages in a database are analyzed to calculate 1) reflection coefficient at the CA and the RA as two peripheries, 2) TF for the AA-CA and AA-RA sections, and 3) transmission parameter and transmission loss in the two sections. Harmonics-dependence of the four parameters varies with aging for both sections, revealing unpracticality of any mathematical model for harmonics-dependence of load impedance. Compared with fluid-loading, arterial non-uniformity significantly affects wave transmission. Transmission loss dramatically alters reconstructed APW, relative to higher harmonics. A 1D uniform model allows accurate reconstruction of APW from PPW, with a caveat that baseline values for the four parameters at different harmonics under different cardiovascular (CV) conditions need to be established a priori. Alternatively, based on the baseline values, PPW can be directly utilized for inferring the CV conditions.