Unique parameter identification for cardiac diagnosis in critical care using minimal data sets (2010)
Type of ContentJournal Article
PublisherUniversity of Canterbury. Electrical and Computer Engineering
University of Canterbury. Mechanical Engineering
Lumped parameter approaches for modelling the cardiovascular system typically havemany parameters of which a significant percentage are often not identifiable from limited data sets. Hence, significant parts of the model are required to be simulated with little overall effect on the accuracy of data fitting, as well as dramatically increasing the complexity of parameter identification. This separates sub-structures of more complex cardiovascular system models to create uniquely identifiable simplified models that are one to one with the measurements. In addition, a new concept of parameter identification is presented where the changes in the parameters are treated as an actuation force into a feed back control system, and the reference output is taken to be steady state values of measured volume and pressure. The major advantage of the method is that when it converges, it must be at the global minimum so that the solution that best fits the data is always found. By utilizing continuous information from the arterial/pulmonary pressurewaveforms and the end-diastolic time, it is shown that potentially, the ventricle volume is not required in the data set, which was a requirement in earlier published work. The simplified models can also act as a bridge to identifying more sophisticated cardiac models, by providing an initial set of patient specific parameters that can reveal trends and interactions in the data over time. The goal is to apply the simplified models to retrospective data on groups of patients to help characterize population trends or un-modelled dynamics within known bounds. These trends can assist in improved prediction of patient responses to cardiac disturbance and therapy intervention with potentially smaller and less invasive data sets. In this way a more complex model that takes into account individual patient variation can be developed, and applied to the improvement of cardiovascular management in critical care.
CitationHann, C.E., Chase, J.G., Desaive, T., Froissart, C.B., Revie, J., Stevenson, D., Lambermont, B., Ghuysen, A., Kolh, P., Shaw, G.M. (2010) Unique parameter identification for cardiac diagnosis in critical care using minimal data sets. Computer Methods and Programs in Biomedicine, 99(1), pp. 75-87.
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Keywordsmodel-based cardiac diagnosis; cardiovascular system; integral-based parameter identification; pressure waveform; ECG; Intensive Care Unit
ANZSRC Fields of Research32 - Biomedical and clinical sciences::3201 - Cardiovascular medicine and haematology::320101 - Cardiology (incl. cardiovascular diseases)
32 - Biomedical and clinical sciences::3202 - Clinical sciences::320212 - Intensive care
40 - Engineering::4003 - Biomedical engineering::400303 - Biomechanical engineering
01 - Mathematical Sciences::0103 - Numerical and Computational Mathematics
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Stevenson, D.; Revie, J.A.; Chase, Geoff; Hann, C.E.; Shaw, Geoff; Lambermont, B.; Ghuysen, A.; Kolh, P.; Desaive, T. (University of Canterbury. Electrical and Computer EngineeringUniversity of Canterbury. Mechanical Engineering, 2012)Background: Cardiac elastances are highly invasive to measure directly, but are clinically useful due to the amount of information embedded in them. Information about the cardiac elastance, which can be used to estimate ...
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