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Non-integer Approach to Modelling Compartments of Cardiovascular Circulatory System

17 pagesPublished: December 11, 2024

Abstract

For predicting cardiovascular diseases, mathematical modelling of the cardiovascular system has been proven to be a powerful asset. The governing idea is to analyse it through compartments as multiple connected subsystems with inputs and outputs. In this paper, models were identified for four subsystems of input-output sequence (left ventricle - left atrium - ascending aorta - descending aorta - left common carotid artery) by modelling frequency response. The data set used for model identification consisted of blood pressure during four consecutive heart contractions of four circulatory segments from clinical trials performed on a pig. The goal is to discover a linear model with a non-integer order that succinctly represents the process, outperforming high-order autoregressive exogenous input (ARX) integer models. This model identification occurs non-parametrically, aiming to achieve the best smooth fit in the frequency domain by minimizing the difference between real measurements and model predictions using the particle swarm optimization (PSO) algorithm.

Keyphrases: cardiovascular system, fractional order systems, frequency response, model identification, particle swarm optimization

In: Varvara L Turova, Andrey E Kovtanyuk and Johannes Zimmer (editors). Proceedings of 3rd International Workshop on Mathematical Modeling and Scientific Computing, vol 104, pages 109-125.

BibTeX entry
@inproceedings{MMSC2024:Non_integer_Approach_Modelling,
  author    = {Iva Janković and Mirna Kapetina Radović},
  title     = {Non-integer Approach to Modelling Compartments of Cardiovascular Circulatory System},
  booktitle = {Proceedings of  3rd International Workshop on Mathematical Modeling and Scientific Computing},
  editor    = {Varvara L Turova and Andrey E Kovtanyuk and Johannes Zimmer},
  series    = {EPiC Series in Computing},
  volume    = {104},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/DfQHr},
  doi       = {10.29007/lsl9},
  pages     = {109-125},
  year      = {2024}}
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