Comparison of noninvasive cardiac output and stroke volume measurements using electrical impedance tomography with invasive methods in a swine model.

Pulmonary artery catheterization (PAC) has been used as a clinical standard for cardiac output (CO) measurements on humans. On animals, however, an ultrasonic flow sensor (UFS) placed around the ascending aorta or pulmonary artery can measure CO and stroke volume (SV) more accurately. The objective of this paper is to compare CO and SV measurements using a noninvasive electrical impedance tomography (EIT) device and three invasive devices using UFS, PAC-CCO (continuous CO) and arterial pressure-based CO (APCO). Thirty-two pigs were anesthetized and mechanically ventilated. A UFS was placed around the pulmonary artery through thoracotomy in 11 of them, while the EIT, PAC-CCO and APCO devices were used on all of them. Afterload and contractility were changed pharmacologically, while preload was changed through bleeding and injection of fluid or blood. Twenty-three pigs completed the experiment. Among 23, the UFS was used on 7 pigs around the pulmonary artery. The percentage error (PE) between COUFS and COEIT was 26.1%, and the 10-min concordance was 92.5%. Between SVUFS and SVEIT, the PE was 24.8%, and the 10-min concordance was 94.2%. On analyzing the data from all 23 pigs, the PE between time-delay-adjusted COPAC-CCO and COEIT was 34.6%, and the 10-min concordance was 81.1%. Our results suggest that the performance of the EIT device in measuring dynamic changes of CO and SV on mechanically-ventilated pigs under different cardiac preload, afterload and contractility conditions is at least comparable to that of the PAC-CCO device. Clinical studies are needed to evaluate the utility of the EIT device as a noninvasive hemodynamic monitoring tool.

Unsupervised sequence-to-sequence learning for automatic signal quality assessment in multi-channel electrical impedance-based hemodynamic monitoring.

BACKGROUND AND OBJECTIVE: This study proposes an unsupervised sequence-to-sequence learning approach that automatically assesses the motion-induced reliability degradation of the cardiac volume signal (CVS) in multi-channel electrical impedance-based hemodynamic monitoring. The proposed method attempts to tackle shortcomings in existing learning-based assessment approaches, such as the requirement of manual annotation for motion influence and the lack of explicit mechanisms for realizing motion-induced abnormalities under contextual variations in CVS over time. METHOD: By utilizing long-short term memory and variational auto-encoder structures, an encoder-decoder model is trained not only to self-reproduce an input sequence of the CVS but also to extrapolate the future in a parallel fashion. By doing so, the model can capture contextual knowledge lying in a temporal CVS sequence while being regularized to explore a general relationship over the entire time-series. A motion-influenced CVS of low-quality is detected, based on the residual between the input sequence and its neural representation with a cut-off value determined from the two-sigma rule of thumb over the training set. RESULT: Our experimental observations validated two claims: (i) in the learning environment of label-absence, assessment performance is achievable at a competitive level to the supervised setting, and (ii) the contextual information across a time series of CVS is advantageous for effectively realizing motion-induced unrealistic distortions in signal amplitude and morphology. We also investigated the capability as a pseudo-labeling tool to minimize human-craft annotation by preemptively providing strong candidates for motion-induced anomalies. Empirical evidence has shown that machine-guided annotation can reduce inevitable human-errors during manual assessment while minimizing cumbersome and time-consuming processes. CONCLUSION: The proposed method has a particular significance in the industrial field, where it is unavoidable to gather and utilize a large amount of CVS data to achieve high accuracy and robustness in real-world applications.

Motion Artifacts Reduction for Noninvasive Hemodynamic Monitoring of Conscious Patients Using Electrical Impedance Tomography: A Preliminary Study.

Electrical impedance tomography (EIT) can monitor the real-time hemodynamic state of a conscious and spontaneously breathing patient noninvasively. However, cardiac volume signal (CVS) extracted from EIT images has a small amplitude and is sensitive to motion artifacts (MAs). This study aimed to develop a new algorithm to reduce MAs from the CVS for more accurate heart rate (HR) and cardiac output (CO) monitoring in patients undergoing hemodialysis based on the source consistency between the electrocardiogram (ECG) and the CVS of heartbeats. Two signals were measured at different locations on the body through independent instruments and electrodes, but the frequency and phase were matched when no MAs occurred. A total of 36 measurements with 113 one-hour sub-datasets were collected from 14 patients. As the number of motions per hour (MI) increased over 30, the proposed algorithm had a correlation of 0.83 and a precision of 1.65 beats per minute (BPM) compared to the conventional statical algorithm of a correlation of 0.56 and a precision of 4.04 BPM. For CO monitoring, the precision and upper limit of the mean ∆CO were 3.41 and 2.82 L per minute (LPM), respectively, compared to 4.05 and 3.82 LPM for the statistical algorithm. The developed algorithm could reduce MAs and improve HR/CO monitoring accuracy and reliability by at least two times, particularly in high-motion environments.

Multi-Channel Trans-Impedance Leadforming for Cardiopulmonary Monitoring: Algorithm Development and Feasibility Assessment Using In Vivo Animal Data.

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. SVEIT and TVEIT 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 SVAPCO values while a mechanical ventilator provided TVVent 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 SVEIT and TVEIT were computed. The SVEIT and TVEIT 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 SVEIT and TVEIT 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.

Electrical Impedance Tomography-Based Abdominal Subcutaneous Fat Estimation Method Using Deep Learning.

This paper proposes a deep learning method based on electrical impedance tomography (EIT) to estimate the thickness of abdominal subcutaneous fat. EIT for evaluating the thickness of abdominal subcutaneous fat is an absolute imaging problem that aims at reconstructing conductivity distributions from current-to-voltage data. Existing reconstruction methods based on EIT have difficulty handling the inherent drawbacks of strong nonlinearity and severe ill-posedness of EIT; hence, absolute imaging may not be possible using linearized methods. To handle nonlinearity and ill-posedness, we propose a deep learning method that finds useful solutions within a restricted admissible set by accounting for prior information regarding abdominal anatomy. We determined that a specially designed training dataset used during the deep learning process significantly reduces ill-posedness in the absolute EIT problem. In the preprocessing stage, we normalize current-voltage data to alleviate the effects of electrodeposition and body geometry by exploiting knowledge regarding electrode positions and body geometry. The performance of the proposed method is demonstrated through numerical simulations and phantom experiments using a 10 channel EIT system and a human-like domain.

A Fidelity-Embedded Regularization Method for Robust Electrical Impedance Tomography.

Electrical impedance tomography (EIT) provides functional images of an electrical conductivity distribution inside the human body. Since the 1980s, many potential clinical applications have arisen using inexpensive portable EIT devices. EIT acquires multiple trans-impedance measurements across the body from an array of surface electrodes around a chosen imaging slice. The conductivity image reconstruction from the measured data is a fundamentally ill-posed inverse problem notoriously vulnerable to measurement noise and artifacts. Most available methods invert the ill-conditioned sensitivity or the Jacobian matrix using a regularized least-squares data-fitting technique. Their performances rely on the regularization parameter, which controls the trade-off between fidelity and robustness. For clinical applications of EIT, it would be desirable to develop a method achieving consistent performance over various uncertain data, regardless of the choice of the regularization parameter. Based on the analysis of the structure of the Jacobian matrix, we propose a fidelity-embedded regularization (FER) method and a motion artifact reduction filter. Incorporating the Jacobian matrix in the regularization process, the new FER method with the motion artifact reduction filter offers stable reconstructions of high-fidelity images from noisy data by taking a very large regularization parameter value. The proposed method showed practical merits in experimental studies of chest EIT imaging.

Electrical impedance spectroscopy for electro-mechanical characterization of conductive fabrics.

When we use a conductive fabric as a pressure sensor, it is necessary to quantitatively understand its electromechanical property related with the applied pressure. We investigated electromechanical properties of three different conductive fabrics using the electrical impedance spectroscopy (EIS). We found that their electrical impedance spectra depend not only on the electrical properties of the conductive yarns, but also on their weaving structures. When we apply a mechanical tension or compression, there occur structural deformations in the conductive fabrics altering their apparent electrical impedance spectra. For a stretchable conductive fabric, the impedance magnitude increased or decreased under tension or compression, respectively. For an almost non-stretchable conductive fabric, both tension and compression resulted in decreased impedance values since the applied tension failed to elongate the fabric. To measure both tension and compression separately, it is desirable to use a stretchable conductive fabric. For any conductive fabric chosen as a pressure-sensing material, its resistivity under no loading conditions must be carefully chosen since it determines a measurable range of the impedance values subject to different amounts of loadings. We suggest the EIS method to characterize the electromechanical property of a conductive fabric in designing a thin and flexible fabric pressure sensor.