Abstract:

Chronic kidney disease (CKD) affects 13% of the worldwide population and end stage patients often receive haemodialysis treatment to control the electrolyte concentrations. The cardiovascular death rate increases by 10% - 30% in dialysis patients than in general population. To analyse possible links between electrolyte concentration variation and cardiovascular diseases, a continuous non-invasive monitoring tool enabling the estimation of potassium and calcium concentration from features of the ECG is desired. Although the ECG was shown capable of being used for this purpose, the method still needs improvement. In this study, we examine the influence of lead reduction techniques on the estimation results of serum calcium and potassium concentrations.We used simulated 12 lead ECG signals obtained using an adapted Himeno et al. model. Aiming at a precise estimation of the electrolyte concentrations, we compared the estimation based on standard ECG leads with the estimation using linearly transformed fusion signals. The transformed signals were extracted from two lead reduction techniques: principle component analysis (PCA) and maximum amplitude transformation (Max- Amp). Five features describing the electrolyte changes were calculated from the signals. To reconstruct the ionic concentrations, we applied a first and a third order polynomial regression connecting the calculated features and concentration values. Furthermore, we added 30 dB white Gaussian noise to the ECGs to imitate clinically measured signals. For the noisefree case, the smallest estimation error was achieved with a specific single lead from the standard 12 lead ECG. For example, for a first order polynomial regression, the error was 0.0003±0.0767 mmol/l (mean±standard deviation) for potassium and -0.0036±0.1710 mmol/l for calcium (Wilson lead V1). For the noisy case, the PCA signal showed the best estimation performance with an error of -0.003±0.2005 mmol/l for potassium and -0.0002±0.2040 mmol/l for calcium (both first order fit). Our results show that PCA as ECG lead reduction technique is more robust against noise than MaxAmp and standard ECG leads for ionic concentration reconstruction.

Abstract:

Patients suffering from end stage of chronic kid- ney disease (CKD) often undergo haemodialysis to normalize the electrolyte concentrations. Moreover, cardiovascular disease (CVD) is the main cause of death in CKD patients. To study the connection between CKD and CVD, we investi- gated the effects of an electrolyte variation on cardiac signals (action potential and ECG) using a computational model. In a first step, simulations with the Himeno et al. ventricular cell model were performed on cellular level with different extra- cellular sodium ([Na+]o), calcium ([Ca2+]o) and potassium ([K+]o) concentrations as occurs in CKD patients. [Ca2+]o and [K+]o changes caused variations in different features describ- ing the morphology of the AP. Changes due to a [Na+]o varia- tion were not as prominent. Simulations with [Ca2+]o varia- tions were also carried out on ventricular ECG level and a 12-lead ECG was computed. Thus, a multiscale simulator from ion channel to ECG reproducing the calcium-dependent inactivation of ICaL was achieved. The results on cellular and ventricular level agree with results from literature. Moreover, we suggest novel features representing electrolyte changes that have not been described in literature. These results could be helpful for further studies aiming at the estimation of ionic concentrations based on ECG recordings.

Abstract:

Electrical impedance tomography is clinically used to trace ventilation related changes in electrical conductivity of lung tissue. Estimating regional pulmonary perfusion using electrical impedance tomography is still a matter of research. To support clinical decision making, reliable bedside information of pulmonary perfusion is needed. We introduce a method to robustly detect pulmonary perfusion based on indicator-enhanced electrical impedance tomography and validate it by dynamic multidetector computed tomography in two experimental models of acute respiratory distress syndrome. The acute injury was induced in a sublobar segment of the right lung by saline lavage or endotoxin instillation in eight anesthetized mechanically ventilated pigs. For electrical impedance tomography measurements, a conductive bolus (10% saline solution) was injected into the right ventricle during breath hold. Electrical impedance tomography perfusion images were reconstructed by linear and normalized Gauss-Newton reconstruction on a finite element mesh with subsequent element-wise signal and feature analysis. An iodinated contrast agent was used to compute pulmonary blood flow via dynamic multidetector computed tomography. Spatial perfusion was estimated based on first-pass indicator dilution for both electrical impedance and multidetector computed tomography and compared by Pearson correlation and Bland-Altman analysis. Strong correlation was found in dorsoventral (r = 0.92) and in right-to-left directions (r = 0.85) with good limits of agreement of 8.74% in eight lung segments. With a robust electrical impedance tomography perfusion estimation method, we found strong agreement between multidetector computed and electrical impedance tomography perfusion in healthy and regionally injured lungs and demonstrated feasibility of electrical impedance tomography perfusion imaging.

Abstract:

Multi-scale computational modeling of cardiac electrophysiology has fostered our understanding of the genesis of the ECG. While current models capture the relevant processes under physiological and many disease conditions with high fidelity, proper representation of the conditions in the extracellular milieu remains challenging. The recent human ventricular myocyte model by Himeno et al. is one of the first biophysical models which faithfully represents the dependence of the action potential (AP) duration on the extracellular calcium concentration ([Ca2+]o). Here, we present a heterogeneous formulation of the Himeno et al. cellular model and integrate it into a multi-scale framework to compute body surface ECGs. We propose three variants of the Himeno et al. model to account for transmural heterogeneity. The ionic current level parameter sets representing subendocardial, M, and subepicardial cell types were informed by the experimental data presented with the O’Hara-Rudy model and tuned to match AP level features such as repolarization stability. As shown in a previous work by Keller et al., an apico-basal gradient of IKs conductance is a likely mechanism causing concordant T-waves. Therefore, we increased the IKs conductance in the Himeno et al. model at the apex by a factor of 3.5 compared to the base to obtain an APD shortening of 12.5%. The model setup comprising transmural and apico-basal heterogeneity yielded a physiological ventricular ECG comparable to previous setups building on the ten Tusscher et al. cellular model. Our novel setup allows to study, for the first time, how realistic changes of the AP under hypo- and hypercalcaemic conditions translate to changes in the ECG. Resulting QT prolongation under hypocalcaemic conditions quantitatively matched human experimental data. In conclusion, the setup presented here provides a tool to study the effect of altered calcium levels in the extracellular milieu of the heart, as e. g. occurring during renal failure, across multiple spatial scales mechanistically.

Abstract:

In Europe, the prevalence of chronic kidney disease lay at approximately 18.38% in 2016. A common treatment for patients in the end stage of this disease is haemodialysis. However, patients undergoing this therapy suffer from an increased risk of cardiac death. A hypothesis is that the cause is an inbalanced electrolyte concentration. To study the underlying mechanisms of this phenomenon and fight the consequences, a continous non-invasive monitoring technique is desired. In this work, we investigated the possibility to reconstruct the extracellular concentrations of potassium and calcium from ECG signals. Therefore, we extracted 71 ECGs using the simulation results of a modified Himeno et al. ventricular cell model comprising variations of the extracellular ionic concentrations of potassium and calcium. The changes dependent on the different extracellular ionic concentrations were captured with five ECG features. These were used to train an artificial neural network for regression. The study was performed both for noise-free and noisy data. The estimation error for the reconstruction of the potassium concentrations was -0.01±0.14 mmol/l (mean±standard deviation) in the noise- free case, -0.03±0.46mmol/l in the noisy case (30dB SNR). For calcium, the result was 0.01±0.11mmol/l in the noise- free case, 0.02±0.17mmol/l in the noisy case. For both ion types, the result was improved by augmenting the dataset. We therefore conclude that with the calculated features, we are able to reconstruct the extracellular ionic concentrations for both potassium and calcium with an acceptable precision. When analysing noisy signals, the accuracy of the estimation method is still sufficient but can be further improved by an augmentation of the dataset.

Abstract:

Patients experiencing respiratory disorders often need to be mechanically ventilated to ensure sufficient gas exchange. An adequate lung monitoring, including regional lung ventilation and perfusion is believed to support a proper diagnosis and reduce ventilator associated lung injuries. Electrical Impedance Tomography (EIT) is a well established device used in clinical treatments for visualizing regional ventilation distributions. As a non-invasive, portable, and real time system, the possibility of monitoring regional perfusion distribution with EIT has gained interest over the past years. First studies have shown very promising results when combining the EIT technique with the injection of a saline solution indicator. For its clinical implementation, further research is necessary to validate the obtained distribution, prove the robustness of the applied methods, and gain spatial resolution. This work deals with adapting EIT reconstruction algorithms for the estimation of spatial pulmonary perfusion. The current algorithm shifts the perfusion distribution towards central and ventral parts of the torso, leading to a poor spatial resolution. To avoid reconstruction biases for pulmonary perfusion estimation, different approaches for the reconstruction of the conductivity distributions are studied on two different datasets: a simulation study and a preclinical study, carried out on pigs. First, three alternative methods for calculating the Jacobian matrix are studied. The Jacobian matrix is calculated assuming an inhomogeneous conductivity distribution, which better fits with the anatomical torso boundaries. Additionally, a configuration updating the conductivity distribution for each time step and one normalizing the sensitivity of the Jacobian matrix were considered. Secondly, four different regularization terms are compared: the classical regularization approaches of Tikhonov 0th and Tikhonov 2nd order, a regularization including temporal information, and a novel approach combining both, Tikhonov 0th and Tikhonov 2nd order approaches, based on the EIT sensitivity. Furthermore, an approach focused on combining images obtained from different smoothing algorithms based on the sensitivity of the Jacobian matrix has been proposed. The results show that assuming an inhomogeneous conductivity distribution generally leads to an improvement in the spatial perfusion distribution. Regarding the regularization term, it was proven that imposing less spatial smoothing at central parts of the torso should be considered for obtaining a better resolution of the images. The positive results suggest the necessity of considering a different EIT reconstruction algorithm for monitoring pulmonary perfusion. Summarizing, the proposed approaches in this work, including a priori information, lead to a better estimation of the spatial perfusion distribution and contribute to conclude the great potential of EIT for monitoring lung respiration.

Abstract:

Approximately 13% of the worldwide population suffers from chronic kidney disease (CKD). Patients suffering from CKD often undergo dialysis therapies to compensate the changes of the ionic concentrations. Moreover, cardiovascular disease (CVD) is the main cause of death in CKD patients. In this study, the connection between the variation of the electrolyte concentrations and the electrophysiology of the heart will be studied, in order to justify the relation between both diseases. The results are based on a computational model, as it is a helpful, cheap and an ethically accepted tool for studying cardiac electrophysiology. The ventricular cell model presented by Himeno et al. was used for analyzing a variation of extracellular sodium, potassium and calcium within different ranges as done in a previous work. Simulations were carried out on cellular level and on tissue level for a ventricular simulation. On cellular level calcium and potassium changes showed variations in the form of the action potentials, and of parameters like the action potential duration or the resting membrane voltage. Changes in the action potential due to a variation of extracellular sodium were not as prominent as for calcium and potassium. In order to simulate the depolarization dispersion of the cells within the ventricles, an apicobasal and a transmural formulation of the Himeno et al. model were proposed. Results from simulations with these formulations accorded with results obtained from in vivo experiments. As a next step, ventricular simulations were carried out and forward calculated for the extraction of the electrocardiogram (ECG). First, the Himeno et al. model was evaluated for tissue simulations, by analyzing the wave propagation. As the model was considered capable for tissue modelling in the tissue study, ventricular simulations with extracellular ionic variation were performed. To evaluate the realism of the resulting ECGs, features were calculated and compared as done in a previous work. A dependence of the evaluated features on the extracellular ionic concentration was found. The results were in accordance with the literature for those features that have already been investigated. In this study the relation between electrolyte concentrations and the electrophysiology of the heart was proved by analyzing the morphology of the action potential and the ECG. These results could be helpful for further studies investigating the reconstruction of the ionic concentrations based on signal processing of the ECG.