Bayesian inference of multi-sensors impedance cardiography for detection of aortic dissection

@article{Badeli2021BayesianIO,
  title={Bayesian inference of multi-sensors impedance cardiography for detection of aortic dissection},
  author={Vahid Badeli and Sascha Ranftl and Gian Marco Melito and Alice Reinbacher-K{\"o}stinger and Wolfgang von der Linden and Katrin Ellermann and Oszk{\'a}r B{\'i}r{\'o}},
  journal={COMPEL - The international journal for computation and mathematics in electrical and electronic engineering},
  year={2021}
}
  • V. BadeliSascha Ranftl O. Bíró
  • Published 21 December 2021
  • Medicine
  • COMPEL - The international journal for computation and mathematics in electrical and electronic engineering
Purpose This paper aims to introduce a non-invasive and convenient method to detect a life-threatening disease called aortic dissection. A Bayesian inference based on enhanced multi-sensors impedance cardiography (ICG) method has been applied to classify signals from healthy and sick patients. Design/methodology/approach A 3D numerical model consisting of simplified organ geometries is used to simulate the electrical impedance changes in the ICG-relevant domain of the human torso. The… 
1 Citations

Figures and Tables from this paper

Cross-Entropy Learning for Aortic Pathology Classification of Artificial Multi-Sensor Impedance Cardiography Signals

A study of the accuracy as a function of the size of an aortic dissection yielded better results for a small false lumen with larger noise, which emphasizes the question of the feasibility of detecting aorta dissections in an early state.

References

SHOWING 1-10 OF 14 REFERENCES

On the Diagnosis of Aortic Dissection with Impedance Cardiography: A Bayesian Feasibility Study Framework with Multi-Fidelity Simulation Data

This work uses the inexpensive low-f fidelity simulation to learn about the expensive high-fidelity simulation, and combines two simulations: one simulation with a high fidelity and another Simulation with a low fidelity, and low and high computational costs accordingly.

Electrode Positioning to Investigate the Changes of the Thoracic Bioimpedance Caused by Aortic Dissection – A Simulation Study

Results show that the remarkable pathological changes in the aorta caused by aortic dissection alters the impedance cardiogram significantly.

Numerical Simulation of Conductivity Changes in the Human Thorax Caused by Aortic Dissection

In case of an aortic dissection (AD), the aortic shape as well as the blood flow will be altered in the region concerned. Based on the thoracic electrical bioimpedance technique that allows to

Aortic dissection: medical, interventional and surgical management

Although dilatation of the aorta (aortic aneurysms) increases the risk through greater wall stress, AAD can as well occur in patients with … and the risk increases with age.

Development of a portable setup suitable for in vivo measurement of the dielectric properties of biological tissues

In the present paper, a preliminary study for the development of a portable setup suitable for in vivo measurements of tissue dielectric properties is presented. The setup consists of a hand-held

Bayesian Probability Theory: Applications in the Physical Sciences

This book presents the roots, applications and numerical implementation of probability theory, and covers advanced topics such as maximum entropy distributions, stochastic processes, parameter estimation, model selection, hypothesis testing and experimental design.

The dielectric properties of biological tissues: I. Literature survey.

The dielectric properties of tissues have been extracted from the literature of the past five decades and presented in a graphical format. The purpose is to assess the current state of knowledge,

Probability theory: the logic of science

Foreword Preface Part I. Principles and Elementary Applications: 1. Plausible reasoning 2. The quantitative rules 3. Elementary sampling theory 4. Elementary hypothesis testing 5. Queer uses for

Handbook of Markov Chain Monte Carlo

A Markov chain Monte Carlo based analysis of a multilevel model for functional MRI data and its applications in environmental epidemiology, educational research, and fisheries science are studied.