ECG-based estimation of respiratory modulation of AV nodal conduction during atrial fibrillation

Information about autonomic nervous system (ANS) activity may be valuable for personalized atrial fibrillation (AF) treatment but is not easily accessible from the ECG. In this study, we propose a new approach for ECG-based assessment of respiratory modulation in AV nodal refractory period and conduction delay. A 1-dimensional convolutional neural network (1D-CNN) was trained to estimate respiratory modulation of AV nodal conduction properties from 1-minute segments of RR series, respiration signals, and atrial fibrillatory rates (AFR) using synthetic data that replicates clinical ECG-derived data. The synthetic data were generated using a network model of the AV node and 4 million unique model parameter sets. The 1D-CNN was then used to analyze respiratory modulation in clinical deep breathing test data of 28 patients in AF, where a ECG-derived respiration signal was extracted using a novel approach based on periodic component analysis. We demonstrated using synthetic data that the 1D-CNN can predict the respiratory modulation from RR series alone ($\rho$ = 0.805) and that the addition of either respiration signal ($\rho$ = 0.830), AFR ($\rho$ = 0.837), or both ($\rho$ = 0.855) improves the prediction. Results from analysis of clinical ECG data of 20 patients with sufficient signal quality suggest that respiratory modulation decreased in response to deep breathing for five patients, increased for five patients, and remained similar for ten patients, indicating a large inter-patient variability.Comment: 20 pages, 7 figures, 5 table

An atrioventricular node model incorporating autonomic tone

The response to atrial fibrillation (AF) treatment is differing widely among patients, and a better understanding of the factors that contribute to these differences is needed. One important factor may be differences in the autonomic nervous system (ANS) activity. The atrioventricular (AV) node plays an important role during AF in modulating heart rate. To study the effect of the ANS-induced activity on the AV nodal function in AF, mathematical modelling is a valuable tool. In this study, we present an extended AV node model that incorporates changes in autonomic tone. The extension was guided by a distribution-based sensitivity analysis and incorporates the ANS-induced changes in the refractoriness and conduction delay. Simulated RR series from the extended model driven by atrial impulse series obtained from clinical tilt test data were qualitatively evaluated against clinical RR series in terms of heart rate, RR series variability and RR series irregularity. The changes to the RR series characteristics during head-down tilt were replicated by a 10% decrease in conduction delay, while the changes during head-up tilt were replicated by a 5% decrease in the refractory period and a 10% decrease in the conduction delay. We demonstrate that the model extension is needed to replicate ANS-induced changes during tilt, indicating that the changes in RR series characteristics could not be explained by changes in atrial activity alone

Identification of metabotypes in complex biological data using tensor decomposition

Differences in the physiological response to treatment, such as dietary intervention, has led to the development of precision approaches in nutrition and medicine to tailor treatment for improved benefits to the individual. One such approach is to identify metabotypes, i.e., groups of individuals with similar metabolic profiles and/or regulation. Metabotyping has previously been performed using e.g., principal component analysis (PCA) on matrix data. However, metabotyping methods suitable for more complex experimental designs such as repeated measures or cross-over studies are needed. We have developed a metabotyping method for tensor data, based on CANDECOMP/PARAFAC (CP) tensor decomposition. Metabotypes are inferred from CP scores using k-means clustering, and robustness is evaluated using bootstrapping of metabolites. As a proof-of-concept, we identified metabotypes from metabolomics data where 79 metabolites were analyzed in 8 time points postprandially in 17 overweight men that underwent a three-arm dietary crossover intervention. Two metabotypes were found, characterized by differences in amino acid metabolite concentration, that were differentially associated with baseline plasma creatinine (p = 0.007) and with the baseline metabolome (p = 0.004). These results suggest that CP decomposition provides a viable approach for metabotype identification directly from complex, high-dimensional data with improved biological interpretation compared to the more simplistic PCA approach. A simulation study together with results from measured data concluded that several preprocessing methods should be taken into consideration for CP-based metabotyping on complex tensor data

Human-based approaches to pharmacology and cardiology: an interdisciplinary and intersectorial workshop.

Both biomedical research and clinical practice rely on complex datasets for the physiological and genetic characterization of human hearts in health and disease. Given the complexity and variety of approaches and recordings, there is now growing recognition of the need to embed computational methods in cardiovascular medicine and science for analysis, integration and prediction. This paper describes a Workshop on Computational Cardiovascular Science that created an international, interdisciplinary and inter-sectorial forum to define the next steps for a human-based approach to disease supported by computational methodologies. The main ideas highlighted were (i) a shift towards human-based methodologies, spurred by advances in new in silico, in vivo, in vitro, and ex vivo techniques and the increasing acknowledgement of the limitations of animal models. (ii) Computational approaches complement, expand, bridge, and integrate in vitro, in vivo, and ex vivo experimental and clinical data and methods, and as such they are an integral part of human-based methodologies in pharmacology and medicine. (iii) The effective implementation of multi- and interdisciplinary approaches, teams, and training combining and integrating computational methods with experimental and clinical approaches across academia, industry, and healthcare settings is a priority. (iv) The human-based cross-disciplinary approach requires experts in specific methodologies and domains, who also have the capacity to communicate and collaborate across disciplines and cross-sector environments. (v) This new translational domain for human-based cardiology and pharmacology requires new partnerships supported financially and institutionally across sectors. Institutional, organizational, and social barriers must be identified, understood and overcome in each specific setting

Computational methods for the estimation of cardiac electrophysiological conduction parameters in a patient specific setting

Cardiovascular disease is the primary cause of death globally. Although this group encompasses a heterogeneous range of conditions, many of these diseases are associated with abnormalities in the cardiac electrical propagation. In these conditions, structural abnormalities in the form of scars and fibrotic tissue are known to play an important role, leading to a high individual variability in the exact disease mechanisms. Because of this, clinical interventions such as ablation therapy and CRT that work by modifying the electrical propagation should ideally be optimized on a patient specific basis. As a tool for optimizing these interventions, computational modelling and simulation of the heart have become increasingly important. However, in order to construct these models, a crucial step is the estimation of tissue conduction properties, which have a profound impact on the cardiac activation sequence predicted by simulations. Information about the conduction properties of the cardiac tissue can be gained from electrophysiological data, obtained using electroanatomical mapping systems. However, as in other clinical modalities, electrophysiological data are often sparse and noisy, and this results in high levels of uncertainty in the estimated quantities. In this dissertation, we develop a methodology based on Bayesian inference, together with a computationally efficient model of electrical propagation to achieve two main aims: 1) to quantify values and associated uncertainty for different tissue conduction properties inferred from electroanatomical data, and 2) to design strategies to optimise the location and number of measurements required to maximise information and reduce uncertainty. The methodology is validated in several studies performed using simulated data obtained from image-based ventricular models, including realistic fibre orientation and conduction heterogeneities. Subsequently, by using the developed methodology to investigate how the uncertainty decreases in response to added measurements, we derive an a priori index for placing electrophysiological measurements in order to optimise the information content of the collected data. Results show that the derived index has a clear benefit in minimising the uncertainty of inferred conduction properties compared to a random distribution of measurements, suggesting that the methodology presented in this dissertation provides an important step towards improving the quality of the spatiotemporal information obtained using electroanatomical mapping.</p

Computational methods for the estimation of cardiac electrophysiological conduction parameters in a patient specific setting

Cardiovascular disease is the primary cause of death globally. Although this group encompasses a heterogeneous range of conditions, many of these diseases are associated with abnormalities in the cardiac electrical propagation. In these conditions, structural abnormalities in the form of scars and fibrotic tissue are known to play an important role, leading to a high individual variability in the exact disease mechanisms. Because of this, clinical interventions such as ablation therapy and CRT that work by modifying the electrical propagation should ideally be optimized on a patient specific basis. As a tool for optimizing these interventions, computational modelling and simulation of the heart have become increasingly important. However, in order to construct these models, a crucial step is the estimation of tissue conduction properties, which have a profound impact on the cardiac activation sequence predicted by simulations. Information about the conduction properties of the cardiac tissue can be gained from electrophysiological data, obtained using electroanatomical mapping systems. However, as in other clinical modalities, electrophysiological data are often sparse and noisy, and this results in high levels of uncertainty in the estimated quantities. In this dissertation, we develop a methodology based on Bayesian inference, together with a computationally efficient model of electrical propagation to achieve two main aims: 1) to quantify values and associated uncertainty for different tissue conduction properties inferred from electroanatomical data, and 2) to design strategies to optimise the location and number of measurements required to maximise information and reduce uncertainty. The methodology is validated in several studies performed using simulated data obtained from image-based ventricular models, including realistic fibre orientation and conduction heterogeneities. Subsequently, by using the developed methodology to investigate how the uncertainty decreases in response to added measurements, we derive an a priori index for placing electrophysiological measurements in order to optimise the information content of the collected data. Results show that the derived index has a clear benefit in minimising the uncertainty of inferred conduction properties compared to a random distribution of measurements, suggesting that the methodology presented in this dissertation provides an important step towards improving the quality of the spatiotemporal information obtained using electroanatomical mapping

Characterisation of human AV-nodal properties using a network model

Characterisation of the AV-node is an important step in determining the optimal form of treatment for supraventricular tachycardias. To integrate and analyse patient-specific measurements, mathematical modelling has emerged as a valuable tool. Here we present a model of the human AV-node, consisting of a series of interacting nodes, each with separate dynamics in refractory time and conduction delay. The model is evaluated in several scenarios, including atrial fibrillation (AF) and clinical pacing, using simulated and measured data. The model is able to replicate signals derived from clinical ECG data as well as from invasive measurements, both under AF and pacing. To quantify the uncertainty in parameter estimation, 1000 parameter sets were sampled, showing that model output similar to data corresponds to limited regions in the model parameter space. The model is the first human AV-node model to capture both spatial and temporal dynamics while being efficient enough to allow interactive use on clinical timescales, as well as parameter estimation and uncertainty quantification. As such, it fills a new niche in the current set of published models and forms a valuable tool for both understanding and clinical research

A spatially extended model of the human atrioventricular node

The atrioventricular (AV) node plays a crucial role during many supraventricular tachycardias (SVT). To better understand its function under these complex conditions, mathematical modelling has emerged as a valuable tool. The model presented here builds on a recently published 1D model of the human AV-node, consisting of a series of interacting nodes, each with separate dynamics in refractory time and conduction delay. Here, we extend the formulation to 2D and demonstrate its ability to reproduce clinical data. Subsequently, we use it to study how AV-nodal properties for clinically assessed single and dual AV-node physiology affect activation for regular and stochastic input. In particular we study the effect of functional gradients within the AV node on ventricular response during atrial pacing and atrial fibrillation. Simulation results display important emergent features such as pathway switching and concealed conduction, and show differences in AF response that are not present in response to pacing. Simulation of a single impulse takes around 30 ms, admitting interactive use on clinical time scales as well as parameter estimation and uncertainty quantification. To our knowledge, the presented model is the first spatially extended human AV-node model, and as such represents a novel tool for understanding the human AV-nodal function in both healthy and diseased individuals, thereby paving the way for improved SVT diagnosis and therapy

Assessment of cardiotoxic effects from ion channel assay data

Drug-induced cardiotoxicity or torsades de pointes (TdP), a potentially lethal cardiac ventricular arrhythmia, is an adverse effect that has long been a leading cause of attrition during drug development.Minimizing the risk of this cardiotoxic effect is thus an important task during the drug development process and regulatory guidelines require new drugs to be evaluated for pro-arrhytmic risk before entering clinical testing. At present, block of the cardiac potassium channel hERG and human QT intervals are assessed as part of the current safety guidelines. Although a block of the cardiac potassium channel hERG and subsequent prolongation of the cardiac QT interval are common features of cardiotoxic drugs, there is no simple one-to-one correlation. TdP involves changes in cardiac cell repolarisation, which is dependent on the concerted activity of several ion channels including hERG, Na-, and Ca-channels. Too much emphasis on hERG as a marker has most likely hampered the development of new drugs by premature discontinuation from development.We aim to directly assess the primary clinical endpoint, namely ventricular proarrhythmia (i.e., cardiotoxicity). To achieve this, we use a data driven approach based on published data to train a neural network architecture.The technology is made easily accessible to potential users via a web based demonstrator

A Comparative Study of Graph-Based, Eikonal, and Monodomain Simulations for the Estimation of Cardiac Activation Times

The bidomain and monodomain equations are well established as the standard set of equations for the simulation of cardiac electrophysiological behavior. However, the computational cost of detailed bidomain/monodomain simulations limits their applicability in scenarios where a large number of simulations needs to be performed (e.g., parameter estimation). In this study, we present a graph-based method, which relies on point-to-point path finding to estimate activation times for single points in cardiac tissue with minimal computational costs. To validate our approach, activation times are compared to monodomain simulation results for an anatomically based rabbit ventricular model, incorporating realistic fiber orientation and conduction heterogeneities. Differences in activation times between the graph-based method and monodomain results are less than 10$\%$ of the total activation time, and computational performance is orders of magnitude faster with the proposed method when calculating activation times at single points. These results suggest that the graph-based method is well suited for estimating activation times when the need for fast performance justifies a limited loss of accuracy