Fast 4D FEM Model for EIT Source Separation Benchmarking

Abstract The accurate separation of cardiac and ventilatory contributions to electrical impedance tomography signals is crucial for complete and non-invasive cardiorespiratory monitoring. However, no consensus on a suitable source separation algorithm was achieved despite several proposals due to lacking systematic evaluation. To address this, we propose a benchmarking 4D finite element method generative model for mixed, cardiac, and ventilatory signals. Our model implements dynamic modelling of the heart, lungs, and pulmonary arteries using realistic volume and flow curve templates, along with cardiac and respiratory frequency coupling.We also employed variable alveolar and blood conductivities. The model was able to obtain long recordings faster than comparably complex models while maintaining significant physiological effects and signal properties such as non-stationarity, spatial delays, time and frequency profiles. The realistic physiological model can be used to taxonomize and evaluate source separation algorithms, as well as aid in the development and training of new ones.