Key result
Unknown input observer reduces central aortic BP estimation error by ~29% versus conventional techniques.
Why the study?
Estimating central aortic blood pressure is important for cardiovascular health and risk prediction, motivating observer-based approaches to noninvasive estimation from multichannel peripheral measurements.
Effect estimate: Up to 28.8% reduction
A novel observer-based deconvolution algorithm improves the accuracy of noninvasive central aortic blood pressure estimation compared to conventional methods.
Requires prospective validation before guiding central BP monitoring; leaves open outcome trials in clinical populations.
Estimating central aortic blood pressure (BP) is important for cardiovascular (CV) health and risk prediction purposes. CV system is a multichannel dynamical system that yields multiple BPs at various body sites in response to central aortic BP. This paper concerns the development and analysis of an observer-based approach to deconvolution of unknown input in a class of coprime multichannel systems applicable to noninvasive estimation of central aortic BP. A multichannel system yields multiple outputs in response to a common input. Hence, the relationship between any pair of two outputs constitutes a hypothetical input-output system with unknown input embedded as a state. The central idea underlying our approach is to derive the unknown input by designing an observer for the hypothetical input-output system. In this paper, we developed an unknown input observer (UIO) for input deconvolution in coprime multichannel systems. We provided a universal design algorithm as well as meaningful physical insights and inherent performance limitations associated with the algorithm. The validity and potential of our approach were illustrated using a case study of estimating central aortic BP waveform from two noninvasively acquired peripheral arterial pulse waveforms. The UIO could reduce the root-mean-squared error (RMSE) associated with the central aortic BP by up to 27.5% and 28.8% against conventional inverse filtering (IF) and peripheral arterial pulse scaling techniques.
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Ghasemi et al. (2020) studied Central aortic blood pressure estimation. Unknown input observer (UIO) for input deconvolution vs. Conventional inverse filtering (IF) and peripheral arterial pulse scaling techniques was evaluated on Root-mean-squared error (RMSE) associated with central aortic BP (Up to 28.8% reduction). An unknown input observer (UIO) for input deconvolution reduced the root-mean-squared error associated with central aortic BP estimation by up to 28.8% compared to conventional techniques.
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