Numerical Simulation of Various Electrode Configurations in Impedance Cardiography to Identify Aortic Dissection

2020 ◽  
pp. 51-54 ◽  
Author(s):  
Alice Reinbacher-Köstinger ◽  
Vahid Badeli ◽  
Gian Marco Melito ◽  
Christian Magele ◽  
Oszkar Bíró
Keyword(s):  
Numerical Simulation ◽  
Aortic Dissection ◽  
Impedance Cardiography

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

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1661
Author(s):  
Tobias Spindelböck ◽  
Sascha Ranftl ◽  
Wolfgang von der Linden
Keyword(s):  
Blood Flow ◽  
Aortic Dissection ◽  
Network Architecture ◽  
Impedance Cardiography ◽  
False Lumen ◽  
Flow Characteristics ◽  
Virtual Patient ◽  
Cross Entropy ◽  
Aortic Pathology ◽  
Aortic Dissections

An aortic dissection, a particular aortic pathology, occurs when blood pushes through a tear between the layers of the aorta and forms a so-called false lumen. Aortic dissection has a low incidence compared to other diseases, but a relatively high mortality that increases with disease progression. An early identification and treatment increases patients’ chances of survival. State-of-the-art medical imaging techniques have several disadvantages; therefore, we propose the detection of aortic dissections through their signatures in impedance cardiography signals. These signatures arise due to pathological blood flow characteristics and a blood conductivity that strongly depends on the flow field, i.e., the proposed method is, in principle, applicable to any aortic pathology that changes the blood flow characteristics. For the signal classification, we trained a convolutional neural network (CNN) with artificial impedance cardiography data based on a simulation model for a healthy virtual patient and a virtual patient with an aortic dissection. The network architecture was tailored to a multi-sensor, multi-channel time-series classification with a categorical cross-entropy loss function as the training objective. The trained network typically yielded a specificity of (93.9±0.1)% and a sensitivity of (97.5±0.1)%. 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 aortic dissections in an early state.

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

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Vahid Badeli ◽  
Sascha Ranftl ◽  
Gian Marco Melito ◽  
Alice Reinbacher-Köstinger ◽  
Wolfgang Von Der Linden ◽  
...  
Keyword(s):  
Probability Theory ◽  
Bayesian Inference ◽  
Aortic Dissection ◽  
Electrical Impedance ◽  
Impedance Cardiography ◽  
Bayesian Probability ◽  
Content Type ◽  
Non Invasive ◽  
Life Threatening ◽  
Relevant Domain

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 Bayesian probability theory is used for detecting an aortic dissection, which provides information about the probabilities for both cases, a dissected and a healthy aorta. Thus, the reliability and the uncertainty of the disease identification are found by this method and may indicate further diagnostic clarification. Findings The Bayesian classification shows that the enhanced multi-sensors ICG is more reliable in detecting aortic dissection than conventional ICG. Bayesian probability theory allows a rigorous quantification of all uncertainties to draw reliable conclusions for the medical treatment of aortic dissection. Originality/value This paper presents a non-invasive and reliable method based on a numerical simulation that could be beneficial for the medical management of aortic dissection patients. With this method, clinicians would be able to monitor the patient’s status and make better decisions in the treatment procedure of each patient.

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

Proceedings ◽  
2019 ◽  
Vol 33 (1) ◽  
pp. 24 ◽  
Author(s):  
Sascha Ranftl ◽  
Gian Marco Melito ◽  
Vahid Badeli ◽  
Alice Reinbacher-Köstinger ◽  
Katrin Ellermann ◽  
...  
Keyword(s):  
Aortic Dissection ◽  
Impedance Cardiography ◽  
Heart Function ◽  
Imaging Techniques ◽  
Computational Cost ◽  
Circulatory System ◽  
Magnetic Resonance Tomography ◽  
Computational Effort ◽  
High Fidelity ◽  
Regression Problem

Aortic dissection is a cardiovascular disease with a disconcertingly high mortality. When it comes to diagnosis, medical imaging techniques such as Computed Tomography, Magnetic Resonance Tomography or Ultrasound certainly do the job, but also have their shortcomings. Impedance cardiography is a standard method to monitor a patients heart function and circulatory system by injecting electric currents and measuring voltage drops between electrode pairs attached to the human body. If such measurements distinguished healthy from dissected aortas, one could improve clinical procedures. Experiments are quite difficult, and thus we investigate the feasibility with finite element simulations beforehand. In these simulations, we find uncertain input parameters, e.g., the electrical conductivity of blood. Inference on the state of the aorta from impedance measurements defines an inverse problem in which forward uncertainty propagation through the simulation with vanilla Monte Carlo demands a prohibitively large computational effort. To overcome this limitation, we combine two simulations: one simulation with a high fidelity and another simulation with a low fidelity, and low and high computational costs accordingly. We use the inexpensive low-fidelity simulation to learn about the expensive high-fidelity simulation. It all boils down to a regression problem—and reduces total computational cost after all.

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

2019 ◽  
Vol 55 (6) ◽  
pp. 1-4 ◽  
Author(s):  
Alice Reinbacher-Kostinger ◽  
Vahid Badeli ◽  
Oszkar Biro ◽  
Christian Magele
Keyword(s):  
Numerical Simulation ◽  
Aortic Dissection ◽  
Human Thorax

scholarly journals Electrode positioning to investigate the changes of the thoracic bioimpedance caused by aortic dissection – a simulation study

2020 ◽  
Vol 11 (1) ◽  
pp. 38-48
Author(s):  
V. Badeli ◽  
G. M. Melito ◽  
A. Reinbacher-Köstinger ◽  
O. Bíró ◽  
K. Ellermann
Keyword(s):  
Aortic Dissection ◽  
Simulation Model ◽  
Impedance Cardiography ◽  
Patient Specific ◽  
Non Invasive ◽  
Impedance Cardiogram ◽  
Aortic Pathology ◽  
Invasive Method ◽  
Electrode Positioning ◽  
Transthoracic Electrical Impedance

AbstractImpedance cardiography (ICG) is a non-invasive method to evaluate several cardiodynamic parameters by measuring the cardiac-synchronous changes in the dynamic transthoracic electrical impedance. ICG allows us to identify and quantify conductivity changes inside the thorax by measuring the impedance on the thorax during a cardiac cycle. Pathologic changes in the aorta, like aortic dissection, will alter the aortic shape as well as the blood flow and consequently, the impedance cardiogram. This fact distorts the evaluated cardiodynamic parameters, but it could lead to the possibility to identify aortic pathology. A 3D numerical simulation model is used to compute the impedance changes on the thorax surface in case of the type B aortic dissection. A sensitivity analysis is applied using this simulation model to investigate the suitability of different electrode configurations considering several patient-specific cases. Results show that the remarkable pathological changes in the aorta caused by aortic dissection alters the impedance cardiogram significantly.

scholarly journals Bayesian Uncertainty Quantification with Multi-Fidelity Data and Gaussian Processes for Impedance Cardiography of Aortic Dissection

Entropy ◽  
2019 ◽  
Vol 22 (1) ◽  
pp. 58
Author(s):  
Sascha Ranftl ◽  
Gian Marco Melito ◽  
Vahid Badeli ◽  
Alice Reinbacher-Köstinger ◽  
Katrin Ellermann ◽  
...  
Keyword(s):  
Machine Learning ◽  
Finite Element ◽  
Aortic Dissection ◽  
Uncertainty Quantification ◽  
Impedance Cardiography ◽  
Finite Element Simulations ◽  
High Fidelity ◽  
Computational Engineering ◽  
Data Problem ◽  
Fully Bayesian

In 2000, Kennedy and O’Hagan proposed a model for uncertainty quantification that combines data of several levels of sophistication, fidelity, quality, or accuracy, e.g., a coarse and a fine mesh in finite-element simulations. They assumed each level to be describable by a Gaussian process, and used low-fidelity simulations to improve inference on costly high-fidelity simulations. Departing from there, we move away from the common non-Bayesian practice of optimization and marginalize the parameters instead. Thus, we avoid the awkward logical dilemma of having to choose parameters and of neglecting that choice’s uncertainty. We propagate the parameter uncertainties by averaging the predictions and the prediction uncertainties over all the possible parameters. This is done analytically for all but the nonlinear or inseparable kernel function parameters. What is left is a low-dimensional and feasible numerical integral depending on the choice of kernels, thus allowing for a fully Bayesian treatment. By quantifying the uncertainties of the parameters themselves too, we show that “learning” or optimising those parameters has little meaning when data is little and, thus, justify all our mathematical efforts. The recent hype about machine learning has long spilled over to computational engineering but fails to acknowledge that machine learning is a big data problem and that, in computational engineering, we usually face a little data problem. We devise the fully Bayesian uncertainty quantification method in a notation following the tradition of E.T. Jaynes and find that generalization to an arbitrary number of levels of fidelity and parallelisation becomes rather easy. We scrutinize the method with mock data and demonstrate its advantages in its natural application where high-fidelity data is little but low-fidelity data is not. We then apply the method to quantify the uncertainties in finite element simulations of impedance cardiography of aortic dissection. Aortic dissection is a cardiovascular disease that frequently requires immediate surgical treatment and, thus, a fast diagnosis before. While traditional medical imaging techniques such as computed tomography, magnetic resonance tomography, or echocardiography certainly do the job, Impedance cardiography too is a clinical standard tool and promises to allow earlier diagnoses as well as to detect patients that otherwise go under the radar for too long.

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