Reconstruction of Continuous Brachial Arterial Pressure From Continuous Finger Arterial Pressure Using a Two-Level Optimization Strategy

• Published in: IEEE transactions on biomedical engineering Vol. 67; no. 11; pp. 3173 - 3184
• Main Authors: Zhang, Pandeng, Liu, Chang, Chen, Haibo, Liu, Jia
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Arteries; Blood pressure; Blood pressure measurement; Brachytherapy; Coefficients; continuous non-invasive blood pressure; Data collection; Delay effects; Elastic waves; Fingers; Impedance; Integrated circuit modeling; Optimization; Parameter estimation; proximal pressure reconstruction; Reconstruction; Regression models; Strategy; Time lag; Transfer functions; tube-load model; Unloading; vascular unloading technique
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2020.2979249

Objective: We attempt to reconstruct brachial arterial pressure (BAP) waves from finger arterial pressure waves measured using the vascular unloading technique without arm-cuff calibration. A novel method called two-level optimization (TOP) strategy is proposed as follows. Methods: We first derive a simplified transfer function (TF) based on a tube-load model with only two parameters to be estimated, a coefficient <inline-formula><tex-math notation="LaTeX">B</tex-math></inline-formula> and a time delay <inline-formula><tex-math notation="LaTeX">\Delta t</tex-math></inline-formula>. Then, at level one, two minimization problems are formulated to estimate the optimal coefficient <inline-formula><tex-math notation="LaTeX">{B^{opt}}</tex-math></inline-formula> and time delay <inline-formula><tex-math notation="LaTeX">\Delta {t^{opt}}</tex-math></inline-formula>. Then, we can derive an optimal TF <inline-formula><tex-math notation="LaTeX">{h^{opt}}(t)</tex-math></inline-formula>. However, this derivation requires true (or reference) BAP waves. Therefore, at level two, we apply multiple linear regression (MLR) to further model the relationship between the derived optimal parameters and subjects' physiologic parameters. Hence, eventually, one can estimate coefficient <inline-formula><tex-math notation="LaTeX">{B^{MLR}}</tex-math></inline-formula> and time delay <inline-formula><tex-math notation="LaTeX">\Delta {t^{MLR}}</tex-math></inline-formula> from subject's physiologic parameters to derive the MLR-based TF <inline-formula><tex-math notation="LaTeX">{h^{MLR}}(t)</tex-math></inline-formula> for the BAP reconstruction. Results: Twenty-one volunteers were recruited for the data collection. The mean ± standard deviation of the root mean square errors between the reference BAP waves and the BAP waves reconstructed by <inline-formula><tex-math notation="LaTeX">{h^{opt}}(t)</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">{h^{MLR}}(t)</tex-math></inline-formula>, and a generalized transfer function (GTF) were 3.46 ± 1.42 mmHg, 3.61 ± 2.28 mmHg, and 6.80 ± 3.73 mmHg (significantly larger with p < 0.01), respectively. Conclusions: The proposed method can be considered as a semi-individualized TF which reconstructs significantly better BAP waves than a GTF. Significance: The proposed TOP strategy can potentially be useful in more general reconstruction of proximal BP waves.