Abstract:

Atrial Fibrillation (AFib) is the most common cardiac arrhythmia. Despite the considerable clinical experience and accumulated evidence from experimental data, the exact mechanism of AFib and their elimination by catheter ablation techniques is still unknown. The aim of this work is to investigate measured intracardiac ECGs with methods of signal processing and multivariate statistical techniques to get a better understanding of atrial excitation during atrial fibrillation. Therefor intracardiac ECGs measured in the coronary sinus during sinus rhythm, atrial flutter and atrial fibrillation were processed and compared. After fragmentation into patterns, the data is analysed by Principal Component Analysis (PCA). Using this new representation of the original data a clustering process is performed and the time-distance between the found clusters is calculated. The main goal of this study is to give quantitative data on spread of depolarization during AFib. The developed algorithms can also be used to analyse complex fractionated atrial electrograms (CFAEs) in further studies.

Abstract:

The curative therapy of atrial fibrillation (AF) is still challenging. Although the electrophysiologists know many strategies to cure AF, the underlying mechanisms are still mostly unknown. Also the optimal ablation strategy for paroxysmal and long-lasting persistent AF is not known. Complex fractionated atrial electrograms (CFAEs) are becoming more and more important in the ablation strategies, especially for long-lasting persistant AF. Automated detection and signal analysis of CFAEs is essential in supporting the physicians during the ablation procedure. The robust algorithm to locate CFAEs presented in the contribution by Nguyen, Schilling and Dössel delivers a good bases for postprocessing and signal analysis of CFAEs. It is employing a non-linear energy operator combined with thresholding. In this paper this new algorithm is tested on clinical data and compared to clinically accepted algorithms.

Abstract:

Atrial fibrillation is the most common cardiac arrhythmia and often leads to severe complications such as stroke and other embolic incidents. Areas of complex fractionated atrial activity are in the focus of electrophysiologists and have been used as a target for catheter ablation therapy. The underlying mechanisms of complex fractionated atrial electrograms (CFAEs) are not entirely understood. CFAEs may contain concurrent rhythmic episodes of signals with differing characteristic frequencies (CFs). We propose a new algorithm to detect multiple periodicities in atrial signals.First, we preprocess the signal by applying Teager's non-linear energy operator. Next, the first three characteristic frequencies are detected in the frequency spectrum. Information contained in the harmonics is used to recursively detect the exact frequency. Frequency information is then transformed into the time domain, where repeated occurance of signal activity according to the respective cycle length is found. Further, the detection rate and the mean distance to gravity are calculated as key figures to determine more characteristics of the periodicity.The algorithm performs well in detecting the rhythmic components of atrial signals. It has been tested using real patient data acquired during electrophysiological studies in sinus rhythm, atrial flutter and several forms of atrial fibrillation, as well as with simulated data produced by a cellular automaton at our research group.Its application may provide new insights into atrial signals especially CFAEs and the interpretation of characteristic and dominant frequencies. It can be the foundation of displaying rhythmicity and CF information onto the 3D representation of the patient's atrium and give the physician an impression of the organization and regularity of cardiac electrograms.

Abstract:

Atrial fibrillation (AFib) is the most common cardiac arrhythmia. Areas in atrial tissue with complex fractionated atrial electrograms (CFAEs) are among others responsible for the maintenance of AFib. Those areas are ideal target sites for ablation to eliminate AFib and restore sinus rhythm. As CFAEs are associated with high fibrillatory frequency, automated identification of CFAEs with spectral analysis helps developing objective strategies for AFib ablation. While the application of current techniques is restricted, this paper introduces a new approach to determine characteristic frequencies during AFib. By using Teagers energy operator we calculate the signal envelope and study its spectrum after Fast Fourier Transformation. Harmonic analysis of distinctive peaks in the power spectrum is carried out to assess characteristic frequencies of a CFAE. While the currently available methods only find one dominant frequency in the spectrum of the signal, our method is capable to find multiple characteristic frequencies, if present. Since it is believed that during AFib the atrium is activated by one or multiple wavelets, our method opens new opportunities for investigation of multiple wavelets propagation.

Abstract:

Areas in atrium tissue with complex fractionated atrial electrograms (CFAEs) are among others responsible for the maintenance of atrial fibrillation (AFib). Those areas are ideal target sites for ablation to eliminate AFib and restore normal rhythm. An automated identification of CFAEs with signal processing algorithms is essential to develop an objective strategy for AFib ablation. This paper introduces a new approach to locate signal complexes corresponding to electrophysiological activity. The idea behind this algorithm is based on the idea of Pan-Tompkins QRS-detection algorithm. However in this approach, the extracted signal feature is the signal energy and therefore the algorithm takes into account not only information of the frequency but also of the amplitude. With adaptive thresholding the algorithm is capable to manage changes in the signal dynamics. The results were validated by experts and the algorithm shows a robust performance.

Abstract:

Patient-specific model adaptation and validation requires a comparison of simulations with measured patient data. For patients suffering from atrial fibrillation, such data is mainly available as intracardiac catheter signals. In this work, we demonstrate the simulation of clinically relevant catheter data as measured using circular mapping catheters (such as Lasso or Orbiter) and coronary sinus catheters using atrial simulations on a realistic geometry. Four circular catheters are modeled using a projection technique for two distinct types of application. We show that in sinus rhythm, the choice of a distinct electrophysiological model does not impair the signal quality. Finally, we compare simulated potentials to a real clinical measurement. In the future, with patient- specific models available, such comparisons can constitute an important interface for personalizing cardiac models.

Abstract:

Intracardiac catheter recordings are available in common clinical practice. They can therefore be employed to adapt and validate atrial computer models of individual patients. Hence, their information content needs to be analyzed quantitatively. During treatment of atrial arrhythmia such as atrial flutter or fibrillation, the location of ectopic foci in the pulmonary veins is of special interest. In this study, virtual catheter signals are extracted from an atrial simulation on a realistic geometry with normal sinus rhythm as well as ectopic stimuli in all four pulmonary veins. Using a simplified Pan-Tompkins algorithm, the activation times are determined. Based on the analysis of the activation sequence in a circular mapping catheter simulated on the posterior left atrial wall, all four ectopic foci can clearly be associated with the pulmonary vein they came from. For a catheter on the anterior wall, this is possible for three of the four ectopic beats. Despite the knowledge gathered for the personalization of patient models, such simulations may help cardiologists to better classify measured signals.

Abstract:

Patient-specific cardiac simulations are approaching clinical applications. They could for example improve the treatment of atrial fibrillation (AF). Currently, many patients suffering from AF are treated with minimally-invasive catheter ablation. Using this technique, trigger sources for AF (mainly the pulmonary veins), are electrically isolated from the rest of the atrium. However, a large set of different ablation strategies is currently used in clinical practice. Therefore, the choice of a certain ablation strategy as well as the probability for successful and sustained AF termination are strongly dependent on the experience of the cardiologist. Atrial simulations could assist the cardiologist in the choice of a suitable method for an individual patient. For this, the atrial models have to be adapted to the patient. Besides anatomical modeling, several challenges must be faced in this process. First, an appropriate model of cellular electrophysiology and excitation conduction must be chosen. The model must provide the necessary accuracy and at the same time be fast enough for clinical applications. As a trade-off between accuracy and speed, we propose a minimal model adapted to atrial electrophysiology. Second, a main problem is the adaptation of physiological parameters in the patient-specific model as well as its validation. Therefore, an interface between clinical data and the model is needed. Data collected in standard clinical workflow are mainly intracardiac catheter ECGs. We therefore present techniques to model such catheter measurements. Signals from both circular mapping catheters (such as Lasso or Orbiter) as well as Coronary Sinus catheters can be simulated and compared to clinical signals. These are important steps towards clinical applications of atrial models. The long-term goal then is to assist the cardiologist in the choice of the best treatment for an individual patient.