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 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 %.