Cardiac eigen imaging: a novel method to isolate cardiac activity in thoracic electrical impedance tomography.

OBJECTIVE

As the global burden of cardiovascular disease increases, proactive cardiovascular healthcare by means of accurate, precise, continuous, and non-invasive monitoring is becoming crucial. However, no current device is able to provide cardiac hemodynamic monitoring with the aforementioned criterion. Electrical impedance tomography (EIT) is an inexpensive, non-invasive imaging modality that can provide real-time images of internal conductivity distributions that describe physiological activity. This work explores and compares a standard approach of regular cardiac gated averaging (RCGA) and a newly developed method, cardiac eigen-imaging (CEI), based on the singular value decomposition (SVD) to isolate cardiac activity in thoracic EIT.

APPROACH: EIT and heart-rate (HR) data were collected from 20 heart-failure patients preceding echocardiography. Features from RCGA and CEI images were correlated with stroke volume (SV) from echocardiography and image reconstruction parameters were optimized using leave-one-out (LOO) cross-validation.

MAIN

RESULTS

CEI per-pixel-based features achieved a Pearson correlation coefficient of 0.80 with SV relative to 0.72 with RCGA. CEI had 33 high-correlating pixels while RCGA had 8. High-correlating pixels tend to concentrate in the right-ventricle (RV) when referenced to a general chest model.

SIGNIFICANCE: While both RCGA and CEI images had high-correlating pixels, CEI had higher correlations, a larger number of high-correlating pixels, and unlike RCGA is not dependent on the quality of the HR data collected. The observed performance of the CEI approach represents a promising step forward for EIT-based cardiac monitoring in either clinical or ambulatory settings.