System dynamics of active and passive postural changes: Insights from principal dynamic modes analysis of baroreflex loop

Analytic and Integrative Framework for Understanding Human Sympathetic Arterial Baroreflex Function: Equilibrium Diagram of Arterial Pressure and Plasma Norepinephrine Level

Background

The sympathetic arterial baroreflex is a closed-loop feedback system for stabilizing arterial pressure (AP). Identification of unique functions of the closed system in humans is a challenge. Here we propose an analytic and integrative framework for identifying a static operating point and open-loop gain to characterize sympathetic arterial baroreflex in humans.

Methods and Results

An equilibrium diagram with two crossing functions of mechanoneural (MN) and neuromechanical (NM) arcs was analyzed during graded tilt maneuvers in seven healthy subjects. AP and plasma norepinephrine level (PNE), as a surrogate for sympathetic nerve activity, and were recorded after vagal modulation of heart function was blocked by atropine. The MN-arc curve was described as a locus of operating points during –7, 0, 15, and 60° head-up tilting (HUT) on a PNE-AP plane. The NM-arc curve was drawn as a line between operating points before and after ganglionic blockade (trimethaphan, 0.1 mg⋅ml^–1⋅kg^–1) during 0° or 15° HUT. Gain values were estimated from the slopes of these functional curves. Finally, an open-loop gain, which is a most important index for performance of arterial baroreflex, was given by a product of the gain values of MN (G_MN) and NM arcs (G_NM). Gain values of MN was 8.92 ± 3.07 pg⋅ml⁻¹⋅mmHg⁻¹; and G_NM at 0° and 15° HUT were 0.61 ± 0.08 and 0.36 ± 0.05 mmHg⋅ml⋅pg^–1, respectively. A postural change from supine to 15° HUT significantly reduced the open-loop gain from 5.62 ± 0.98 to 3.75 ± 0.62. The effects of HUT on the NM arc and open-loop gain seemed to be similar to those of blood loss observed in our previous animal studies.

Conclusion

An equilibrium-diagram analysis contributes to a quantitative and integrative understanding of function of human sympathetic arterial baroreflex.

A Strategy for Classification of "Vaginal vs. Cesarean Section" Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings

We propose objective and robust measures for the purpose of classification of "vaginal vs. cesarean section" delivery by investigating temporal dynamics and complex interactions between fetal heart rate (FHR) and maternal uterine contraction (UC) recordings from cardiotocographic (CTG) traces. Multivariate extension of empirical mode decomposition (EMD) yields intrinsic scales embedded in UC-FHR recordings while also retaining inter-channel (UC-FHR) coupling at multiple scales. The mode alignment property of EMD results in the matched signal decomposition, in terms of frequency content, which paves the way for the selection of robust and objective time-frequency features for the problem at hand. Specifically, instantaneous amplitude and instantaneous frequency of multivariate intrinsic mode functions are utilized to construct a class of features which capture nonlinear and nonstationary interactions from UC-FHR recordings. The proposed features are fed to a variety of modern machine learning classifiers (decision tree, support vector machine, AdaBoost) to delineate vaginal and cesarean dynamics. We evaluate the performance of different classifiers on a real world dataset by investigating the following classifying measures: sensitivity, specificity, area under the ROC curve (AUC) and mean squared error (MSE). It is observed that under the application of all proposed 40 features AdaBoost classifier provides the best accuracy of 91.8% sensitivity, 95.5% specificity, 98% AUC, and 5% MSE. To conclude, the utilization of all proposed time-frequency features as input to machine learning classifiers can benefit clinical obstetric practitioners through a robust and automatic approach for the classification of fetus dynamics.