Noise-robust bioimpedance approach for cardiac output measurement

Objective : Congestive heart failure is a problem affecting 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 suppo...

Source: Physiological Measurement - July 28, 2019 Category: Physiology Authors: Ethan K Murphy, Justice Amoh, Saaid H Arshad, Ryan J Halter and Kofi Odame Source Type: research