title: Cardiovascular regulation: a modelling approach.
abstract: A detailed analysis of autonomic cardiovascular control (ACVC) may provide a key to a better understanding of the mechanisms underlying postflight orthostatic hypotension. The central substrate of human ACVC is not directly accessible to measurements and observation in space research. Modelling--supporting inference and physiological reasoning--is a valuable tool to disclose its involvement We are currently determining the suitability of artificial neural networks (ANN's) as a model of the central substrate of ACVC. Having conducted a number of experiments with simulated tilt test data to clarify the choice of input coding and of architectural biases in network training we will now report on the approximation of data obtained from human subjects during preparation of the German MIR'97 and D-2 missions.
authors.affiliation: Bonn University, Department of Computer Science I, FRG. aster@cs.uni-bonn.de
authors.forename: A
authors.initials: A
authors.lastname: Asteroth
authors.forename: K
authors.initials: K
authors.lastname: Moller
authors.forename: F
authors.initials: F
authors.lastname: Baisch
authors.forename: L
authors.initials: L
authors.lastname: Beck
authors.forename: J
authors.initials: J
authors.lastname: Drescher
mesh_terms: D000222:Adaptation, Physiological; D001341:Autonomic Nervous System; D001794:Blood Pressure; D002320:Cardiovascular Physiological Phenomena; D003198:Computer Simulation; D006339:Heart Rate; D006439:Hemodynamics; D006801:Humans; D007024:Hypotension, Orthostatic / Q000209:etiology; D008955:Models, Cardiovascular; D016571:Neural Networks, Computer; D013026:Space Flight; D018667:Tilt-Table Test; D014655:Vascular Resistance; D014893:Weightlessness