Applying NeuralODE-based Cardiovascular Model Identification for Experimental Data Analysis

Publication Name: Saci 2024 18th IEEE International Symposium on Applied Computational Intelligence and Informatics Proceedings

Publication Date: 2024-01-01

Volume: Unknown

Issue: Unknown

Page Range: 437-442

Description:

Recent model-based diagnostic methods have been found promising to provide non-invasive perfusion markers to assess the efficacy of fluid therapy, the most common treatment method for acute circulatory failure (ACF). The development of these model-based diagnostic methods requires the identification of the central arterial pressure curve based on the femoral arterial pressure. This current study presents improvements of the previously suggested NeuralODE-based identification method by suggesting the use of a physiologically interpretable parameter set of the Tube-load model-based transfer function for the physiological system analysis and suggesting a calculation method decreasing the measurement error-caused uncertainty of the identification parameter, called pulse transfer time.

Open Access: Yes

DOI: 10.1109/SACI60582.2024.10619737

Authors - 6