Abstract:

Catheter ablation has emerged as an effective treatment strategy for atrial fibrillation (AF) in recent years. During AF, complex fractionated atrial electrograms (CFAE) can be recorded and are known to be a potential target for ablation. Automatic algorithms have been developed to simplify CFAE detection, but they are often based on a single descriptor or a set of descriptors in combination with sharp decision classifiers. However, these methods do not reflect the progressive transition between CFAE classes. The aim of this study was to develop an automatic classification algorithm, which combines the information of a complete set of descriptors and allows for progressive and transparent decisions. We designed a method to automatically analyze CFAE based on a set of descriptors representing various aspects, such as shape, amplitude and temporal characteristics. A fuzzy decision tree (FDT) was trained and evaluated on 429 predefined electrograms. CFAE were classified into four subgroups with a correct rate of 81+/-3%. Electrograms with continuous activity were detected with a correct rate of 100%. In addition, a percentage of certainty is given for each electrogram to enable a comprehensive and transparent decision. The proposed FDT is able to classify CFAE with respect to their progressive transition and may allow objective and reproducible CFAE interpretation for clinical use.

Abstract:

Multiscale cardiac modeling has made great advances over the last decade. Highly detailed atrial models were created and used for the investigation of initiation and perpetuation of atrial fibrillation. The next challenge is the use of personalized atrial models in clinical practice. In this study, a framework of simple and robust tools is presented, which enables the generation and validation of patient-specific anatomical and electrophysiological atrial models. Introduction of rule-based atrial fiber orientation produced a realistic excitation sequence and a better correlation to the measured electrocardiograms. Personalization of the global conduction velocity lead to a precise match of the measured P-wave duration. The use of a virtual cohort of nine patient and volunteer models averaged out possible model-specific errors. Intra-atrial excitation conduction was personalized manually from left atrial local activation time maps. Inclusion of LE-MRI data into the simulations revealed possible gaps in ablation lesions. A fast marching level set approach to compute atrial depolarization was extended to incorporate anisotropy and conduction velocity heterogeneities and reproduced the monodomain solution. The presented chain of tools is an important step towards the use of atrial models for the patient-specific AF diagnosis and ablation therapy planing.

Abstract:

Atrial arrhythmias are frequently treated using catheter ablation during electrophysiological (EP) studies. However, success rates are only moderate and could be improved with the help of personalized simulation models of the atria. In this work, we present a workflow to generate and validate personalized EP simulation models based on routine clinical computed tomography (CT) scans and intracardiac electrograms. From four patient data sets, we created anatomical models from angiographic CT data with an automatic segmentation algorithm. From clinical intracardiac catheter recordings, individual conduction velocities were calculated. In these subject-specific EP models, we simulated different pacing maneuvers and measurements with circular mapping catheters that were applied in the respective patients. This way, normal sinus rhythm and pacing from a coronary sinus catheter were simulated. Wave directions and conduction velocities were quantitatively analyzed in both clinical measurements and simulated data and were compared. On average, the overall difference of wave directions was 15° (8%), and the difference of conduction velocities was 16 cm/s (17%). The method is based on routine clinical measurements and is thus easy to integrate into clinical practice. In the long run, such personalized simulations could therefore assist treatment planning and increase success rates for atrial arrhythmias.

Abstract:

Conduction velocity (CV) and CV restitution are important substrate parameters for understanding atrial arrhythmias. The aim of this work is to (i) present a simple but feasible method to measure CV restitution in-vivo using standard circular catheters, and (ii) validate its feasibility with data measured during incremental pacing. From five patients undergoing catheter ablation, we analyzed 8 datasets from sinus rhythm and incremental pacing sequences. Every wavefront was measured with a circular catheter and the electrograms were analyzed with a cosine-fit method that calculated the local CV. For each pacing cycle length, the mean local CV was determined. Furthermore, changes in global CV were estimated from the time delay between pacing stimulus and wavefront arrival. Comparing local and global CV between pacing at 500 and 300 ms, we found significant changes in 7 of 8 pacing sequences. On average, local CV decreased by 2015% and global CV by 1713%. The method allows for in-vivo measurements of absolute CV and CV restitution during standard clinical procedures. Such data may provide valuable insights into mechanisms of atrial arrhythmias. This is important both for improving cardiac models and also for clinical applications, such as characterizing arrhythmogenic substrates during sinus rhythm.

Abstract:

Atrial arrhythmias, such as atrial flutter or fibrillation, are frequent indications for catheter ablation. Recorded intracardiac electrograms (EGMs) are, however, mostly evaluated subjectively by the physicians. In this paper, we present a method to quantitatively extract the wave direction and the local conduction velocity from one single beat in a circular mapping catheter signal. We simulated typical clinical EGMs to validate the method. We then showed that even with noise, the average directional error was below 10(°) and the average velocity error was below 5.4 cm/s. In a realistic atrial simulation, the method could clearly distinguish between stimuli from different pulmonary veins. We further analyzed eight clinical data segments from three patients in normal sinus rhythm and with stimulation. We obtained stable wave directions for each segment and conduction velocities between 70 and 115 cm/s. We conclude that the method allows for easy quantitative analysis of single macroscopic wavefronts in intracardiac EGMs, such as during atrial flutter or in typical clinical stimulation procedures after termination of atrial fibrillation. With corresponding simulated data, it can provide an interface to personalize electrophysiological (EP) models. Furthermore, it could be integrated into EP navigation systems to provide quantitative data of high diagnostic value to the physician

Abstract:

Background: Catheter ablation of persistent atrial fibrillation (AF) is challenging. The underlying mechanisms are mostly unknown and discussed very controversially. Automated detection and signal analysis of complex fractionated atrial electrograms (CFAEs) is essential in supporting the physicians during the ablation procedure. To investigate the clinical value of descriptors for CFAEs, we calculate their value before and after pulmonary vein isolation (PVI). Pulmonary vein isolation effects the excitation propagation of AF. This should be detected by every descriptor. We calculated the dominant frequency (DF), the fractionation index (CFE-Idx), and the activity ratio (AR) before and after PVI.Methods: (1) A common analysis technique of AF is DF analysis. It is an estimation of the atrial activation rates. (2) Ensite-NavX provides an algorithm that delivers a CFE-Idx based on the cycle length of distinguishable local activities in one electrogram. (3) A third method calculates atrial activity with a segmentation algorithm based on a nonlinear energy operator. Complex fractionated atrial electrograms are marked as active segments. The AR is then defined as the ratio between the length of active segments and the total length of the signal [2]. Dominant frequency, CFE-Idx, and AR were compared on data sets of 17 patients suffering from persistent AF. All patients were sent to hospital for catheter ablation. Electrograms of 5 seconds were recorded before and after PVI at customary 46 locations per patient in the left atrium. Nine patients terminated during ablation (A), whereas 8 patients did not terminate (B) and underwent an external cardioversion. Results: The mean DF decreased from 5.7 ± 0.6 to 5.5 ± 0.3 Hz (A) and increased from 5.3 ± 0.5 to 5.5 ± 0.5 Hz (B). Mean CFE-Idx increased from 157 ± 68 to 223 ± 51 milliseconds (A) and from 222 ± 88 to 273 ± 72 milliseconds (B). Mean AR decreased from 0.65 ± 0.1 to 0.63 ± 0.04 (A) and increased from 0.69 ± 0.5 to 0.72 ± 0.1 (B).Conclusion: More regular excitation should result in higher CFE-Idx and lower DF and AR. We found intergroup differences and could show the influence of PVI on the excitation during AF. The fractionation index of CFE has shown the most distinct results in differentiation of the 2 states of PVI (before/after) and also in differentiation of group A to B. Nevertheless, AR and DF are promising alternatives. Removal of outliers will increase performance of AR and DF.

Abstract:

Catheter ablation of atrial fibrillation (AF), especially persistent AF, is still challenging. The underlying mechanisms are not yet completely understood and are discussed very controversially. Automated detection and analysis of complex frac- tionated atrial electrograms is essential in supporting the electrophysiologists during ablation therapy. Signal analysis of atrial signals works better the less noise and unwanted signals superimpose the signal to be analysed. As for catheter ablation of persistent AF the atrial signals play the most important role, ventricular activity is unwanted to be seen. For catheter positions in close proximity to the ventricles, i.e. the coronary sinus catheter, those ventricular far fields are taint- ing the atrial signals. For this reason we present a method to cancel the ventricular far field from atrial electrograms. Atrial segments synchronized to the ventricular activity are extracted and the ventricular far field is cancelled by use of a PCA approach. Signal processing of the sole atrial electrogram leads to better results and therefore can better support the abla- tion therapy.

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:

Intracardiac electrograms are the key in under- standing, interpretation and treatment of cardiac arrhythmias. However, electrogram morphologies are strongly variable due to catheter position, orientation and contact. Simulations of intracardiac electrograms can improve comprehension and quantification of influencing parameters and therefore reduce misinterpretations. In this study simulated intracardiac electro- grams are analyzed regarding tilt angles of the catheter relative to the propagation direction, electrode tissue distances as well as clinical filter settings. Catheter signals are computed on a realistic 3D catheter geometry using bidomain simulations of cardiac electrophysiology. Thereby high conductivities of the catheter electrodes are taken into account. For validation, simulated electrograms are compared with in vivo electrograms recorded during an EP-study with direct annotation of catheter orientation and tissue contact. Good agreement was reached regarding timing and signal width of simulated and measured electrograms. Correlation was 0.92±0.07 for bipolar, 0.92±0.05 for unipolar distal and 0.80 ± 0.12 for unipolar proximal electrograms for different catheter orientations and locations.

Abstract:

A framework for step-by-step personalization of a computational model of human atria is presented. Beginning with anatomical modeling based on CT or MRI data, next fiber structure is superimposed using a rule-based method. If available, late-enhancement-MRI images can be considered in order to mark fibrotic tissue. A first estimate of individual electrophysiology is gained from BSPM data solving the inverse problem of ECG. A final adjustment of electrophysiology is realized using intracardiac measurements. The framework is applied using several patient data. First clinical application will be computer assisted planning of RF-ablation for treatment of atrial flutter and atrial fibrillation.

Abstract:

In spite of the considerable medical and technical progress during the last years, catheter ablation of atrial fibrillation is still challenging. For a successful execution of the ablation and the avoidance of intricacies the catheter must be in contact with the endocardium, which is still difficult to assure with existent techniques. It would be desirable to detect the endocardial catheter contact directly from the signal shape and its properties. In this work, significant signal property changes were detected and investigated, which allow an automatic contact detection. Furthermore, atrial electrograms were simulated and compared with a database of measured and annotated signals. During these simulations, the distance between endocardium and the catheter tip could be chosen discretionary. The simulated signals revealed themselves to be very accurate. Simulations can now be used to analyse intracardiac signals more closely. The exact position of the catheter will hereby always be assured, which is not always granted in clinical practice.

Abstract:

Background: Catheter ablation of complex atrial arrhythmias, such as atrial fibrillation and atypical atrial flutter, is still challenging. Clinically evaluated ablation methods are leading to moderate success rates. Assessments of intracardiac electrograms are often done subjectively by the physician. Automatic algorithms can therefore improve the analysis of complex atrial electrograms (EGMs). In this work, we demonstrate a quantitative analysis of intracardiac EGMs from circular mapping catheters in humans. Both the wave direction and the local conduction velocity (CV) were calculated from individual wave fronts passing the catheter.Methods: Intracardiac EGMs measured with circular mapping catheters in humans were retrospectively analyzed. Five data sets from 3 patients undergoing catheter ablation of atrial fibrillation or flutter were available. Using a nonlinear energy operator, activation times from 9 bipolar catheter signals were calculated for each atrial activity. The resulting activation pattern was fitted to a cosine-shaped data model that has been validated in a previous simulation study. The cosine phase represented the wave direction. From the cosine amplitude and the catheter radius, the conduction velocity was calculated.Results: The wave directions in all five measurements were stable with a standard deviation below 10°. Calculated CVs were in the range of 70 to 110 cm/s, which is in accordance with published values. In one patient, electrograms were recorded during atrial stimulation. Stimulation cycle length was decreased from 500 to 300 milliseconds. Conduction velocity decreased by approximately 10% at a cycle length of 300 milliseconds compared with the CV at 500 milliseconds.Conclusion: The results show the ability to reliably extract wave direction and conduction velocity from intracardiac EGMs recorded with circular mapping catheters. Detected directions were stable, and the CV values were in a physiological range. As individual beats are analyzed, the method will also enable the quantitative study of singular events such as ectopic beats and facilitate the localization of tachycardia origins. Further, it will help to measure substrate parameters such as the CV and even CV restitution behavior. This way, the method can help to identify patient-specific physiological parameters that can be integrated into patient-specific models. Furthermore, it can directly provide quantitative data of high diagnostic value to the examiner and thereby improve clinical success rates.

Abstract:

Catheter ablation of complex atrial arrhythmias is a frequently applied procedure, but its success rates are only moderate and highly dependent on the experience of the physician. Personalized atrial simulation models could assist the physician in treatment planning and thus increase success rates. In this work we created a personalized anatomical model for a specific patient from CT image data. Left atrial conduction velocity and local wave directions were determined from intracardiac electrogram (EGM) recordings. We simulated normal sinus rhythm and the clinical pacing protocol using a Cellular Automaton. The incidence direction and conduction velocity were extracted from the simulated data and compared to the results of the clinical EGMs of the same patient. We then showed that the incidence angles differed by less than 15% and that the conduction velocity error was below 12 cm/s. This implies that the model has similar electric properties compared to the real atria. In conclusion, we have presented a workflow for model personalization and validation.

Abstract:

Atrial fibrillation (AFib) is a frequent and serious cardiac arrhythmia. A successful method to treat AFib is catheter ab- lation. Areas with complex fractionated atrial electrograms (CFAE) are ideal targets for catheter ablation. Concerning the ablation strategy and the search for CFAEs the physician is mainly dependent on his own judgment. For this reason ablation strategies are highly operator dependent. In this work a set of seven descriptors is presented which show promising results concerning a classification of measured atrial electrograms. The descriptors are evaluated on a database of 25 CFAE sig- nals. The results reveal a possible discrimination between CFAE classes which could be a valuable support for physicians curing AFib

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.

Abstract:

This work provides methods to measure and analyze features of atrial electrograms - especially complex fractionated atrial electrograms (CFAEs) - mathematically. Automated classification of CFAEs into clinical meaningful classes is applied and the newly gained electrogram information is visualized on patient specific 3D models of the atria. Clinical applications of the presented methods showed that quantitative measures of CFAEs reveal beneficial information about the underlying arrhythmia.