Abstract:

Radiofrequency ablation (RFA) therapy is the gold standard in interventional treatment of many cardiac arrhythmias. A major obstacle are non transmural lesions, leading to recurrence of arrhythmias. Recent clinical studies have suggested intracardiac electrogram (EGM) criteria as a promising marker to evaluate lesion development. Seeking for a deeper understanding of underlying mechanisms, we established a simulation approach for acute RFA lesions. Ablation lesions were modeled by a passive necrotic core surrounded by a borderzone with properties of heated myocardium. Herein, conduction velocity and electrophysiological properties were altered. We simulated EGMs during RFA to study the relation between lesion formation and EGM changes using the bidomain model. Simulations were performed on a three dimensional setup including a geometrically detailed representation of the catheter with highly conductive electrodes. For validation, EGMs recorded during RFA procedures in five patients were analyzed and compared to simulation results. Clinical data showed major changes in the distal unipolar EGM. During RFA, the negative peak amplitude decreased up to 104% and maximum negative deflection was up to 88% smaller at the end of the ablation sequence. These changes mainly occurred in the first 10 s after ablation onset. Simulated unipolar EGM reproduced the clinical changes, reaching up to 83% negative peak amplitude reduction and 80% decrease in maximum negative deflection for transmural lesions. In future work, the established model may enable the development of further EGM criteria for transmural lesions even for complex geometries in order to support clinical therapy.

Abstract:

For the purpose of accurate preclinical drug screening and particularly to evaluate the risk of undesired cardiac arrhyth- mias or drug induced toxicity, human embryonic stem cell derived cardiomyocytes clusters can be used. A novelty micrcocavity array screening platform has been developed to facilitate recordings of extracellular field potentials and de- tect QT prolongation and cardiotoxic effects. The measured signal is similar to a human ECG with a missing P wave. In order to automate the drug screening process and delineate the filed potential recordings a signal-analyzing algorithm has been developed.

Abstract:

The risk stratification of sudden cardiac death after my- ocardial infarction plays an important role in cardiology. It influences the treatment of a patient and the use of im- plantable devices. However, the majority of well known methods for stratifying risk still fail to predict sudden car- diac death with high accuracy. The heart rate turbulence delivers good results that could be complemented by study- ing ECG morphology. For this purpose, the post extrasys- tolic T wave change was studied in this work. 10 patients with structural healthy ventricles were paced in the right ventricular apex and the subsequent response of the heart was measured in the ECG. Complementary, computer sim- ulations of the human transmembrane voltages and poste- rior ECG reconstruction were also carried out. Morpho- logical changes in the post extrasystolic T wave and its restitution to the original shape were measurable in every patient of this study. The patients presented diminished or alternating postectopic T waves and prolongation of T wave duration. However, the simulation does not present significant T wave changes. Furthermore, the new mor- phological parameters do not seem to correlate with the standard HRT parameters.

Abstract:

Ectopic beats are a common cause for cardiac arrhythmia. The methods presented in this paper deal with the evaluation of the features that are used by an existing classifier to distinguish between normal, supraventricular ectopic and ventricular ectopic beats. In order to classify the beats, a support vector machine (SVM) is used. Since noisy features can confuse the classifier and downgrade its performance, high quality features should be chosen. In the end, the performance should be improved by using only the selected features after the evaluation process. For this purpose, a receiver operating character- istic (ROC) analysis was conducted first. Secondly, the Gini diversity index (GDI) was calculated for every feature which is often used as split criterion in decision trees. As a third evaluation tool, the information gain ratio (IGR) was applied to estimate the quality of the features. To conclude the evaluation part, a fourth analysis was implemented. The ROC was applied again to the beats that are falsely classified in a first run-through. This was a first step into a deeper investigation of the dependency among features. As result of the evaluation process, a feature ranking was built and 36 of the 55 features were chosen to build the new SVM. A training and testing process was conducted using beats of the MIT-BIH-Arrhythmia- Database. A correct rate of 98.574%, a sensitivity of 98.592% and a positive predictive value of 99.062% were achieved.

Abstract:

Post-ablation atrial flutter(AF) is a frequently occurring arrhythmia after treatment for persistent atrial fibrillation. However, mapping the flutter circuit using intracardiac electrograms is often challenging due to low signal voltage and scar areas caused by prior substrate modification. In addition, signals are frequently compromised by ventricular far field (VFF) artifacts, which obscure atrial activity (AA). This work introduces a new approach for VFF removal, which is based on the Periodic Component Analysis (&#1113099;CA). It utilizes the stable temporal relationship between AA and VFF, which poses a problem for other techniques like Principal Component Analysis (PCA) when both components superpose. A benchmark using simulated electrograms demonstrated significantly better correlation for this case when comparing pure AA to the reconstructed data using &#1113099;CA instead of PCA (0.98 vs. 0.90, p<0.001). Its benefit for diagnosis is demonstrated on clinical data.