Optimization of a generalized radial-aortic transfer function using parametric techniques. (1st October 2016)

• Record Type: Journal Article
• Authors: Akalanli, Cagla; Tay, David; Cameron, James D.
• Is Part Of: Computers in biology and medicine. Volume 77(2016)
• Page Start: 206
• Page End: 213
• Publication Date: 2016-10-01
• Subjects: Noninvasive measurement -- Transfer function (TF) -- Central aortic blood pressure (cBP); Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2016.08.015
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: The central aortic blood pressure (cBP) waveform, which is different to that of peripheral locations, is a clinically important parameter for assessing cardiovascular function, however the gold standard for measuring cBP involves invasive catheter-based techniques. The difficulties associated with invasive measurements have given rise to the development of a variety of noninvasive methods. An increasingly applied method for the noninvasive derivation of cBP involves the application of transfer function (TF) techniques to a non-invasively measured radial blood pressure (BP) waveform. The purpose of the current study was to investigate the development of a general parametric model for determination of cBP from tonometrically transduced radial BP waveforms. The study utilized simultaneously measured invasive central aortic and noninvasive radial BP waveform measurements. Data sets were available from 92 subjects, a large cohort for a study of this nature. The output error (OE) model was empirically identified as the most appropriate model structure. A generalized model was developed using a pre-specified derivation cohort and then applied to a validation data set to estimate the recognized features of the cBP waveform. While our results showed that many relevant BP parameters could be derived within acceptable limits, the estimated augmentation index (AI) displayed only a weak correlation compared to the invasively measured value, indicating that any clinical diagnosis or interpretation based on estimated AI should be undertaken with caution. Highlights: An optimized parametric arterial transfer function model developed. Model estimation of central aortic blood pressure parameters. Model developed using data from largest cohort of subjects reported. Results indicate the overall level of accuracy achievable using a transfer function.