Heart rate variability studies on ambulatory subjects using EKG derived respiration

Power spectral analysis of heart rate variability (HRV) provides a powerful non-invasive too] for exploring the balance between sympathetic and parasympathetic components of the autonomic nervous system. In the spectrum of HRV, there are three distinct peaks. The high frequency (HF) band (0.15 to 0.4 Hz) is correlated with parasympathetic activity and this is the band of interest \u27in the study. Two OTOUPS Of SL113jeCtS were considered in this work, stroke survivors and subjects performing an oral presentation in front of an audience. Data were collected with a Holter monitor to allow for ambulatory recording. Respiration was derived from the EKG and was used to determine the parasympathetic frequency band in the HRV spectrum. The power spectral analysis of HRV from stroke survivors revealed significantly less parasympathetic activity than in control subjects. Sustained low variation of heart rate suggested that stroke\u27 survivors may suffer from one or more aspects of autonomic nervous system imbalance. The power spectral analysis of HRV from normal subjects during a formal presentation revealed that vagal activity decreased significantly with the anticipation of making a presentation as well as during the presentation in the presence of an audience, when compared to vagal activity during a presentation without an audience

Investigation of the baroreflex of the rat : steady state and dynamic features

The baroreflex is one of the most important feedback systems in the body to maintain blood pressure variation within the homeostatic range. In this dissertation, the important features of the carotid and aortic baroreflexes have been extensively investigated on ventilated, central nervous system intact, neuromuscular blocked (NMB) rats using different control system and signal processing tools. Studies have demonstrated that sinoaortic denervation (SAD) caused substantial increases in the blood pressure variability. Comparing the pre- and post-SAD blood pressure spectra, there was a significant increase of power in the very low frequency region (0.00195 -0.2 Hz), and a significant decrease of power in the low frequency region (0.2 - 0.6 Hz) after SAD. The dominant power change after SAD was in the very low frequency region of the blood pressure spectra. The carotid and aortic baroreflexes were accessed by volumetric manipulation of the carotid sinus and electrical manipulation of the aortic depressor nerve (ADN) using step and sinusoidal stimulations. Myelinated ADN-A fibers and myelinated + unmyelinated ADN-A+C fibers were accessed separately in the experiments. Results showed that the baroreflex functions as a \u27low-pass\u27 filter, with -3dB cutoff frequency at approximately \u3c0. I Hz. The major working area of the baroreflex system is in the VLF region of the blood pressure spectra. The estimated system transportation lag was 1.07s, which would cause the baroreflex system to oscillate at frequencies around 0.4 Hz. Analyses demonstrated that it is not likely that the baroreflex is activated only occasionally, such as in response to postural shifts, but operates continuously to bring the blood pressure into balance. It is theoretically and experimentally demonstrated that the absolute gain of the open-loop baroreflex system can be predicted by the ratio of the pre-and post- blood pressure amplitude spectra

Is Homophily a Necessity for Graph Neural Networks?

Graph neural networks (GNNs) have shown great prowess in learning representations suitable for numerous graph-based machine learning tasks. When applied to semi-supervised node classification, GNNs are widely believed to work well due to the homophily assumption ("like attracts like"), and fail to generalize to heterophilous graphs where dissimilar nodes connect. Recent works design new architectures to overcome such heterophily-related limitations, citing poor baseline performance and new architecture improvements on a few heterophilous graph benchmark datasets as evidence for this notion. In our experiments, we empirically find that standard graph convolutional networks (GCNs) can actually achieve better performance than such carefully designed methods on some commonly used heterophilous graphs. This motivates us to reconsider whether homophily is truly necessary for good GNN performance. We find that this claim is not quite true, and in fact, GCNs can achieve strong performance on heterophilous graphs under certain conditions. Our work carefully characterizes these conditions, and provides supporting theoretical understanding and empirical observations. Finally, we examine existing heterophilous graphs benchmarks and reconcile how the GCN (under)performs on them based on this understanding