Abstract:

Cardiologists measure electric signals inside the human heart aiming at a better diagnosis and optimized therapy of atrial arrhythmias like atrial flutter and atrial fibrillation. The catheters that are used for this purpose are improving: now they are able to pick up the electric signals at up to 64 positions inside the heart simultaneously. The patterns of electric depolarization are sometimes very simple, comparable to plane waves. But in case of patients with severe atrial arrhythmias they can be quite complex: U-turns around a line of block, ectopic centres, break throughs, reentry circuits, rotors, fractionated signals and chaotic patterns are often observed. Methods of biosignal analysis can support the cardiologists in classifying the signals and extract information of high diagnostic relevance. Computer models of the electrophysiology of the human heart can serve to design better algorithms for data analysis and to test algorithms, because the ground truth is known.

Abstract:

The arrhythmogenic mechanisms of atrial fibrillation (AF) are still not well understood. Increased atrial fibrosis is a structural hallmark in patients with persistent AF. We assessed the electrogram signature rotational activity and their spatial relationship to low voltage areas in patients with persistent AF. Computer simulations implicating 3- dimensional atrial tissue with different amount of atrial fibrosis were used to assess development and stability of rotational activities during AF. Rotor anchoring occurred at the borderzone between fibrosis and healthy atrial tissue with 12 consecutive rotations prior to rotor extinction. Rotational activity in fibrotic tissue resulted in fractionated signals and were overlapped with large negative electrograms in unipolar recording mode from neighboring healthy tissue impressing as a focal source. Necessary conditions for development and stability of rotational activities around fibrosis were on the one hand a minimum size of atrial fibrosis area equal or larger than 10mm x 10mm and on the other hand the degree of atrial fibrosis of 40%. Clinical data showed that AF termination sites were located within low voltage areas (displaying <0,5mV in AF on the multielectrode mapping catheter) in 80% and at their borderzones in 20% of cases.

Abstract:

Aiming for patient specific treatment of atrial fibrillation, cardiologists in the EP-lab (ElectroPhysiology-lab) intend to identify the pattern of depolarization waves in the atria by measuring endocardial electrograms with multichannel catheters. Hereby the pattern of plane waves, ectopic foci, lines of block, or rotors are of special interest. Data acquisition is performed with various multichannel catheters, and all four patterns leave different fingerprints in the electrograms. In this work we extract features from the activation sequence in the electrograms that can support the cardiologist to identify the underlying depolarization pattern. To this end computer simulations of fundamental depolarization scenarios were carried out and the corresponding activation patterns were analyzed.

Abstract:

Atrial fibrillation is the most common arrhythmia. However, the mechanisms of AF are not completely understood. It is known that fractionated signals are measured in AF but the etiology of fractionated signals is still not clear. The central question is to evaluate the effects of segmented fibrotic areas in histological tissue sections on the extracellular potential in a simulation study. We calculated the transmembrane voltages and extracellular potentials from the excitation wave front around a 3D fibrotic area from mouse hearts that were reconstructed from histological tissue sections. Extracellular potentials resulted in fragmented signals and differed strongly by stimulations from different directions. The transmural angle of the excitation waves had a significantly influence on the signal morphologies. We suggest for future clinical systems to implement the possibility for substrate mapping by stimulations from different directions in sinus rhythm.

Abstract:

Atrial fibrillation is a common irregular heart rhythm. Until today there is still a need for research to quantify typical signal characteristics of rotors, which can induce atrial fibrillation. In this work, signal characteristics of a stable and a more unstable rotor in a realistic heart model including fiber orientation were analyzed with the following methods: peak-to-peak amplitude, Hilbert phase, approximate entropy and RS-difference. In this simulation model the stable rotor rotated with a cycle length of 145 ms and stayed in an area of 1.5 mm x 3 mm. Another more unstable rotor with a cycle length of 190 ms moved in an area of 10 mm × 4 mm. In a distance of 2 mm to the rotor tip, the peak-to-peak amplitude decreased significantly, whereas the RS-difference and the approximate entropy were maximal. The rotor center trajectories were detected by phase singularity points determined by the Hilbert transform. We showed that more unstable rotors resulted in more amplitude changes over time and also the cycle length differed more. Furthermore, we presented typical activation time patterns of the Lasso catheter centered at the rotor tip and in different distances to the rotor tip. We suggest that cardiologists use a combination of the described methods to determine a rotor tip position in a more robust manner.

Abstract:

There is still a need for research to understand the co- herences of the origin of arrhythmias such like rotors and possible ablation strategies. The aim of this work was the analysis of typical signal characteristics near a rotor cen- ter. Rotors were simulated on 2D patch geometry (100 mm x 100 mm) with spatial resolution of 0.1mm. Based on extracellular potentials, different features were evalu- ated: Local activation time, peak to peak amplitude, steep- est negative slope and approximate entropy were com- pared regarding their ability to indicate the rotor tip lo- cation. Furthermore, typical signal patterns of different mapping catheters centered at the rotor tip position were analyzed. The determined maximum distances between the focal point of phase singularities and determined centers by the peak to peak amplitudes were maximal 1.7 mm.

Abstract:

Direction of propagation (DOP) and conduction velocity (CV) of excitation waves are essential parameters to identify targets for catheter ablation of cardiac arrhythmias. Most approaches to determine the DOP and CV rely on manual anno- tation. Many, time-consuming measurements with mapping catheters are required. Aim of this work was to quantitatively extract the DOP and the CV of wavefronts from intracardiac electrograms with a single shot measurement. We used a simulation database of planar waves computed with a cellular automaton with different CVs between 500 mm/s and 1100 mm/s. By comparing the correct values of CV and DOP with the computed values from the developed algorithm the median CV- error was between 7 mm/s and 50 mm/s and the median DOP- error variated between 1\0 and 4\0.

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:

Atrial fibrillation is the most common cardiac arrhythmia. Many physicians believe in the hypothesis that persistent atrial fibrillation is maintained by centers of rotatory activity. These so called rotors are sometimes found by physicians during catheter ablation or electrophysiological studies but there are also physicians who claim that they did not find any rotors at all. One reason might be that today rotors are mainly identified by visual inspection of the data. Thus we are aiming at an algorithm for rotor detection. We first developed an algorithm based on the local activation times of the intracardiac electrograms recorded by a multielectrode catheter that can automatically determine the cycle length coverage. This was done to get an objective view on possible rotors and therefore help to quantify whether a rotor was found or not. The algorithm was developed and evaluated in two different simulation setups, where it could reliably determine cycle length coverage. But we found out that effects like wave collision and slow conduction have strong influence on cycle length coverage. This prevents cycle length coverage from being suited as the only parameter to quantify whether a rotor is present or not. On the other hand we could confirm that rotors imply a cycle length coverage of >70% if the multielectrode catheter is centered in an area of <5 mm away from the rotor tip. Therefore cycle length coverage can at least be used in some situations to exclude the presence of possible rotors.

Abstract:

The goal of this research was to classify cardiac excitation patterns during atrial fibrillation (AFib). For this purpose, virtual models of intracardiac mapping catheters were moved across in-silico cardiac tissue to extract local activation times (LATs) of each catheter electrode from simulated cardiac action potential (AP) signals. The resulting LAT patterns consisting of the LATs of all electrodes resemble patterns measured in clinical cases. The LATs represent the input information for features that were used to separate four different excitation patterns during AFib. Those four excitation patterns were plane wave, ectopic focus (spherical wave), rotor (spiral wave) and block. A feature selection algorithm was used to investigate the features concerning their power to classify the different simulated excitation patterns. The scores of the selected features were used to train and optimize a support vector machine (SVM). The optimized and cross-validated SVM was then used to classify the simulated cardiac excitation patterns. The achieved overall classification accuracy of this SVM model was 98.4 %.

Abstract:

By means of computer modeling general comprehension of electrophysiology (EP) of human atria can be improved and simulated patterns of ectopic foci, reentry and rotors can be created. On the other hand atrial electrograms are measured in the EP lab of many hospitals every day. In this contribution simulated and measured clinical signals are compared critically aiming at better understanding of atrial fibrillation and validation of computer modeling.

Abstract:

This work aimed at the detection of rotor centers within the atrial cavity during atrial fibrillation on the basis of phase singularities. A voxel based method was established which employs the Hilbert transform and the phase of unipolar electrograms. The method provides a 3D overview of phase singularities at the endocardial surface and within the blood volume. Mapping those phase singularities from the inside of the atria at the endocardium yielded rotor center trajectories.We discuss the results for an unstable and a more stable rotor. The side length of the areas covered by the trajectories varied from 1.5mm to 10 mm. These results are important for cardiologists who target rotors with RF ablation in order to cure atrial fibrillation.