title: Structural identifiability analysis of a cardiovascular system model.
abstract: The six-chamber cardiovascular system model of Burkhoff and Tyberg has been used in several theoretical and experimental studies. However, this cardiovascular system model (and others derived from it) are not identifiable from any output set. In this work, two such cases of structural non-identifiability are first presented. These cases occur when the model output set only contains a single type of information (pressure or volume). A specific output set is thus chosen, mixing pressure and volume information and containing only a limited number of clinically available measurements. Then, by manipulating the model equations involving these outputs, it is demonstrated that the six-chamber cardiovascular system model is structurally globally identifiable. A further simplification is made, assuming known cardiac valve resistances. Because of the poor practical identifiability of these four parameters, this assumption is usual. Under this hypothesis, the six-chamber cardiovascular system model is structurally identifiable from an even smaller dataset. As a consequence, parameter values computed from limited but well-chosen datasets are theoretically unique. This means that the parameter identification procedure can safely be performed on the model from such a well-chosen dataset. Thus, the model may be considered suitable for use in diagnosis.
authors.affiliation: University of Liège (ULg), GIGA-In Silico Medicine, Liège, Belgium. Electronic address: a.pironet@ulg.ac.be.
authors.forename: Antoine
authors.initials: A
authors.lastname: Pironet
authors.affiliation: University of Liège (ULg), GIGA-In Silico Medicine, Liège, Belgium.
authors.forename: Pierre C
authors.initials: PC
authors.lastname: Dauby
authors.affiliation: University of Canterbury, Department of Mechanical Engineering, Christchurch, New Zealand.
authors.forename: J Geoffrey
authors.initials: JG
authors.lastname: Chase
authors.affiliation: University of Canterbury, Department of Mechanical Engineering, Christchurch, New Zealand.
authors.forename: Paul D
authors.initials: PD
authors.lastname: Docherty
authors.affiliation: University of Canterbury, Department of Mechanical Engineering, Christchurch, New Zealand.
authors.forename: James A
authors.initials: JA
authors.lastname: Revie
authors.affiliation: University of Liège (ULg), GIGA-In Silico Medicine, Liège, Belgium.
authors.forename: Thomas
authors.initials: T
authors.lastname: Desaive
mesh_terms: D000465:Algorithms; D001794:Blood Pressure; D001810:Blood Volume; D002320:Cardiovascular Physiological Phenomena; D002319:Cardiovascular System / Q000033:anatomy & histology*; D008955:Models, Cardiovascular
keywords: Identifiability; Lumped-parameter model; Parameter identification; Physiological model