Abstract
Impedance cardiography (ICG) is a simple and cheap method for acquiring hemodynamic parameters. Unfortunately, not all physiological influences on the ICG signal have yet been identified. In this work, the influence of heart and lung dynamics is analyzed using a simplified model of the human thorax with high temporal resolution. Simulations are conducted using the finite integration technique (FIT) with temporal resolution of 103 Hz. It is shown that changes in heart volume as well as conductivity changes of the lung have a high impact on the ICG signal, if analyzed separately. Considering the sum signal of both physiological sources, it can be shown that they compensate each other and thus do not contribute to the signal. This finding supports Kubicek’s model.
Highlights
One of the most common causes of death in Western Europe is chronic heart failure (CHF)
This task will be accomplished by simulations using finite integration technique (FIT) and an anatomical data set of a male human as a basis for a simplified dynamic model
The sum of the dynamic impedance measured at 100 kHz and including the volume change of the aorta has been compared to measured data of a male human and the results show excellent agreement (r = 0.94) using a correction factor [12]
Summary
One of the most common causes of death in Western Europe is chronic heart failure (CHF). Measures of the severity of this cardiovascular disease are hemodynamic parameters such as stroke volume (SV). The gold standard for measuring these parameters has been the thermodilution technique, which utilizes a pulmonary artery catheter. An alternative method for assessing hemodynamic parameters non-invasively and cost-effectively is ICG. ICG is not commonly used as a diagnostic method, because it is not considered to be valid [1]. One reason is the inaccuracy of the technology itself concerning SV calculations. Another possible reason for this is that processes in the human body during ICG measurements are widely unknown. One way to analyze where the current paths run and which tissue contributes significantly to the measurement result is to use computer simulations employing FIT. Other researchers have already examined multiple sources of the ICG signal, using various approaches: some works are based on simple geometries [2], others on real anatomical data, such as MRI data [3]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
