This paper demonstrates preliminary in-human validity of a novel subject-specific approach to estimation of central aortic blood pressure (CABP) from peripheral circulatory waveforms. In this "Individualized Transfer Function" (ITF) approach, CABP is estimated in two steps. First, the circulatory dynamics of the cardiovascular system are determined via model-based system identification, in which an arterial tree model is characterized based on the circulatory waveform signals measured at the body's extremity locations. Second, CABP waveform is estimated by de-convolving peripheral circulatory waveforms from the arterial tree model. The validity of the ITF approach was demonstrated using experimental data collected from 13 cardiac surgery patients. Compared with the invasive peripheral blood pressure (BP) measurements, the ITF approach yielded significant reduction in errors associated with the estimation of CABP, including 1.9-2.6 mmHg (34-42 %) reduction in BP waveform errors (p < 0.05) as well as 5.8-9.1 mmHg (67-76 %) and 6.0-9.7 mmHg (78-85 %) reductions in systolic and pulse pressure (SP and PP) errors (p < 0.05). It also showed modest but significant improvement over the generalized transfer function approach, including 0.1 mmHg (2.6 %) reduction in BP waveform errors as well as 0.7 (20 %) and 5.0 mmHg (75 %) reductions in SP and PP errors (p < 0.05).

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In this paper, we assess the validity of two alternative tube-load models for describing the relationship between central aortic and peripheral arterial blood pressure (BP) waveforms in humans. In particular, a single-tube (1-TL) model and a serially connected two-tube (2-TL) model, both terminated with a Windkessel load, are considered as candidate representations of central aortic-peripheral arterial path. Using the central aortic, radial and femoral BP waveform data collected from eight human subjects undergoing coronary artery bypass graft with cardiopulmonary bypass procedure, the fidelity of the tube-load models was quantified and compared with each other. Both models could fit the central aortic-radial and central aortic-femoral BP waveform pairs effectively. Specifically, the models could estimate pulse travel time (PTT) accurately, and the model-derived frequency response was also close to the empirical transfer function estimate obtained directly from the central aortic and peripheral BP waveform data. However, 2-TL model was consistently superior to 1-TL model with statistical significance as far as the accuracy of the central aortic BP waveform was concerned. Indeed, the average waveform RMSE was 2.52 mmHg versus 3.24 mmHg for 2-TL and 1-TL models, respectively (p < 0.05); the r² value between measured and estimated central aortic BP waveforms was 0.96 and 0.93 for 2-TL and 1-TL models, respectively (p < 0.05). We concluded that the tube-load models considered in this paper are valid representations that can accurately reproduce central aortic-radial/femoral BP waveform relationships in humans, although the 2-TL model is preferred if an accurate central aortic BP waveform is highly desired.

In this work, we consider distributed adaptive high‐gain extended Kalman filtering for nonlinear systems subject to data losses and delays in communications. Specifically, we consider a class of nonlinear systems that consist of several subsystems interacting with each other via their states. A local adaptive high‐gain extended Kalman filter is designed for each subsystem and the distributed estimators communicate to exchange the information. Each subsystem estimator takes the advantage of a predictor accounting for the delays and data losses simultaneously. The predictor of each subsystem is used to generate state predictions of interacting subsystems for interaction compensation. To get a reliable prediction, the predictors are designed based on a prediction‐update algorithm. The convergence of the proposed distributed state estimation is ensured under sufficient conditions handling communication delays and data losses. Finally, a chemical process example is used to evaluate the applicability and effectiveness of the proposed design. © 2016 American Institute of Chemical Engineers AIChE J, 62: 4321–4333, 2016

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