Abstract:

The Purkinje system is part of the fast-conducting ventricular excitation system. The anatomy of the Purkinje system varies from person to person and imposes a unique excitation pattern on the ventricular myocardium, which defines the morphology of the QRS complex of the ECG to a large degree. While it cannot be imaged in-vivo, it plays an important role for personalizing computer simulations of cardiac electrophysiology. Here, we present a new method to automatically model and customize the Purkinje system based on the measured electrocardiogram (ECG) of a patient. A graphbased algorithm was developed to generate Purkinje systems based on the parameters fibre density, minimal distance from the atrium, conduction velocity, and position and timing of excitation sources mimicking the bundle branches. Based on the resulting stimulation profile, the activation times of the ventricles were calculated using the fast marching approach. Predescribed action potentials and a finite element lead field matrix were employed to obtain surface ECG signals. The root mean square error (RMSE) between the simulated and measured QRS complexes of the ECGs was used as cost function to perform optimization of the Purkinje parameters. One complete evaluation from Purkinje tree generation to the simulated ECG could be computed in about 10 seconds on a standard desktop computer. The measured ECG of the patient used to build the anatomical model was matched via parallel simplex optimization with a remaining RMSE of 4.05 mV in about 16 hours. The approach presented here allows to tailor the structure of the Purkinje system through the measured ECG in a patient-specific way. The computationally efficient implementation facilitates global optimization.

Abstract:

This study examines the effect of mental workload on the electrocardiogram (ECG) of participants driving the Lane Change Task (LCT). Different levels of mental workload were induced by a secondary task (n-back task) with three levels of difficulty. Subjective data showed a significant increase of the experienced workload over all three levels. An exploratory approach was chosen to extract a large number of rhythmical and morphological features from the ECG signal thereby identifying those which differentiated best between the levels of mental workload. No single rhythmical or morphological feature was able to differentiate between all three levels. A group of parameters were extracted which were at least able to discriminate between two levels. For future research, a combination of features is recommended to achieve best diagnosticity for different levels of mental workload.

Abstract:

Heart rate variability (HRV) plays an important role in medicine and psychology because it is used to quantify imbalances of the autonomic nervous system (ANS). An important manifestations of the ANS on HRV is also directly related to respiration and it is called respiratory sinus arrhythmia (RSA). This is a controlled phenomenon that leads to a synchronized coupling between respiration and instantaneous heart rate. Thus, the portion of HRV that is not related to respiration, and could potentially contain undiscovered diagnostic value, is overlapped and remains hidden in a standard HRV analysis. In such cases, a decoupling procedure would deliver a discriminated HRV analysis and possible new insights about the regulation of the cardiovascular system. In this work, we propose an algorithm based on Granger's causality to measure coupling between respiration and HRV. In the case of significant coupling, we estimate and cancel the respiration driven HRV component using a linear filtering approach. We tested the method using synthetic signals and prove it to deliver a reliable coupling measurement in 96.3% of the cases and reconstruct respiration free signals with a median correlation coefficient of 0.992. Afterwards, we applied our method to signals recorded during paced respiration and during natural breathing. We demonstrated that coupling is dependent on respiratory frequency and that it maximizes at 0.3 Hz. Furthermore, the HRV parameters measured during paced respiration tend to level among subjects after decoupling. The intersubject variability of HRV parameter is also decreased after the separation process. During natural breathing, coupling is notoriously lower to non-existing and decoupling has little impact on HRV. We conclude that the method proposed here can be used to investigate the diagnostic value of respiration independent HRV parameters.

Abstract:

The most important ECG marker for the diagnosis of ischemia or infarction is a change in the ST segment. Baseline wander is a typical artifact that corrupts the recorded ECG and can hinder the correct diagnosis of such diseases. For the purpose of finding the best suited filter for the removal of baseline wander, the ground truth about the ST change prior to the corrupting artifact and the subsequent filtering process is needed. In order to create the desired reference, we used a large simulation study that allowed us to represent the ischemic heart at a multiscale level from the cardiac myocyte to the surface ECG. We also created a realistic model of baseline wander to evaluate five filtering techniques commonly used in literature. In the simulation study, we included a total of 5.5 million signals coming from 765 electrophysiological setups. We found that the best performing method was the wavelet-based baseline cancellation. However, for medical applications, the Butterworth high-pass filter is the better choice because it is computationally cheap and almost as accurate. Even though all methods modify the ST segment up to some extent, they were all proved to be better than leaving baseline wander unfiltered.

Abstract:

Radiofrequency ablation has become a first-line approach for curative therapy of many cardiac arrhythmias. Various existing catheter designs provide high spatial resolution to identify the best spot for performing ablation and to assess lesion formation. However, creation of transmural and nonconducting ablation lesions requires usage of catheters with larger electrodes and improved thermal conductivity, leading to reduced spatial sensitivity. As trade-off, an ablation catheter with integrated mini electrodes was introduced. The additional diagnostic benefit of this catheter is still not clear. In order to solve this issue, we implemented a computational setup with different ablation scenarios. Our in silico results show that peak-to-peak amplitudes of unipolar electrograms from mini electrodes are more suitable to differentiate ablated and nonablated tissue compared to electrograms from the distal ablation electrode. However, in orthogonal mapping position, no significant difference was observed between distal electrode and mini electrodes electrograms in the ablation scenarios. In conclusion, catheters with mini electrodes bring about additional benefit to distinguish ablated tissue from nonablated tissue in parallel position with high spatial resolution. It is feasible to detect conduction gaps in linear lesions with this catheter by evaluating electrogram data from mini electrodes.

Abstract:

In den Lebenswissenschaften ist die Individualisierte Medizin aktuell ein zentrales Thema, vielleicht ein Hype. Das bestätigen auch synonyme, erweiternde und klärende Begriffe wie Personalisierte Medizin, Customized Medicine, Stratifizierende Medizin oder Präzisionsmedizin. In einer State of the Union Address an die Bevölkerung der Vereinigten Staaten von Amerika hat Präsident Barack Obama 2015 die Bedeutung der Präzisionsmedizin hervorgehoben [1]. Diese Initiative wurde inhaltlich wesentlich vom Direktor des National Institute of Health, dem Genetiker Francis Collins, vorangetrieben [2]. Individualisierte Medizin, das ist eine gute Botschaft, betont sie doch die Wertigkeit des einzelnen Patienten. Für klinisch tätige Ärzte ist das bereits eine Selbstverständlichkeit. Die Bedeutung eines Wortes, und das sei mit einem Lächeln hinzugefügt, zeigt sich nach Wittgenstein im Gebrauch der Sprache [3].Die Nationale Akademie der Wissenschaften Leopoldina, die acatech Deutsche Akademie der Technikwissenschaften und die Union der Deutschen Akademien der Wissenschaften haben im Dezember 2014 eine Stellungnahme über Voraussetzungen und Konsequenzen der Individualisierten Medizin publiziert. Darin wird der Fokus, wie im Vorwort ausgeführt, auf molekulare, genetische und pharmakologische Aspekte der Onkologie gelegt, einen Bereich, in dem die Individualisierung am weitesten fortgeschritten ist. Diese Beschränkung bedeutet, dass andere eng assoziierte Themen, wie die Patienten- und Versorgungsperspektiven, der Bereich der Medizintechnik oder neue Erkrankungen, beispielsweise in der Psychiatrie, in dieser Studie nicht beleuchtet werden. Die Behandlung dieser Gebiete bedarf einer separaten nachfolgenden Betrachtung. [4].Vor einer Betrachtung wesentlicher Beiträge allein der Bildgebung in der Individualisierten Medizin seien wenige vorausschickende Bemerkungen zu zwei Grenz-Fragen der Wissenschaft gemacht. Was steht für und gegen Stellungnahmen der genannten Akademien? Erfüllt die Beschäftigung mit der Bildgebung Kriterien der Wissenschaft?Eine der Aufgaben der Akademien ist es, Ordnung in die vorhandenen Aussagen und auch damit vorhandene Daten zu bringen, die in unterschiedlichen Disziplinen hervorgebracht werden, um daraus ein Gesamtbild zu formen. Weiterhin sollen Empfehlungen gegeben werden, wie die Entwicklungen günstig zu beeinflussen sind [5]. Wichtige Grenzen der wissenschaftlichen Politik- bzw. Gesellschaftsberatung bilden etwa Katastrophenwarnungen und Heilsverheißungen, andererseits nicht-altruistische, sondern dem Eigeninteresse dienende, wie auch politisierende Verlautbarungen. Das setzt eine Selbstbegrenzung voraus.Wünschenswert ist bei wissenschaftlichem Handeln der Respekt von und vor Grenzen, ist die Distanz zur Macht. Diese Macht kann der Politik, der Ökonomie und auch den Medien zugeschrieben werden; fatal sind Nähe oder gar Einfluss von Ideologien. Dabei wird nicht nur ein behutsamer Umgang mit Empfehlungen angesprochen; im wissenschaftlichen Handeln sind gerade beim Gegenstand der Bildgebung Berührungen mit der und sogar Überlappungen mit Interessen der Industrie möglich, teilweise gewünscht und fruchtbar. Letztlich ist es ja immer die Industrialisierung, die medizinischen Fortschritt, sei er pharmakologisch oder technologisch abbildbar, breiten Bevölkerungsgruppen und entlegenen Standorten zugänglich macht. Das bedarf immer einer gegenseitigen kritischen Begleitung.Die Wissenschaft dient dem Erkenntnisgewinn. Dazu gehört eine methodische Suche nach Wahrheit, die alle Befähigten überprüfen und nachvollziehen können. Wenn dieses angenommen, akzeptiert werden kann, dann sind die Technikwissenschaften Wissenschaften, dann betreiben Radiologie und Nuklearmedizin Wissenschaft (science). Dann bedarf es nicht der Aufzählung von Nobelpreisen oder ähnlichen Anerkennungen in der Scientific Community. Dann bedarf es nicht der Aufzählung fachlicher Beispiele; die Methoden und die Technologien fallen nicht vom Himmel. Wissenschaft wird von Menschen betrieben und getragen. An Pioniere der Bildgebung, an Personen, wie Wilhelm Conrad Röntgen, Marie Curie, Godfrey Hounsfield, Peter Mansfield, Paul Lauterbur oder Stephan Hell sei erinnert.* Dieses Editorial wird zeitgleich in Nuklearmedizin publiziert. Schober O, Dössel O, Ermert H et al. Bildgebung in Klinik und Forschung: Beitrag zur Individualisierten Medizin? Nuklearmedizin 2017; 56: 157-161; https://doi.org/10.3413/2017-05-0002.

Abstract:

Today, patients suffering from atrial arrhythmias like atrial flutter (AFlut) or atrial fibrillation (AFib) are examined in the EP-lab (electrophysiology lab) in order to understand and treat the disease. Multichannel catheters are advanced into the atria in order to measureelectric signals at manyintracardiacpositions simultaneously. Complementary to clinical learning,comprehension of the disease and therapeutic strategies can be improved with computer modeling of the heart. This way, hypotheses about initiation and perpetuation of the arrhythmia can be tested and ablation strategies can be assessed in-silico. Modeling and biosignal analysis can benefit from mutual fertilization. On the one hand, modeling can be improved and personalization can be achieved via high density mapping of the atria. On the other hand, new algorithms for the interpretation of multichannel electrograms can be developed and evaluated with synthetic signals from computer models of the atria. This article illustrates the synergetic potential by examples and highlights challenges to be addressed in the future.

Abstract:

Generating meshes of complex structures in the human body like the heart organ is a prerequisite for computational simulations of of organ function. The quality of the conclusions derived from these simulations greatly depends on the quality and accuracy of the mesh they are based on. Volumetric computation domain can be represented by an equally-spaced voxel grid, or – in case of more sophisticated partial differential equation discretization methods (finite elements, finite volumes) – first, second or even higher order tetrahedral meshes. Here, we present a workflow that is capable of creating high quality meshes for such simulations. The workflow contains segmentation, surface mesh generation, volume mesh generation, and patient-specific parameter fitting to produce the desired results. While segmentation itself is a more or less unique mapping from a grayscale DICOM data set to a labeled, three-dimensional voxel mesh, different approaches exist for their transformation to a surface mesh. Our process involves a two-level approach for obtaining triangular or mixed rectangular surface meshes of desired quality and resolution. Both are crucial for the next step: obtaining a volumetric tetrahedral grid with the desired degrees of freedom. In the last step, a derivative-free parameter estimation approach is used to calibrate the dynamic behavior and tailor the model patient-specifically. All software used in the workflow is published under open source licenses and freely available. Its capability is demonstrated by means of an elastomechanical simulation of a human heart and yields measurable validation quantities in physiological ranges. We want to stress that the presented approach is generic and can easily be used for the model generation of other organs like liver, lungs or the aortic arch as well. The resulting meshes can be used for various types of simulations (electrical excitation propagation, blood flow) and use cases (clinical diagnostics, therapy planning etc.).

Abstract:

Cardiac arrhythmias are a widely spread disease in industrialized countries. A common clinical treatment for this disease is radiofrequency ablation (RFA), in which high frequency alternating current creates a lesion on the myocardium. However, the formation of the lesion is not entirely understood. To obtain more information about ablation lesions (ALs) and their electrophysiological properties, we established an in-vitro setup to record electrical activity of rat myocardium. Electrical activity is measured by a circular shaped multielectrode array. This work was focused to gain more information by developing algorithms to process the measured electrical signals to collect different features, which may allow us to characterize an AL. First, pacing artefacts were detected and blanked. Subsequently, data were filtered. Afterwards, activations in atrial signals were detected using a non-linear energy operator (NLEO) and templates of these activations were generated. Finally, we determined different features on each activation in order to evaluate changes of unipolar as well as bipolar electrograms and considered these features before and after ablation. In conclusion, the majority of the signal features delivered significant differences between normal tissue and lesion. Among others, a reduction in peak to peak amplitude and a diminished spectral power in the band 0 to 100 Hz may be useful indicators for AL. These criteria should be verified in future studies with the aim of estimating indirectly the formation of a lesion.

Abstract:

Atrial arrhythmias such as atrial flutter and atrial fibrillation are a burden for patients and a major challenge for modern healthcare systems. Identification of patients at risk to develop atrial arrhythmias at an early stage carries the potential to reduce the incidence by implementing appropriate strategies to mitigate the risks. Diagnostic methods based on the ECG are ideal risk markers due to their noninvasiveness and omnipresence. The left atrium (LA) plays a major role in the intiation and perpetuation of atrial reentry arrhythmias. However, the LA is not well represented in the P-wave derived through standard ECG leads. Here, we optimize ECG lead positions to maximize LA information content. Towards this end, we used a cohort of eight personalized computational models providing the unique opportunity to separate LA and right atrial (RA) contributions to the P-wave, which is not feasible in vivo. The location of maximum P-wave signal energy was located on the center of the chest for all subjects with marked overlap between regions of maximum LA and RA P-wave amplitude. The regions of highest ratio between LA and RA signal energy differed between patients. However, a region with LA signal energy being higher than that of the RA and providing a sufficiently large absolute P-wave amplitude was identified at the center of the back consistently across five models of the cohort. Optimized linear combinations of standard 12-lead signals yielded comparably good results. Our newly proposed electrode positions on the back as well as selected linear combinations of standard 12-lead signals improve the LA information content considerably. By using these, more relevant diagnostic information regarding the anatomical and electrophysiological properties of the LA can be derived in future.

Abstract:

Chronic kidney disease (CKD) affects more than 30 million patients in the European Union. CKD causes alterations in the extracellular plasma electrolyte concentrations, which affect cardiac electrophysiology. A total of 25% of all deaths of CKD patients are due to sudden cardiac death (SCD). Until recently, ventricular fibrillation was assumed to be the main reason. In a 2015 study, Wong et al. observed bradycardia and asystole as the predominant mechanisms of SCD in patients with CKD. This shows that the influence of electrolyte changes on the underlying mechanisms of pacemaking in the sinoatrial node (SAN) needs to be better understood. In this work, we have updated the computational model of the human SAN given by Fabbri et al. and investigated the CKD-induced change of [Ca2+]o (0.6-3mM), [K+]o (3-9mM) and [Na+]o (120-150mM) on pacemaking. [Ca2+]o had the most dominant effects on SAN function. Low [Ca2+]o caused severe bradycardia in the model (down to 17 bpm) for 0.6 mM. A critical concentration range of calcium in the subspace [Ca2+]sub was identified as the possible underlying mechanism for pacemaking. For increasing [Ca2+]o, the heart rate (HR) increased, resulting in 142 bpm for the highest calcium concentration. The effect of [K+]o variation was similar to the one for [Ca2+]o, but caused less pronounced change. The resultant changes due to variation of [Na+]o were relatively small. In this work, several potential mechanisms for SCD in CKD patients could be identified. The low HR for low [Ca2+]o is seen as a possible link to the observed bradycardia in CKD patients. The findings in this work could lead to a better surveillance of [Ca2+]o in hemodialysis patients, and therefore to a decrease in the SCD rate.

Abstract:

Lung ventilation and perfusion analyses using chest imaging methods require a correct segmentation of the lung to offer anatomical landmarks for the physiological data. An automatic segmentation approach simplifies and accelerates the analysis. However, the segmentation of the lungs has shown to be difficult if collapsed areas are present that tend to share similar gray values with surrounding non-pulmonary tissue. Our goal was to develop an automatic segmentation algorithm that is able to approximate dorsal lung boundaries even if alveolar collapse is present in the dependent lung areas adjacent to the pleura. Computed tomography data acquired in five supine pigs with injured lungs were used for this purpose. First, healthy lung tissue was segmented using a standard 3D region growing algorithm. Further, the bones in the chest wall surrounding the lungs were segmented to find the contact points of ribs and pleura. Artificial boundaries of the dorsal lung were set by spline interpolation through these contact points. Segmentation masks of the entire lung including the collapsed regions were created by combining the splines with the segmentation masks of the healthy lung tissue through multiple morphological operations. The automatically segmented images were then evaluated by comparing them to manual segmentations and determining the Dice similarity coefficients (DSC) as a similarity measure. The developed method was able to accurately segment the lungs including the collapsed regions (DSCs over 0.96).

Abstract:

Cardiac excitation during atrial fibrillation (AFib) is changing dynamically, compromising the ability to identify underlying mechanisms by intracardiac catheter mapping. Statistical analysis of dominant excitation patterns may help to identify and subsequently eliminate the drivers of this tachycardia. As the morphology of local bipolar intracardiac electrograms (EGMs) depends on the orientation of the propagating excitation wave, its evaluation for a fixed multichannel catheter position can provide information about the stability of the depolarization pattern. Up to date, analysis of morphology is most often done by computing a similarity index or the recurrence rate of individual EGMs, reflecting how often similar excitations appear. We sougth to extend this approach to a classification based analysis technique. In each multichannel EGM, local activation waves (LAWs) were automatically detected by assessing instantaneous signal energy. A greedy algorithm was implemented to cluster LAWs based on their similiarity. New clusteres were formed when similarity fell below a predefined threshold. The concept was tested using simulated EGM data (quadratic patch of cardiac tissue, bidomain simulation, both planar and focal excitations, various catheter types). Results demonstrated that the algorithm correctly identified and classified the simulated excitation patterns. Subsequent quantitative analysis allowed to both discard singular classes of excitation and identify dominant excitations. The presented method forms the basis for statistical assessment of prevailing depolarization patterns, and for computation of additional features like conduction velocity, presence of focal sources, or dissociation when applied on multichannel data.

Abstract:

A common treatment of focal ventricular tachycardia is the catheter ablation of triggering sites. They have to be found manually by the physician during an intervention in a catheter lab. Thus, a method for determining the position of the focus automatically is desired. The inverse problem of electrocardiography addresses this problem by reconstructing the source of the ectopic beats using the surface ECG. This problem is ill-posed and therefore needs specific methods for solving it. We propose a machine learning approach for localisation of the ectopic foci in the heart to assist cardiologists with their therapy planning.We simulated 600 120-lead ECGs with different known excitation origins in the heart using a cellular automaton followed by a forward calculation. Features from the ECGs were used as input for a support vector regression (SVR). We assumed a functional relation between features from the ECG and the excitation origin. To benchmark SVR, we also used the well-known Tikhonov 0th order regularisation to reconstruct the transmembrane potentials in the heart and detect the location of the ectopic foci. Parameters for SVR and regularisation were chosen using a grid search minimising the error between estimated and true excitation origin. Compared to the Tikhonov regularisation method, SVR achieved a smaller deviation between estimated and real excitation origin evaluated with 6-fold cross validation. Future work could investigate on the behaviour on data from simulations with other torso and electrophysiological models, the influence of other methods for feature extraction and finally the evaluation with clinical data.

Abstract:

Hyperthermia during radiofrequency ablation causes reversible and irreversible changes of the electrophysiological properties of cardiac tissue. However, the mechanisms are incompletely understood. We studied changes of conduction velocity (CV) in rat myocardium under hyperthermic conditions from macroscopic to microscopic scale by using simultaneous optical mapping and a miniaturized electrode array. Atrial preparations from five rats were superfused at tissue bath temperatures between 36.7°C and 43.8°C. Optical mapping data showed an elevated median CV by 21% when increasing the temperature from 36.7°C to 42.0°C. CV did not increase above 42.0°C. Electrical measurements revealed a similar temperature dependence of CV between 36.7°C and 42.0°C, i.e. an increase of median CV by 26%. The consolidation of optical and electrical data in this study allowed investigation of excitation during global hyperthermia. Macroscopic optical mapping and microscopic electrical measurements demonstrated that hyperthermia strongly influenced electrical propagation at a microscopic scale.

Abstract:

Radiofrequency ablation is the gold standard for treating cardiac arrhythmias. However, the success rate of this procedure depends on numerous parameters. Wet lab experiments provide the opportunity to investigate cardiac electrophysiology under reproducible conditions. To evaluate the electrophysiological changes of ablated myocardium in these studies it is necessary to consider the three-dimensional (3D) geometry of the lesions. For this purpose, we investigated the usage of different magnetic resonance imaging (MRI) sequences as well as an image processing procedure to analyze in-vitro preparations. To differentiate signal intensities between nonablated and ablated tissue we evaluated FISP (fast imaging with steady-state precession; delivering dominantly T1-weighted images) and RARE (rapid acquisition with relaxation enhancement; delivering dominantly T2-weighted images). After image processing, the ablated tissue was segmented in each image slice forming a 3D volume. The geometry of the lesion was modeled by the boundary of this volume. It was generally feasible to distinguish between healthy myocardium and ablated tissue as well as to determine lesion transmurality. The analysis of the reconstructed lesion geometries from FISP and RARE MRI showed a high agreement, however T2-weighted sequences showed larger lesion volumes as well as higher variations in segmentation compared to T1- mapping. FISP with higher quality may be used to reconstruct the 3D geometry of the ablation lesions.

Abstract:

Modelling the interaction of the heart and the vascular system allows to study the pumping efficiency of the heart in a controlled environment under various cardiac and vascular conditions such as arrhythmias, dyssynchronies, regions of stiffened myocardium, valvular stenoses or decreased vascular compliances. To pose realistic hemodynamic boundary conditions to a four-chambered elastomechanical heart model, we developed a lumped element model of the closed loop human vascular system. Systemic and pulmonary circulations were each represented by a three-element Windkessel model emptying into a venous compliance. Both circulations were coupled by connecting the venous compliances to the corresponding atrium via venous resistances. Cardiac valves were represented by ideal diodes and resistances. Strong coupling between the heart and the vascular system model was accomplished by estimating the cardiac pressures that lead to continuous flows across the model interfaces. Active regulatory mechanisms were not considered. Pressures, flows and volumes throughout the circulatory system were simulated until a steady state was reached and the effects of model parameters on these hemodynamic parameters were evaluated in a sensitivity analysis. Increasing the systemic peripheral resistance by 50% caused an 8% decrease in stroke volume (SV) and a 33% increase in mean arterial pressure. Increased venous resistance descreased the E/A wave ratio of the atrioventricular flow and led to a reduced SV by impeding passive cardiac filling. Increasing the arterial compliance decreased mean cardiac pressures, while only slightly reducing the SV. Larger arterial resistances mainly caused higher peak systolic pressures. Furthermore, we show that embedding the heart model into surrounding elastic tissue by forcing permanent contact at the pericardial surface leads to more realistic time courses of atrial volumes and atrial pressure-volume curves composed of an A and a V loop as found in measurements. In conclusion, this work enables simulations of diseases that involve significant cardiovascular interaction.

Abstract:

Intracardiac electrogram recordings during atrial fibrillation (AFib) are characterized by irregular rhythms and complex morphologies. Hence, analysis in the time domain is a difficult task. The so called dominant frequency DF is a spectrum based approach that aims at finding the most relevant frequency in a signal providing information about the rate and dynamics of AFib. However, in recent years various studies reported controversial results regarding the clinical relevance of the DF. In this work, a definition of the DF at a fundamental scale is proposed as the rate at which action potentials are triggered in atrial cells. The most common method to estimate the DF in literature, labeled as DFSpec, is examined in comparison to the proposed definition. A signal processing study using synthetic signals verified that the DFSpec is stable for all changes in morphology of atrial activations. However, it is also demonstrated that the DFSpec becomes unstable for variations above 20% in the cycle length of a signal. Spectrum based DF estimation should be interpreted in a critical manner and is not advisable for study endpoints or clinical markers.

Abstract:

Acquiring adequate mapping data in patients with atrial fibrillation is still one of the main obstacles in the treatment of this atrial arrhythmia. Due to the lack of catheters with both a panoramic field of view and sufficient electrode density for simultaneous mapping, electrophysiologists are forced to fall back on sequential mapping techniques. But, because activation patterns change rapidly during atrial fibrillation, they cannot be mapped sequentially. We propose that mapping tissue properties which are time independent, in contrast, allows a sequential approach. Here, we use the shortest measured electrogram cycle length to estimate the effective refractory period of the underlying tissue in a simulation study. Atrial fibrillation was simulated in a spherical model of the left atrium comprised of regions with varied refractory period. We found that the minimal measured electrogram cycle length correlates with the effective refractory period of the underlying tissue if the regions with distinct refractory properties are large enough and if the absolute difference in effective refractory periods is sufficient. This approach is capable of identifying regions of lowered effective refractory period without the need for cardioversion. Those regions are likely to harbor drivers of atrial fibrillation, which emphasizes the necessity of their localization.

Abstract:

Atrial arrhythmias like atrial fibrillation and atrial flutter are a major health challenge in developed countries. Radiofrequency ablation performed via intracardiac catheters is a curative therapy for these reentrant arrhythmias. However, the optimal location of ablation lesions is not straightforward to determine, particularly for complex activation patterns. Thus, a clinical need for tools to intuitively visualize complex activation patterns and to provide a platform to evaluate different ablation strategies in dry runs is apparent. Here, we present a virtual reality system that allows to interactively simulate atrial excitation propagation and place ablation lesions. Our software builds on the IMHOTEP framework for the Unity3D engine and implements a multithreaded model-view-controller design pattern. Excitation propagation is computed using a fast marching approach considering refractoriness. Interactive rewind and playback is supported through a combination of the flyweight pattern for simulation data with complete snapshots for key frames. The system was evaluated in a user study using the HTC ViveTM headset including two controllers. For high fidelity virtual reality interaction, a minimum frame rate of 60 per second is required. In a biatrial anatomical model comprising 36,059 nodes (Figure 1), even complex activation patterns with multiple wavefronts could be simulated and rendered down to 2x slow motion (1 sec activation sequence displayed during 2 sec wall time) on a desktop machine. Results of the user study suggest added value regarding the comprehension of arrhythmias and ablation options and very good intuitiveness of the user interface requiring almost no teach-in. The virtual reality tool is ready to be used for educational purposes and prepared to import personalized models supporting diagnosis and therapy planning for atrial arrhythmias in the future.

Abstract:

The heart rate variability (HRV) is a measure which is commonly used to assess sympathetic and parasympathetic auto-nomic function. It is well known, that respiration can have a strong influence on HRV. Especially, a phenomenon called Respiratory Sinus Arrythmia (RSA) modulates the RR intervals and is a major contributor to the HRV. The interpreta-tion of common HRV parameters can be ambiguous due to different respiration rates and patterns. To assess this ambi-guity, the coupling of RSA on HRV was quantified and the HRV parameters were compared during different respirato-ry states.A pilot study with five healthy subjects was performed. A three lead ECG was acquired and the respiration was estimat-ed by measuring the aeration of the lungs using the PulmoVista 500 by Dräger. This device uses Electrical Impedance Tomography (EIT) to monitor impedance changes due to the changing amount of air within the lungs during respira-tion. The subjects were asked to breath at controlled respiration rates of 8, 15 and 24 breaths per minute as well as spon-taneously for 1 min each. In addition, to analyze HRV during apnoic phases without any respiration, the subjects were asked to hold their breath for 40s at end-inspiration and end-expiration. After preprocessing of the ECG and the respiration signal, the coupling between the measured respiration and the RR intervals was quantified using the Granger causality. If significant coupling was present, the HRV was separated from its respiratory influence using an ARMAX model. The measured respiration hereby formed the exogeneous input to the filter. Finally, common HRV parameters were calculated for the original and the decoupled RR intervals.We showed, that coupling strength depends on respiratory rates, which might complicate HRV interpretation. Moreo-ver, the coupling is decreased during spontaneous breathing in comparison to controlled respiration. Additionally we found, that HRV parameters during apnoic phases differ from decoupled HRV parameters during spontaneous or con-trolled respiration.

Abstract:

Electrocardiographic imaging (ECGI) could help in diagnosis and treatment of atrial fibrillation (AF), the most common life-threatening arrhythmia. Based on a previous work by Figuera et al. on the reconstruction of epicardial potentials (EP) during AF, we explore the performance of a Tikhonov regularization with two spatial constraints for transmembrane voltage (TMV) based ECGI. We develop a new method to impose epi-endocardial similarity and show its benefit for ECGI of atrial activity. Apart from TMVs, local activation times and dominant frequency are evaluated as target parameters. In the AF models studied, joint reconstruction of epi- and endocardial TMVs showed performance comparable to the previously reported EPs imaging.

Abstract:

Atrial fibrillation and atrial flutter are the most common atrial arrhythmias placing a heavy burden on patients and posing a challenge on healthcare systems. If patients at risk to develop atrial arrhythmias can be identified at an early stage, the arrhythmia incidence can be lowered by implementing appropriate strategies to mitigate the risks. Diagnostic methods based on the ECG are ideal risk markers due to their noninvasiveness and omnipresence. The left atrium (LA) plays a major role in the initiation and perpetuation of atrial reentry arrhythmias. However, the LA is not well represented in the P-wave derived through standard ECG leads. Here, we optimize ECG leads to maximize LA information content. Towards this end, we used a cohort of eight personalized computational models providing the unique opportunity to separate LA and right atrial (RA) contributions to the P-wave, which is not feasible in vivo. The location of maximum P-wave signal energy was located on the center of the chest for all subjects with marked overlap between regions of maximum LA and RA P-wave amplitude. The regions of highest ratio between LA and RA signal energy differed between patients. However, a region with LA signal energy being higher than that of the RA and providing a sufficiently large absolute P-wave signal energy was identified at the lower left quadrant of the back consistently across most subjects of the cohort. Optimized linear combinations of standard 12-lead signals (considering the eight independent leads) yielded comparably good results amplifying LA information by more than one order of magnitude. Our newly proposed electrode positions on the back as well as selected combinations of standard ECG signals improve the LA information content considerably. By using these, more relevant diagnostic information regarding anatomical and electrophysiological properties of the LA can be derived in future.

Abstract:

Chronic kidney disease appears worldwide. In the United States, the number of patients suffering from kid- ney failure doubled from 1998 to 2010. A common treat- ment for these patients is haemodialysis. However, the frequency of deaths caused by cardiovascular diseases is up to 10% to 30% higher in patients undergoing dialysis than in the general population. To analyse the underly- ing effects and for a possible risk prediction, a continuous monitoring of the ionic concentrations that are influenced by dialysis is desired. In this work, a method for the re- construction of the ionic concentrations of calcium and potassium from the ECG is proposed. In a first step, 91 monodomain simulations with the ten Tusscher ventricular cell model were performed for different extracellular ionic concentrations. From there, a standard 12-lead ECG was extracted. Calcium and potassium changes yielded ECGs clearly differing in amplitude and morphology. In a second step, the simulated ECG signals were used for reconstruc- ting the ionic concentrations directly from the ECG. Fea- tures were extracted from the signals designed to describe changes caused by varied ionic concentrations. The in- verse problem, i.e. coming back from the ECG features to the ionic concentrations was solved by regression with an artificial neural network. Results for potassium estimation yield an error of 0.00±0.28 mmol/l (mean±standard de- viation) calculated with 7-fold cross validation. The esti- mation error for calcium was 0.00±0.08 mmol/l. Although these results underline the suitability of the method, the used ECGs differed from the observed in a clinical envi- ronment. However, simulations allow an evaluation un- der controlled conditions of a particular effect that was intended to be investigated. As the application to clinical data is yet missing, this study can be seen as a proof of concept showing that an artificial neural network is capa- ble of exactly estimating potassium and calcium concen- trations from ECG features. 1. Introduction Haemodialysis therapy is a common treatment method for patients suffering from chronic kidney disease (CKD) in the terminal stage. The amount of people in the United States suffering from kidney failure increased from 320,000 in 1998 to 650,000 in 2010. The frequency of deaths caused by cardiovascular events within the dialysis patient group is up to 10% to 30% higher than in gene- ral population [1]. Patients suffering from end-stage CKD experience high variations of blood electrolyte concentra- tions. These can directly influence the functioning of the heart. Thus, research on cardiovascular links could im- prove therapy and risk stratification. One tool which is capable of capturing the electrophysiological properties of the heart in a non-invasive way is the electrocardiogram (ECG). It is known, that electrolyte concentrations of po- tassium (K+) and calcium (Ca2+) affect the ECG [2]. Un- til now, a determination of the concentrations is connec- ted to a blood test. Hence, continuous monitoring of the ionic concentration is impracticable. However, the ECG as a continuous, non-invasive monitoring tool could shed a light on the relation between heart diseases and changes in the ionic concentration particularly after leaving the strictly supervised clinical area where dialysis takes place, i.e allowing a monitoring at home. Articles have been pub- lished showing that the reconstruction of extracellular K+ concentration can be done using just one feature from the ECG with a quadratic regression [3]. In this study, we tried to estimate both K+ and Ca2+ concentrations from the ECG. Therefore, we examined simulated ECGs at dif- ferent concentration levels and designed features descri- bing the observed changes in the ECG. A subset of these was used in connection with a machine learning method to reconstruct the concentrations. 2. Methods 2.1. Simulations A total number of 91 computer simulations of the car- diac electrophysiology were performed at whole heart

Abstract:

Atrial fibrillation (AF) is the most common type of arrhythmia encountered in clinical practice but its maintaining mechanisms remain elusive. Over the last years, various theories have been proposed to target AF mechanisms. Recently, there has been an increasing interest in understanding how spiral waves and rotors sustain AF and how they might be therapeutic targets for catheter-based ablation. Phase mapping has recently been used as a robust method to characterize the spatiotemporal variability of electrical activities. In this study, we propose an independent approach for basket catheter electrogram (EGM) processing to detect rotors in AF. An improved version of the sinusoidal recomposition method for the local activation timings (LATs) has been developed and 3D phase maps have been reconstructed. An algorithm able to detect stable and meandering rotors on the left atrium (LA) surface was then developed. This workflow has been validated on synthetic EGMs and in silico showing excellent results. On in vivo data, we found 4.0±3.4 and 4.6±5.0 localized and meandering rotors with a persistence in time: 303.2 ±58.2ms and 302.3±52.0ms respectively.

Abstract:

Atrial fibrillation (AF) ablation guided by basket catheter mapping has shown to be beneficial. Yet, the initial excitement is mitigated by a growing skepticism due to the difficulty in verifying the protocol in multicenter studies. Overall, the underlying assumptions of rotor ablation require further verification. The aim of this study was therefore to test such hypotheses by using computational modeling. The 3D left atrial geometry of an AF patient was segmented from a pre-operative MR scan. Atrial activation was simulated on the 3D anatomy using the monodomain approach and a variant of the Courtemanche action potential model. Ablated tissue was assigned zero conductivity. Reentry was successfully initialized by applying a single suitably delayed extra stimulus. Unipolar electrograms were computed at the simulated electrode positions. The final dataset was generated by varying location of reentry and catheter position within the LA. The effect of inter-electrode distance and distance to the atrial wall was studied in relation to the ability to recover rotor trajectory, as computed by a novel algorithm described here. The effect of rotor ablation was also assessed.

Abstract:

Electrical impedance tomography (EIT) can trace ventilation and perfusion related changes in electrical properties of lung tissue. So far, the ability of EIT to assess lung perfusion has been examined experimentally by electron-beam and single-photon-emission computerized tomography (CT) in healthy or globally injured lungs [1,2].