EIT of the Human Body with Optimal Current Patterns and Skin-Electrode Impedance Compensation

Following the lead of the EIT research group at Rensselaer Polytechnic Institute, we have designed and implemented a system comprising 32 independent current sources, in which it is possible to apply current patterns optimizing distinguishability. One potential technical problem is that we are measuring voltages on current-carrying electrodes, giving some sensitivity to time varying skinelectrode impedances. We demonstrate here an algorithm to estimate simultaneously changes in the medium and timevarying skin-electrode impedances

Multi-channel EIT for layer-based hydration monitoring

Accurate monitoring of hydration level in patients remains a major challenge for hemodialysis therapy. Using a prototype EIT system with simultaneous multi-channel current excitation, we demonstrated the capability to detect a difference of 35ml daily fluid change in human subjects who wear compression sock only on one leg. The prototype system has the potential to be used in clinical settings with hydration monitoring needs

A High Precision Parallel Current Drive Experimental EIT System

Our parallel current drive EIT architecture can simultaneously drive 32 independent high impedance current sources and measure 32 independent precision voltage channels. Coherent modulation and demodulation is digitally implemented using field programmable gate arrays. High accuracy and precision is achieved using custom analog circuits containing modified Howland current sources coupled to negative impedance converters

diffuse

(Submitted for the TBO feature issue

Utilizing machine learning to improve clinical trial design for acute respiratory distress syndrome

Heterogeneous patient populations, complex pharmacology and low recruitment rates in the Intensive Care Unit (ICU) have led to the failure of many clinical trials. Recently, machine learning (ML) emerged as a new technology to process and identify big data relationships, enabling a new era in clinical trial design. In this study, we designed a ML model for predictively stratifying acute respiratory distress syndrome (ARDS) patients, ultimately reducing the required number of patients by increasing statistical power through cohort homogeneity. From the Philips eICU Research Institute (eRI) database, no less than 51,555 ARDS patients were extracted. We defined three subpopulations by outcome: (1) rapid death, (2) spontaneous recovery, and (3) long-stay patients. A retrospective univariate analysis identified highly predictive variables for each outcome. All 220 variables were used to determine the most accurate and generalizable model to predict long-stay patients. Multiclass gradient boosting was identified as the best-performing ML model. Whereas alterations in pH, bicarbonate or lactate proved to be strong predictors for rapid death in the univariate analysis, only the multivariate ML model was able to reliably differentiate the disease course of the long-stay outcome population (AUC of 0.77). We demonstrate the feasibility of prospective patient stratification using ML algorithms in the by far largest ARDS cohort reported to date. Our algorithm can identify patients with sufficiently long ARDS episodes to allow time for patients to respond to therapy, increasing statistical power. Further, early enrollment alerts may increase recruitment rate

Direct numerical reconstruction of conductivities in three dimensions using scattering transforms

A direct three-dimensional EIT reconstruction algorithm based on complex geometrical optics solutions and a nonlinear scattering transform is presented and implemented for spherically symmetric conductivity distributions. The scattering transform is computed both with a Born approximation and from the forward problem for purposes of comparison. Reconstructions are computed for several test problems. A connection to Calderon's linear reconstruction algorithm is established, and reconstructions using both methods are compared