Recent Advances in and Limitations of Cardiac Output Monitoring by Means of Electrical Impedance Tomography

Pikkemaat, Robert; Lundin, S.; Stenqvist, O.; Hilgers, Ralf-Dieter; Leonhardt, Steffen

doi:10.1213/ane.0000000000000241

Cited by 37 publications

(46 citation statements)

References 32 publications

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“…Some studies describe limitations and challenges in cardiovascular EIT monitoring, in particular concerning the monitoring of SV. Patterson et al (2001) concluded the cardiac EIT signal depends strongly on electrode position, lung volume and posture, in healthy volunteers. In pig experiments, Pikkemaat et al (2014) observed variations in the subject-specific scaling of the cardiac EIT signal, which A c c e p t e d M a n u s c r i p t were interpreted to stem from lung volume or heart and belt position.…”

Section: Introductionmentioning

confidence: 96%

Limitations and challenges of EIT-based monitoring of stroke volume and pulmonary artery pressure

et al. 2018

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Section: Introductionmentioning

confidence: 96%

Limitations and challenges of EIT-based monitoring of stroke volume and pulmonary artery pressure

et al. 2018

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“…These animals were kept in the current analyses, but a sensitivity analysis showed that the estimated reliabilities were not substantially affected by the inclusion or exclusion of these animals (see Web Appendix Part II). Note that the data for PEEP level 25 were included in the current analysis, as well as in the Pikkemaat et al study, although they were not explicitly mentioned in the latter.…”

Section: Case Studymentioning

confidence: 99%

“…were included in the current analysis, as well as in the Pikkemaat et al [8] study, although they were not explicitly mentioned in the latter. Figure 1 shows the individual profiles (gray lines) of ZSV as a function of measurement moment.…”

Section: Case Studymentioning

confidence: 99%

Estimating the reliability of repeatedly measured endpoints based on linear mixed‐effects models. A tutorial

Elst

Molenberghs

Hilgers

et al. 2016

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There are various settings in which researchers are interested in the assessment of the correlation between repeated measurements that are taken within the same subject (i.e., reliability). For example, the same rating scale may be used to assess the symptom severity of the same patients by multiple physicians, or the same outcome may be measured repeatedly over time in the same patients. Reliability can be estimated in various ways, for example, using the classical Pearson correlation or the intra-class correlation in clustered data. However, contemporary data often have a complex structure that goes well beyond the restrictive assumptions that are needed with the more conventional methods to estimate reliability. In the current paper, we propose a general and flexible modeling approach that allows for the derivation of reliability estimates, standard errors, and confidence intervals - appropriately taking hierarchies and covariates in the data into account. Our methodology is developed for continuous outcomes together with covariates of an arbitrary type. The methodology is illustrated in a case study, and a Web Appendix is provided which details the computations using the R package CorrMixed and the SAS software. Copyright © 2016 John Wiley & Sons, Ltd.

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“…A promising technology to non-invasively and continuously monitor CO is electrical impedance tomography (EIT). EIT uses multiple bioimpedance measurements recorded from electrodes placed around the chest, and, generally, uses physics-based approaches to recover the conductivity, which has been shown to correlate well with SV in several studies [9,10,11]. We have been developing a wearable EIT system for continuous monitoring of CO with low power constraints and potential smart-phone compatibility [12,13].…”

Section: Introductionmentioning

confidence: 99%

Noise-robust bioimpedance approach for cardiac output measurement

et al. 2019

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Objective. Congestive heart failure is a problem effecting millions of Americans. A continuous, non-invasive, telemonitoring device that can accurately monitor cardiac metrics could greatly help this population, reducing unnecessary hospitalizations and cost. Approach. Machine Learning (ML) algorithms trained on electrical-impedance tomography (EIT) data are presented for portable cardiac monitoring. The approach was validated on a simulated thorax and a measured tank experiment. A highly detailed 4D chest model (finite element method mesh and conductivity profiles) was developed utilizing the 4D XCAT phantom to provide realistic data. The ML algorithms were trained using databases that assumed the presence of poorly contacting electrodes without any assumptions of knowing which electrodes would be bad in the experiment. The trained ML algorithms were compared to EIT evaluated with and without removing bad electrodes. Main results. A regression Support Vector Machine (rSVM) and a Deep Neural Network (DNN) were found to be the most accurate and robust to poorly contacting electrodes while not needing to know which electrodes were in poor contact in the simulated and measured experiments, respectively. Significance. Although, the ML algorithms are not always better than EIT (with bad electrodes removed), the comparable results without needing a priori knowledge of which electrodes are bad is seen as a very promising feature. An evaluation of computational costs showed that the DNN required comparable computational to the other methods while requiring less memory, which could make the DNNs an attractive algorithm for a low-power, portable system. This work represents an important validation of the method using measured data, and model development, which is needed to apply this method on real clinical data. Additionally, the developed 4D simulated thorax model could be an important tool within the EIT community.

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Recent Advances in and Limitations of Cardiac Output Monitoring by Means of Electrical Impedance Tomography

Cited by 37 publications

References 32 publications

Limitations and challenges of EIT-based monitoring of stroke volume and pulmonary artery pressure

Limitations and challenges of EIT-based monitoring of stroke volume and pulmonary artery pressure

Estimating the reliability of repeatedly measured endpoints based on linear mixed‐effects models. A tutorial

Noise-robust bioimpedance approach for cardiac output measurement

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