Multi-Channel Trans-Impedance Leadforming for Cardiopulmonary Monitoring: Algorithm Development and Feasibility Assessment Using In Vivo Animal Data
Overview
Biophysics
Affiliations
Objective: The objectives of this study were to develop a multi-channel trans-impedance leadforming method for beat-to-beat stroke volume (SV) and breath-by-breath tidal volume (TV) measurements and assess its feasibility on an existing in vivo animal dataset.
Methods: A deterministic leadforming algorithm was developed to extract a cardiac volume signal (CVS) and a respiratory volume signal (RVS) from 208-channel trans-impedance data acquired every 20 ms by an electrical impedance tomography (EIT) device. SV and TV values were computed as a valley-to-peak value in the CVS and RVS, respectively. The method was applied to the existing dataset from five mechanically-ventilated pigs undergoing ten mini-fluid challenges. An invasive hemodynamic monitor was used in the arterial pressure-based cardiac output (APCO) mode to simultaneously measure SV values while a mechanical ventilator provided TV values.
Results: The leadforming method could reliably extract the CVS and RVS from the 208-channel trans-impedance data measured with the EIT device, from which SV and TV were computed. The SV and TV values were comparable to those from the invasive hemodynamic monitor and mechanical ventilator. Using the data from 5 pigs and a simple calibration method to remove bias, the error in SV and TV was 9.5% and 5.4%, respectively.
Conclusion: We developed a new leadforming method for the EIT device to robustly extract both SV and TV values in a deterministic fashion. Future animal and clinical studies are needed to validate this leadforming method in various subject populations.
Significance: The leadforming method could be an integral component for a new cardiopulmonary monitor in the future to simultaneously measure SV and TV noninvasively, which would be beneficial to patients.
A Mini-Fluid Challenge to Predict Fluid Responsiveness in Swine.
Yoshida K J Am Assoc Lab Anim Sci. 2025; 64(1):106-110.
PMID: 40035283 PMC: 11808368. DOI: 10.30802/AALAS-JAALAS-24-000026.