CARG Health Devices: Simulation and Modeling of Physiological Devices and Systems

Modeling and Simulation
The CARG team works on creating simulations and models for health-related devices and systems. Our goal is to make biomedical devices work better and more efficiently using advanced computational methods.

Motivation

CARG is motivated by the need to make health monitoring devices more accurate and reliable. By developing models and simulations, we aim to understand and improve how these devices interact with the human body.

Current Research

Here are some of their recent projects:

Future Directions

Looking ahead, CARG wants to:

Motivation

Research Papers

Yoshida, M, Dajani, HR, Ando, S, Shimizu, S, Bolic, M, Groza, V. Analysis of the effect of CPAP on hemodynamics using clinical data and a theoretical model: CPAP therapy decreases cardiac output mechanically but increases it via afterload. Sleep Medicine, 2024.
- Description: Analysis of the effect of CPAP on hemodynamics using clinical data and a theoretical model: CPAP therapy decreases cardiac output mechanically but increases it via afterload published in Sleep Medicine.
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Bolic, M. Simulating the Effects of Melanin and Air Gap Depth on the Accuracy of Reflectance Pulse Oximeters. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 1: BIODEVICES, pages 64-71 ISBN: 978-989-758-631-6; ISSN: 2184-4305.
- Description: Evaluates the effects of skin color on the accuracy of oxygen saturation estimates.
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Bolic, M. Pervasive Cardiovascular and Respiratory Monitoring Devices: Model-Based Design, 2023.
- Description: The book about modeling and simulation of biomedical devices.
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Olhosseiny, H. H., Mirzaloo, M., Bolic, M., Dajani, H. R., Groza, V., Yoshida, M.. Identifying High Risk of Atherosclerosis Using Deep Learning and Ensemble Learning. 2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 2021.
- Description: The paper presents a classification of patients based on physiological signals.
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Researchers

External Collaborators

Alumni