Study Of Physiological Systems Research Articles

System identification of physiological systems using short data segments.

System identification of physiological systems poses unique challenges, especially when the structure of the system under study is uncertain. Nonparametric techniques can be useful for identifying system structure, but these typically assume stationarity and require large amounts of data. Both of these requirements are often not easily obtained in the study of physiological systems. Ensemble methods for time-varying nonparametric estimation have been developed to address the issue of stationarity, but these require an amount of data that can be prohibitive for many experimental systems. To address this issue, we developed a novel algorithm that uses multiple short data segments. Using simulation studies, we showed that this algorithm produces system estimates with lower variability than previous methods when limited data are present. Furthermore, we showed that the new algorithm generates time-varying system estimates with lower total error than an ensemble method. Thus, this algorithm is well suited for the identification of physiological systems that vary with time or from which only short segments of stationary data can be collected.

Modelling of dynamic cerebral pressure autoregulation using sequential genetic algorithm

Accurate modelling is desirable for analysis and clinical studies of physiological systems. The present work provides methodology for fully automated sequential genetic algorithm (SGA) for auto regressive exogenous (ARX) modelling. The SGA has been implemented to determine proper model structure and thereafter model parameters. The proposed algorithm has been tested on known ARX model and sunspot data modelling problem. Finally, SGA has been applied to model the dynamic cerebral autoregulation (CA) system. The results are promising and models obtained using SGA are better as compared to standard least square (LS) algorithms and can be reliably applied to model physiological system.

Gating modifier peptides as probes of pancreatic β-cell physiology

Pancreatic β-cells depolarize in response to glucose and fire calcium-dependent actions potentials that trigger insulin secretion. The major current responsible for action potential repolarization in these cells is a delayed rectifier and Kv2.1 subunits are thought be a major contributor of the delayed rectifier channels. Hence, blockers of Kv2.1 channels might prolong action potentials and enhance calcium influx and insulin secretion. However, the lack of specific small molecule Kv2.1 inhibitors has hindered the testing of this mechanism. Importantly, several gating modifier peptides inhibit Kv2.1 channels in a relatively specific fashion. Hanatoxin (HaTX) and guangxitoxin-1 (GxTX-1) are examples that have been used to probe the role of Kv2.1 channels in β-cell physiology. Both HaTX and GxTX-1 strongly inhibit the Kv current of β-cells from various species, arguing that Kv2.1 subunits contribute significantly to the β-cell delayed rectifier. GxTX-1 prolongs glucose-triggered action potentials, enhances glucose-dependent intracellular calcium elevations and augments glucose-dependent insulin secretion. Taken together, these data suggest that blockers of Kv2.1 channels may be a useful approach to the design of novel therapeutic agents for the treatment of type 2 diabetes. These studies highlight the utility of gating modifier peptides in the study of physiological systems.

SPACED OUT MITOCHONDRIA

![Figure][1] Studies of physiological systems under extreme conditions teach us important lessons regarding the basics of life. One fascinating extreme condition is space travel. Space travel affects physiological functions in many ways, and one of its key signatures is muscle atrophy. Muscle

Introduction to Bioengineering

List of contributors. 1: W. Goldsmith: Biomechanics of solids. 2: L. Talbot: Fundamentals of fluid mechanics. 3: S.A. Berger: Physiological fluid mechanics. 4: M.C. Williams: Mass transfer. 5: T.K. Eto and B. Rubinsky: Bioheat transfer. 6: E.L. Keller: The modeling approach to the study of physiological systems. 7: E.R. Lewis: A brief introduction to network theory. 8: R.B. Martin: Biomaterials. 9: H.B. Skinner: The interaction of biomatrials and biomechanics. 10: S.L. Lehman, R. Kram and C.T. Farley: Locomotion and muscle biomechanics. 11: P.D. Grossman and D.S. Soane: Principles of electrophoretic separations. 12: T.F. Budinger: Medical imaging. 13: S. Kaplan and H. Maccabee: Biological applications of ionizing radiation. 14: C. Susskind: Bioeffects of nonionizing electromagnetic fields. Appendix A: Linear transforms. Index

Episodes of low-dimensional self-organized dynamics from electroencephalographic alpha-signals.

Self-organized neuronal dynamics revealed by cortical alpha-rhythms occur as episodes, which are rarely observed without extraction of the alpha-band from the other spectral components. Three episodes of an unusually long duration of 10 s, two with no signal processing after data recording at the clinic, are described and show evidence of low-dimensional alpha-dynamics. The evidence is gained from an analysis of scaled structures appearing in families of slope curves of the correlation integrals and is checked against time reparametrization. The data for the two unprocessed 10-s episodes are used for a test of the methodology, as well as a re-examination of the adequacy of the model of an autonomous dynamic system in steady state and of the concept of an attractor in brain dynamics investigations. Striking evidence for the model's inadequacy is provided by the episode of subject S1. In this example five consecutive overlapping 6-s sections do show evidence for low-dimensional dynamics, whereas the 10-s section containing those sections does not. The episode of subject 1 provides an example of alpha-activity which may involve self-organized dynamics extending down to low frequencies. The system (the neuronal network) showing episodes of attractor-ruled dynamics, under conditions of blurred and smoothly fading out evidence that it stays on an attractor, is designated as being ruled by a 'shadow-attractor'. This concept is compared with that of a 'quasi-attractor' introduced by H. Haken in studies of physiological systems. One possible mechanism for the observed episodes is proposed, based on a time-dependent number of enslaved sub-systems.

Generalized eigenvector algorithm for nonlinear system identification with non-white inputs.

Traditional methods for nonlinear system identification require a white, Gaussian, test input, a restriction that has limited their usability in many fields. In this study, we address the problem of identifying the dynamics of a nonlinear system when the input is highly colored-a restriction commonly encountered in the study of physiological systems. An extension of the parallel cascade method is developed that is optimal in a constrained minimum mean squared error sense and exactly corrects for the distortion induced by the non-white input spectrum. However, this correction is a deconvolution, which may become extremely ill-conditioned if the input spectrum departs significantly from whiteness; to confront this, we develop a low-rank projection operation that stabilizes the deconvolution. The overall algorithm is robust and places few requirements on the nature of the test input. Practical application of this new method is demonstrated by using it to identify a known analog nonlinear system from experimental data.

Analysis of the neuromuscular system using random parameter models

Noninvasive procedures for studies of physiological systems using real-time experimental data have attracted considerable attention from researchers recently. The primary objective is to avoid measurements of the system input signals, which can generally be obtained only by invasive clinical methods. It is envisaged that system-theoretic fundamentals along with techniques of system identification and estimation may be utilized effectively to achieve this goal. In the analysis of the neuromuscular system (NMS), the following factors are involved: (a) assumptions about the possible stochastic features of the input, categorizing them into various classes for test purposes; (b) specification of the system structure using the actual anatomical and physiological information, and the stochastic nature of the NMS output; and (c) application of a class of multipath random models is proposed. A statistical approach is adopted for analyzing the multipath model to estimate the probable behavior of the NMS. The proposed multipath model contains in each path a realization of a random parameter model which is a cascade of random nonlinear and linear blocks. This approach shows that the multipath models with nonlinearities in each path, have an expected output that is linearly related to the input under certain conditions. This approach aims to decompose the clinical electromyograms into their constituent motor-unit action potentials and their configurational and firing properties.