AIMS: The treatment of atrial fibrillation beyond pulmonary vein isolation has remained an unsolved challenge. Targeting regions identified by different substrate mapping approaches for ablation resulted in ambiguous outcomes. With the effective refractory period being a fundamental prerequisite for the maintenance of fibrillatory conduction, this study aims at estimating the effective refractory period with clinically available measurements. METHODS AND RESULTS: A set of 240 simulations in a spherical model of the left atrium with varying model initialization, combination of cellular refractory properties, and size of a region of lowered effective refractory period was implemented to analyse the capabilities and limitations of cycle length mapping. The minimum observed cycle length and the 25% quantile were compared to the underlying effective refractory period. The density of phase singularities was used as a measure for the complexity of the excitation pattern. Finally, we employed the method in a clinical test of concept including five patients. Areas of lowered effective refractory period could be distinguished from their surroundings in simulated scenarios with successfully induced multi-wavelet re-entry. Larger areas and higher gradients in effective refractory period as well as complex activation patterns favour the method. The 25% quantile of cycle lengths in patients with persistent atrial fibrillation was found to range from 85 to 190 ms. CONCLUSION: Cycle length mapping is capable of highlighting regions of pathologic refractory properties. In combination with complementary substrate mapping approaches, the method fosters confidence to enhance the treatment of atrial fibrillation beyond pulmonary vein isolation particularly in patients with complex activation patterns.
OBJECTIVE: Atrial flutter (AFl) is a common arrhythmia that can be categorized according to different self-sustained electrophysiological mechanisms. The non-invasive discrimination of such mechanisms would greatly benefit ablative methods for AFl therapy as the driving mechanisms would be described prior to the invasive procedure, helping to guide ablation. In the present work, we sought to implement recurrence quantification analysis (RQA) on 12-lead ECG signals from a computational framework to discriminate different electrophysiological mechanisms sustaining AFl. METHODS: 20 different AFl mechanisms were generated in 8 atrial models and were propagated into 8 torso models via forward solution, resulting in 1,256 sets of 12-lead ECG signals. Principal component analysis was applied on the 12-lead ECGs, and six RQA-based features were extracted from the most significant principal component scores in two different approaches: individual component RQA and spatial reduced RQA. RESULTS: In both approaches, RQA-based features were significantly sensitive to the dynamic structures underlying different AFl mechanisms. Hit rate as high as 67.7% was achieved when discriminating the 20 AFl mechanisms. RQA-based features estimated for a clinical sample suggested high agreement with the results found in the computational framework. CONCLUSION: RQA has been shown an effective method to distinguish different AFl electrophysiological mechanisms in a non-invasive computational framework. A clinical 12-lead ECG used as proof of concept showed the value of both the simulations and the methods. SIGNIFICANCE: The non-invasive discrimination of AFl mechanisms helps to delineate the ablation strategy, reducing time and resources required to conduct invasive cardiac mapping and ablation procedures.
Background: Rate-varying S1S2 stimulation protocols can be used for restitution studies to characterize atrial substrate, ionic remodeling, and atrial fibrillation risk. Clinical restitution studies with numerous patients create large amounts of these data. Thus, an automated pipeline to evaluate clinically acquired S1S2 stimulation protocol data necessitates consistent, robust, reproducible, and precise evaluation of local activation times, electrogram amplitude, and conduction velocity. Here, we present the CVAR-Seg pipeline, developed focusing on three challenges: (i) No previous knowledge of the stimulation parameters is available, thus, arbitrary protocols are supported. (ii) The pipeline remains robust under different noise conditions. (iii) The pipeline supports segmentation of atrial activities in close temporal proximity to the stimulation artifact, which is challenging due to larger amplitude and slope of the stimulus compared to the atrial activity. Methods and Results: The S1 basic cycle length was estimated by time interval detection. Stimulation time windows were segmented by detecting synchronous peaks in different channels surpassing an amplitude threshold and identifying time intervals between detected stimuli. Elimination of the stimulation artifact by a matched filter allowed detection of local activation times in temporal proximity. A non-linear signal energy operator was used to segment periods of atrial activity. Geodesic and Euclidean inter electrode distances allowed approximation of conduction velocity. The automatic segmentation performance of the CVAR-Seg pipeline was evaluated on 37 synthetic datasets with decreasing signal-to-noise ratios. Noise was modeled by reconstructing the frequency spectrum of clinical noise. The pipeline retained a median local activation time error below a single sample (1 ms) for signal-to-noise ratios as low as 0 dB representing a high clinical noise level. As a proof of concept, the pipeline was tested on a CARTO case of a paroxysmal atrial fibrillation patient and yielded plausible restitution curves for conduction speed and amplitude. Conclusion: The proposed openly available CVAR-Seg pipeline promises fast, fully automated, robust, and accurate evaluations of atrial signals even with low signal-to-noise ratios. This is achieved by solving the proximity problem of stimulation and atrial activity to enable standardized evaluation without introducing human bias for large data sets.
In patients with atrial fibrillation, intracardiac electrogram signal amplitude is known to decrease with increased structural tissue remodeling, referred to as fibrosis. In addition to the isolation of the pulmonary veins, fibrotic sites are considered a suitable target for catheter ablation. However, it remains an open challenge to find fibrotic areas and to differentiate their density and transmurality. This study aims to identify the volume fraction and transmurality of fibrosis in the atrial substrate. Simulated cardiac electrograms, combined with a generalized model of clinical noise, reproduce clinically measured signals. Our hybrid dataset approach combines and clinical electrograms to train a decision tree classifier to characterize the fibrotic atrial substrate. This approach captures different dynamics of the electrical propagation reflected on healthy electrogram morphology and synergistically combines it with synthetic fibrotic electrograms from experiments. The machine learning algorithm was tested on five patients and compared against clinical voltage maps as a proof of concept, distinguishing non-fibrotic from fibrotic tissue and characterizing the patient's fibrotic tissue in terms of density and transmurality. The proposed approach can be used to overcome a single voltage cut-off value to identify fibrotic tissue and guide ablation targeting fibrotic areas.
Atrial flutter (AFL) is a common atrial arrhythmia typically characterized by electrical activity propagating around specific anatomical regions. It is usually treated with catheter ablation. However, the identification of rotational activities is not straightforward, and requires an intense effort during the first phase of the electrophysiological (EP) study, i.e., the mapping phase, in which an anatomical 3D model is built and electrograms (EGMs) are recorded. In this study, we modeled the electrical propagation pattern of AFL (measured during mapping) using network theory (NT), a well-known field of research from the computer science domain. The main advantage of NT is the large number of available algorithms that can efficiently analyze the network. Using directed network mapping, we employed a cycle-finding algorithm to detect all cycles in the network, resembling the main propagation pattern of AFL. The method was tested on two subjects in sinus rhythm, six in an experimental model of in-silico simulations, and 10 subjects diagnosed with AFL who underwent a catheter ablation. The algorithm correctly detected the electrical propagation of both sinus rhythm cases and in-silico simulations. Regarding the AFL cases, arrhythmia mechanisms were either totally or partially identified in most of the cases (8 out of 10), i.e., cycles around the mitral valve, tricuspid valve and figure-of-eight reentries. The other two cases presented a poor mapping quality or a major complexity related to previous ablations, large areas of fibrotic tissue, etc. Directed network mapping represents an innovative tool that showed promising results in identifying AFL mechanisms in an automatic fashion. Further investigations are needed to assess the reliability of the method in different clinical scenarios.
OBJECTIVE: Unipolar intracardiac electrograms (uEGMs) measured inside the atria during electro-anatomic mapping contain diagnostic information about cardiac excitation and tissue properties. The ventricular far field (VFF) caused by ventricular depolarization compromises these signals. Current signal processing techniques require several seconds of local uEGMs to remove the VFF component and thus prolong the clinical mapping procedure. We developed an approach to remove the VFF component using data obtained during initial anatomy acquisition. METHODS: We developed two models which can approximate the spatio-temporal distribution of the VFF component based on acquired EGM data: Polynomial fit, and dipole fit. Both were benchmarked based on simulated cardiac excitation in two models of the human heart and applied to clinical data. RESULTS: VFF data acquired in one atrium were used to estimate model parameters. Under realistic noise conditions, a dipole model approximated the VFF with a median deviation of 0.029mV, yielding a median VFF attenuation of 142. In a different setup, only VFF data acquired at distances of more than 5mm to the atrial endocardium were used to estimate the model parameters. The VFF component was then extrapolated for a layer of 5mm thickness lining the endocardial tissue. A median deviation of 0.082mV (median VFF attenuation of 49x) was achieved under realistic noise conditions. CONCLUSION: It is feasible to model the VFF component in a personalized way and effectively remove it from uEGMs. SIGNIFICANCE: Application of our novel, simple and computationally inexpensive methods allows immediate diagnostic assessment of uEGM data without prolonging data acquisition.
Atypical atrial flutter (AFlut) is a reentrant arrhythmia which patients frequently develop after ablation for atrial fibrillation (AF). Indeed, substrate modifications during AF ablation can increase the likelihood to develop AFlut and it is clinically not feasible to reliably and sensitively test if a patient is vulnerable to AFlut. Here, we present a novel method based on personalized computational models to identify pathways along which AFlut can be sustained in an individual patient. We build a personalized model of atrial excitation propagation considering the anatomy as well as the spatial distribution of anisotropic conduction velocity and repolarization characteristics based on a combination of a priori knowledge on the population level and information derived from measurements performed in the individual patient. The fast marching scheme is employed to compute activation times for stimuli from all parts of the atria. Potential flutter pathways are then identified by tracing loops from wave front collision sites and constricting them using a geometric snake approach under consideration of the heterogeneous wavelength condition. In this way, all pathways along which AFlut can be sustained are identified. Flutter pathways can be instantiated by using an eikonal-diffusion phase extrapolation approach and a dynamic multifront fast marching simulation. In these dynamic simulations, the initial pattern eventually turns into the one driven by the dominant pathway, which is the only pathway that can be observed clinically. We assessed the sensitivity of the flutter pathway maps with respect to conduction velocity and its anisotropy. Moreover, we demonstrate the application of tailored models considering disease-specific repolarization properties (healthy, AF-remodeled, potassium channel mutations) as well as applicabiltiy on a clinical dataset. Finally, we tested how AFlut vulnerability of these substrates is modulated by exemplary antiarrhythmic drugs (amiodarone, dronedarone). Our novel method allows to assess the vulnerability of an individual patient to develop AFlut based on the personal anatomical, electrophysiological, and pharmacological characteristics. In contrast to clinical electrophysiological studies, our computational approach provides the means to identify all possible AFlut pathways and not just the currently dominant one. This allows to consider all relevant AFlut pathways when tailoring clinical ablation therapy in order to reduce the development and recurrence of AFlut.
OBJECTIVE: Atrial tachycardia (AT) still pose a major challenge in catheter ablation. Although state-of-the-art electroanatomical mapping systems allow to acquire several thousand intracardiac electrograms (EGMs), algorithms for diagnostic analysis are mainly limited to the amplitude of the signal (voltage map) and the local activation time~(LAT map). We applied spatio-temporal analysis of EGM activity to generate maps indicating reentries and diastolic potentials, thus identifying and localizing the driving mechanism of AT. METHODS: First, the time course of active surface area (ASA) is determined during one basic cycle length (BCL). The global cycle length coverage (gCLC) reflects the relative duration within one BCL for which activity was present in each individual atrium. A local cycle length coverage (lCLC) is computed for circular sub-areas with 20mm diameter. The simultaneous active surface area sASA is determined to indicate the spatial extent of depolarizing tissue. RESULTS: Combined analysis of these spatial scales allowed to correctly identify and localize the driving mechanism: gCLC values of 100% were indicative for atria harbouring a reentrant driver. lCLC could detect micro reentries within an area of 1.651.28cm in simulated data and differentiate them against focal sources. Mid-diastolic potentials, being potential targets for catheter ablation, were identified as the areas showing confined activity based on sASA values. CONCLUSION: The concept of spatio-temporal activity analysis proved successful and correctly indicated the tachycardia mechanism in 20 simulated AT scenarios and three clinical data sets. SIGNIFICANCE: Automatic interpretation of intracardiac mapping data could help to improve the treatment strategy in complex cases of AT.
Background: During atrial fibrillation, heterogeneities and anisotropies result in a chaotic propagation of the depolarization wavefront. The electrophysiological parameter called conduction velocity (CV) influences the propagation pattern over the atrium. We present a method that determines the regional CV for deformed catheter shapes, which result due to the catheter movement and changing wall contact.Methods: The algorithm selects stable catheter positions, finds the local activation times (LAT), considers the wall contact and calculates all CV estimates within the area covered by the catheter. The method is evaluated with simulated data and then applied to four clinical data sets. Both sinus rhythm activity as well as depolarization wavefronts initiated by stimulation are analyzed. The regional CV is compared with the fractionation duration (FD) and peak-to-peak (P2P) voltages. A speed of 0.5 m/s was defined to create the simulated LAT.Results: After analyzing the simulated LAT with clinical catheter spatial coordinates, the median CV of 0.5 m/s with an interquartile range of 0.22 and exact CV direction vectors were obtained. For clinical cases, the CV magnitude range of 0.08 m/s to 1.0 m/s was obtained. The P2P amplitude of 0.7 mV to 3.7 mV and the mean FD from 40.79ms to 48.66ms was obtained. The correlation of 0.86 was observed between CV and P2P amplitude, and 0.62 between CV and FD.Conclusion: In this paper, a method is presented and validated which calculates the CV for the deformed catheter and changing wall contact. In an exemplary clinical data set correlation between regional CV with FD and the P2P voltage was observed.
G. Lenis, N. Pilia, T. Oesterlein, A. Luik, C. Schmitt, and O. Dössel. P wave detection and delineation in the ECG based on the phase free stationary wavelet transform and using intracardiac atrial electrograms as reference. In Biomedizinische Technik. Biomedical Engineering, vol. 61(1) , pp. 37-56, 2016
Robust and exact automatic P wave detection and delineation in the electrocardiogram (ECG) is still an interesting but challenging research topic. The early prognosis of cardiac afflictions such as atrial fibrillation and the response of a patient to a given treatment is believed to improve if the P wave is carefully analyzed during sinus rhythm. Manual annotation of the signals is a tedious and subjective task. Its correctness depends on the experience of the annotator, quality of the signal, and ECG lead. In this work, we present a wavelet-based algorithm to detect and delineate P waves in individual ECG leads. We evaluated a large group of commonly used wavelets and frequency bands (wavelet levels) and introduced a special phase free wavelet transformation. The local extrema of the transformed signals are directly related to the delineating points of the P wave. First, the algorithm was studied using synthetic signals. Then, the optimal parameter configuration was found using intracardiac electrograms and surface ECGs measured simultaneously. The reverse biorthogonal wavelet 3.3 was found to be optimal for this application. In the end, the method was validated using the QT database from PhysioNet. We showed that the algorithm works more accurately and more robustly than other methods presented in literature. The validation study delivered an average delineation error of the P wave onset of -0.32+/-12.41 ms when compared to manual annotations. In conclusion, the algorithm is suitable for handling varying P wave shapes and low signal-to-noise ratios.
BACKGROUND AND OBJECTIVE: Progress in biomedical engineering has improved the hardware available for diagnosis and treatment of cardiac arrhythmias. But although huge amounts of intracardiac electrograms (EGMs) can be acquired during electrophysiological examinations, there is still a lack of software aiding diagnosis. The development of novel algorithms for the automated analysis of EGMs has proven difficult, due to the highly interdisciplinary nature of this task and hampered data access in clinical systems. Thus we developed a software platform, which allows rapid implementation of new algorithms, verification of their functionality and suitable visualization for discussion in the clinical environment. METHODS: A software for visualization was developed in Qt5 and C++ utilizing the class library of VTK. The algorithms for signal analysis were implemented in MATLAB. Clinical data for analysis was exported from electroanatomical mapping systems. RESULTS: The visualization software KaPAVIE (Karlsruhe Platform for Analysis and Visualization of Intracardiac Electrograms) was implemented and tested on several clinical datasets. Both common and novel algorithms were implemented which address important clinical questions in diagnosis of different arrhythmias. It proved useful in discussions with clinicians due to its interactive and user-friendly design. Time after export from the clinical mapping system to visualization is below 5min. CONCLUSION: KaPAVIE(2) is a powerful platform for the development of novel algorithms in the clinical environment. Simultaneous and interactive visualization of measured EGM data and the results of analysis will aid diagnosis and help understanding the underlying mechanisms of complex arrhythmias like atrial fibrillation.
Whole-chamber mapping using a 64-pole basket catheter (BC) has become a featured approach for the analysis of excitation patterns during atrial fibrillation. A flexible catheter design avoids perforation but may lead to spline bunching and influence coverage. We aim to quantify the catheter deformation and endocardial coverage in clinical situations and study the effect of catheter size and electrode arrangement using an in silico basket model. Atrial coverage and spline separation were evaluated quantitatively in an ensemble of clinical measurements. A computational model of the BC was implemented including an algorithm to adapt its shape to the atrial anatomy. Two clinically relevant mapping positions in each atrium were assessed in both clinical and simulated data. The simulation environment allowed varying both BC size and electrode arrangement. Results showed that interspline distances of more than 20 mm are common, leading to a coverage of less than 50% of the left atrial (LA) surface. In an ideal in silico scenario with variable catheter designs, a maximum coverage of 65% could be reached. As spline bunching and insufficient coverage can hardly be avoided, this has to be taken into account for interpretation of excitation patterns and development of new panoramic mapping techniques.
Background: Intracardiac electrograms are an indispensable part during diagnosis of supraventriculararrhythmias, but atrial activity (AA) can be obscured by ventricular far-fields (VFF). Concepts based onstatistical independence like principal component analysis (PCA) cannot be applied for VFF removalduring atrial tachycardia with stable conduction.Methods: A database of realistic electrograms containing AAand VFF was generated. Both PCA and thenew technique periodic component analysis (πCA) were implemented, benchmarked, and applied toclinical data.Results: The concept of πCA was successfully verified to retain compromised AA morphology,showing high correlation (cc = 0.98 ± 0.01) for stable atrial cycle length (ACL). Performance ofPCA failed during temporal coupling (cc = 0.03 ± 0.08) but improved for increasing conductionvariability (cc = 0.77 ± 0.14). Stability of ACL was identified as a critical parameter for πCAapplication. Analysis of clinical data confirmed these findings.Conclusion: πCA is introduced as a powerful new technique for artifact removal in periodic signals.Its concept and performance were benchmarked against PCA using simulated data and demonstratedon measured electrograms.
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.
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.
In case of chest pain, immediate diagnosis of myocardial ischemia is required to respond with an appropriate treatment. The diagnostic capability of the electrocardiogram (ECG), however, is strongly limited for ischemic events that do not lead to ST elevation. This computational study investigates the potential of different electrode setups in detecting early ischemia at 10 minutes after onset: standard 3-channel and 12-lead ECG as well as body surface potential maps (BSPMs). Further, it was assessed if an additional ECG electrode with optimized position or the right-sided Wilson leads can improve sensitivity of the standard 12-lead ECG. To this end, a simulation study was performed for 765 different locations and sizes of ischemia in the left ventricle. Improvements by adding a single, subject specifically optimized electrode were similar to those of the BSPM: 211% increased detection rate depending on the desired specificity. Adding right-sided Wilson leads had negligible effect. Absence of ST deviation could not be related to specific locations of the ischemic region or its transmurality. As alternative to the ST time integral as a feature of ST deviation, the K point deviation was introduced: the baseline deviation at the minimum of the ST-segment envelope signal, which increased 12-lead detection rate by 7% for a reasonable threshold.
There is evidence that rotors could be drivers that maintain atrial fibrillation. Complex fractionated atrial electrograms have been located in rotor tip areas. However, the concept of electrogram fractionation, defined using time intervals, is still controversial as a tool for locating target sites for ablation. We hypothesize that the fractionation phenomenon is better described using non-linear dynamic measures, such as approximate entropy, and that this tool could be used for locating the rotor tip. The aim of this work has been to determine the relationship between approximate entropy and fractionated electrograms, and to develop a new tool for rotor mapping based on fractionation levels. Two episodes of chronic atrial fibrillation were simulated in a 3D human atrial model, in which rotors were observed. Dynamic approximate entropy maps were calculated using unipolar electrogram signals generated over the whole surface of the 3D atrial model. In addition, we optimized the approximate entropy calculation using two real multi-center databases of fractionated electrogram signals, labeled in 4 levels of fractionation. We found that the values of approximate entropy and the levels of fractionation are positively correlated. This allows the dynamic approximate entropy maps to localize the tips from stable and meandering rotors. Furthermore, we assessed the optimized approximate entropy using bipolar electrograms generated over a vicinity enclosing a rotor, achieving rotor detection. Our results suggest that high approximate entropy values are able to detect a high level of fractionation and to locate rotor tips in simulated atrial fibrillation episodes. We suggest that dynamic approximate entropy maps could become a tool for atrial fibrillation rotor mapping.
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.
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.
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
Conference Contributions (55)
A. Luik. Fortschritt - erst technisch dann klinisch. In Cardio Campus, vol. 24(11/12) , pp. 24, 2021
Catheter ablation of atrial fibrillation (AF) is still challenging and the sustaining mechanisms are discussed controversially. Basket mapping has emerged to a promising technique to detect temporary events like focal impulses fast changing fibrillation waves or meandering rotors.The aim of this study was to evaluate the atrial coverage of the basket catheter with respect to the distance of the electrodes to the endocardial surface and inter spline separation.
A. Luik, C. Schilling, O. Dössel, and C. Schmitt. Einfluss der segmentalen Pulmonalvenenisolation auf die Defraktionierung bei Patienten mit persistierendem Vorhofflimmern. In Deutsche Gesellschaft für Kardiologie 75. Jahrestagung Mannheim, 2009
A. Luik, C. Schilling, M. Merkel, O. Dössel, and C. Schmitt. Effect of Pulmonary Vein Isolation on the mean Fractionation and the mean dominant Frequency of the left atrium in Patients with Persistent Atrial Fibrillation. In Heart Rhythm, vol. 6(5S) , pp. 153, 2009
L. A. Unger, A. Luik, A. Haas, and O. Dössel. Comparison of Morphology-Based and Delay-Based Measures for Reference Beat Classification during Atrial Tachycardia. In Computing in Cardiology Conference (CinC), 2020
Beat acceptance and rejection during atrial tachycardia are crucial for the compilation of meaningful electroanatomical maps during an electrophysiological study. State of the art methods compare the delays in activation time between two or more electrograms recorded with electrodes of a spatially stable reference catheter. This work introduces morphology-based measures for beat selection in the context of mapping atrial tachycardia. Active segments were extracted from bipolar reference electrograms with the help of the non-linear energy operator. After prealignment by means of maximum cross-correlation, the correlation coefficient as well as the normalized 1-norm distance yielded a similarity measure for each pair of prealigned active segments. The morphology-based measures were then compared to the delay-based measure. In an exemplary patient with 5163 recorded beats, the delay-based measures were strongly dependent on the accuracy of the local activation times as well as on the selection of reference leads. The morphology-based measures emphasized changes in the target tachycardia which were not detectable by the delay-based method. The correlation and the distance measure showed similar behavior but stressed different aspects of morphological changes. Ventricular components in active segments caused minor changes in morphology which were also reflected in the morphology-based measures. The morphology-based measures introduced in this work enhanced beat selection in the exemplary patient. A follow-up study with a representative patient cohort needs to quantify the improvement across patients and translate the measure to clinical practice. A combination of activation delays and morphological similarity is strongly expected to exploit the advantages of both methods for beat selection.
Heterogeneous atrial substrate can induce, maintain and promote cardiac arrhythmias. The level of heterogeneity may be used to assess disease progression. One key parameter, suspected to be correlated with tissue vitality is the conduction velocity (CV). By measuring not only the current CV of the patient but rather its rate dependent changes, restitution information is gained. In the following, we show our approach towards a patient-specific quantitative atrial substrate characterization by combining sets of local and global CV restitution measurements to create a parametrization of the individual patient substrate characteristics.
Atrial fibrillation is the most common cardiac arrhythmia characterized by a rapid and irregular atrial excitation rate. Mimicking this behaviour, the S1-S2 stimulation protocol is currently the clinically established method for measuring tissue rate dependency, leading to a need for an automated segmentation method. We propose a method for stimulus artefact removal tailored towards the S1-S2 protocol. We show that this method results in the detection of atrial signals minimizing distortion by the stimulus artefact and is therefore an effective segmentation tool and a building block for automation of signal analysis.
Cardiac arrhythmias such as atrial fibrillation occur frequently in industrialized countries. Radiofrequency ablation (RFA) is a standard treatment if drug therapy fails. This minimally invasive surgery aims at stabilizing the heart rhythm on a permanent basis. However, the procedure commonly needs to be repeated because of the high recurrence rate of arrhythmias. Non-transmural lesions as well as gaps within linear lesions are among the main problems during the RFA. The assessment of lesion formation is not adequate in state of the art procedures. Therefore, the aim of this study is to investigate the short-term reversibility of lesions using human electrograms recorded by a high-density mapping system during an electrophysiological study (EPS). A predefined measurement protocol was executed during the EPS in order to create three ablation points in the left atrium. Subsequently, after preprocessing the recorded signals, electrogram (EGM) paths were formed along the endocardial surface of the atrium. By analyzing changes of peak to peak amplitudes of unipolar EGMs before and after ablation, it was possible to distinguish lesion area and healthy myocardium. The peak to peak amplitudes of the EGMs decreased by 40-61% after 30 seconds of ablation. Furthermore, we analyzed the morphological changes of EGMs surrounding the lesion. High-density mapping data showed that not only the tissue, which had direct contact with the catheter tip during the RFA, but also the surrounding tissue was affected. This was demonstrated by low peak to peak amplitudes in large areas with a width of 14 mm around the center of the ablation lesion. After right pulmonary vein isolation, high-density mapping was repeated on the previous lesions. The outer region of RFA-treated tissue appears to recover as opposed to the central core of the ablation point. This observation suggests that the meaningfulness of an immediate remap after ablation during an EPS may lead the physician to false conclusions.
O. Dössel, T. Oesterlein, L. Unger, A. Loewe, C. Schmitt, and A. Luik. Spatio-temporal Analysis of Multichannel Atrial Electrograms Based on a Concept of Active Areas. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, vol. 2018, pp. 490-493, 2018
Atrial tachycardia and atrial flutter are frequent arrhythmia that occur spontaneously and after ablation of atrial fibrillation. Depolarization waves that differ significantly from sinus rhythm propagate across the atria with high frequency (typically 140 to 220 beats per minute). A detailed and personalized analysis of the spread of depolarization is imperative for a successful ablation therapy. Thus, catheters with several electrodes are employed to measure multichannel electrograms inside the atria. Here we propose a new concept for spatio-temporal analysis of multichannel electrograms during atrial tachycardia and atrial flutter. It is based on the calculation of simultaneously active areas. The method allows to identify atrial tachycardia and to automatically distinguish between subtypes of focal activity, micro-reentry and macro-reentry.
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.
Intracardiac electrogram recordings during atrial fibrillation (AFib) are characterized by irregular rhythms and complex morphologies. Hence, analysis in the time domain is a difficult task. The so called dominant frequency DF is a spectrum based approach that aims at finding the most relevant frequency in a signal providing information about the rate and dynamics of AFib. However, in recent years various studies reported controversial results regarding the clinical relevance of the DF. In this work, a definition of the DF at a fundamental scale is proposed as the rate at which action potentials are triggered in atrial cells. The most common method to estimate the DF in literature, labeled as DFSpec, is examined in comparison to the proposed definition. A signal processing study using synthetic signals verified that the DFSpec is stable for all changes in morphology of atrial activations. However, it is also demonstrated that the DFSpec becomes unstable for variations above 20% in the cycle length of a signal. Spectrum based DF estimation should be interpreted in a critical manner and is not advisable for study endpoints or clinical markers.
Recent studies about the development of endocardial radiofrequency (RF) ablation lesions (ALs) tried to identify reliable electrogram (EGM) markers for assessment of lesion transmurality. Additional clinically relevant information for physicians can be provided by examining endocardial EGM parameters like signal morphology, amplitude or time points in the signal. We investigated EGM features of the pulmonary vein ostia before and after RF ablation for three point-shaped lesions. Using high-density (HD) mapping, local activation time (LAT) and voltage maps were created, which provided information about the RF ALs regarding the lesion size and showed activation time delay as well as low-voltage areas with bipolar peak-to-peak voltages smaller than 2mV. The time delay of the depolarization front comparing the activation times anterior and posterior to the RF AL was up to 51.5 ms. In a circular area with 5mm radius around an RF AL the mean peak-to-peak voltage decreased by 62-94% to about 0.12-0.44mV and the mean maximal absolute EGM derivative was reduced by 65-96 %. Comparing the results of this study with EGMs of similar clinical settings confirmed our expectations regarding the low-voltage areas caused by the ablation procedure. An improved understanding of the electrophysiological changes is of fundamental importance to provide more information for enhanced RF ablation assessment.
The novel high-density mapping system RhythmiaTM Medical (Boston Scientific, Marlborough, USA) allows a fast and automatic acquisition of intracardiac electrograms (EGMs). For recording the ORION mini-basket catheter is used. Due to the small electrode surface, the spatial averaging is smaller than with other commonly used mapping catheters. This results in a higher quality of unipolar signals. However, these are still corrupted by noise such as high frequency interference. Within this project, methods were developed and benchmarked that can be applied to detect and remove these undesired components. An algorithm was implemented to detect and eliminate artificial peaks in the spectrum of the EGM. The filtered signals showed improved quality in time domain. The performance of the spectral peak detection resulted in a median sensitivity of 92.1% and in a median positive predictive value of 91.9%.
G. Lenis, A. Kramlich, T. Oesterlein, A. Luik, C. Schmitt, and O. Dössel. Development and Benchmarking of Activity Detection Algorithms for Intracardiac Electrograms Measured During Atrial Flutter. In Workshop Biosignal 2016. Innovation bei der Erfassung und Analyse bioelektrischer und bimagnetischer Signale, pp. 5-8, 2016
Catheter ablation has become a very efficient strategy to terminate sustained cardiac arrhythmias like atrial flutter (AFlut). Identification of the optimal ablation spot, however, often proves difficult when scar from previous ablations is present. Although the application of electro-anatomical mapping systems allows to record thousands of intracardiac electrograms (EGMs) from each atrium, state-of-the-art techniques provide limited options for automatic signal processing. Goal of the presented research was the development of an algorithm to detect EGMs that present double potentials (DPs), as these often indicate functional or anatomical lines of block for cardiac excitation. Using an annotated database, we developed several features based on the morphological descriptors of DPs. These were used to train a binary decision tree which was able to detect DPs with a correct rate of over 90%.
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.
Atrial arrhythmia is the most common cardiac arrhythmia. Parameters such as conduction velocity (CV), CV restitution etc. are under analysis in order to understand the cardiac arrhythmias. A number of methods have been proposed for CV calculation in simulation as well as clinical environments. Regional CV gives the information about the magnitude and direction of the propagating depolarization wavefronts on the atrium with homogeneous and heterogeneous tissue. The CV in different regions can provide important quantitative electrophysiological information about the underlying tissue. In this work the regional CV has been calculated using simulated local activation times (LAT) on clinical atrial geometries. Regions with homogeneous and heterogeneous propagation were manually selected for LAT simulation and later the regional CV has been calculated. The calculated CV for both the homogeneous and heterogeneous cases for all the clinical cases have been visualized on the atrial geometries. The visualization of the CV on the atrium represents insight into the regional behavior of the atrial substrate. The benefit of the region-specific study in clinical context is that it could enable the localization of critical sites in the patient specific atrial anatomies. Thus, this could aid physicians in cardiac therapies.
B. Verma, T. G. Oesterlein, A. Luik, C. Schmitt, and O. Dössel. Combined analysis of unipolar and bipolar electrograms for local activation time annotation near the stimulus site of paced rhythms.. In Dreilandertagung Swiss, Austrian, and German society of Biomedical Engineering, 2016
B. Verma, T. Oesterlein, A. Luik, C. Schmitt, and O. Dössel. Combined analysis of unipolar and bipolar electrograms for local activation time annotation near the stimulus site of paced rhythms. In Current Directions in Biomedical Engineering, vol. 2, 2016
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.
T. Oesterlein, G. Lenis, A. Luik, C. Schmitt, and O. Dössel. Optimized Approach for the Detection of Active Segments in Intracardiac Electrograms Measured during Atrial Flutter. In 42nd International Congress on Electrocardiology Conference Book of Abstracts, 2015
Diagnosis of atrial flutter caused by ablation of atrial fibrillation is complex due to ablation scars. This paper presents an approach to replicate the clinically measured flutter circuit in a dynamic computer model. In a first step, important anatomical features of the flutter circuit are extracted manually based on the clinical measurement. With the help of this information, the electrical excitation propagation is simulated on the atrial geometry using the fast marching method. The simulated flutter circuit is used to estimate the global and local conduction velocity by approximating it iteratively. The parameterized flutter simulation is supposed to support the physician during diagnosis and treatment of atrial flutter.
The success rate of the cardiac ablation procedure to cure atrial fibrillation is moderate and depends on the experience and expertise of the physicians. It could be increased by precisely locating arrhythmogenic substrates. The aim of this work is to present a simple and feasible method to analyze intraatrial electrograms to identify the arrhythmogenic substrate on the atrium, under sinus rhythm and pacing sequences. The change in the depolarization wavefront propagation, resulting from consecutive triggering at a point in the coronary sinus (CS), can be an indication of the arrhythmogenic substrate. The region specific study enables the localization of critical sites in the patient specific atrial anatomy. This could aid the physicians in ascertaining the efficacy of cardiac therapies. In this work the point- to-point analysis of the intraatrial electrograms was carried out.
Catheter ablation is the most widely used minimum invasive technique to cure atrial arrhythmias. However, the success rate of the treatment is still moderate and depends on the experience and expertise of the physicians. The aim of this work is to present a simple and feasible method to identify the arrhythmogenic areas on the atrium based on the duration of atrial activities in the intraatrial electrograms. Depolarization waves are created by giving pacing impulses from coronary sinus (CS). The duration of the activity triggered from sinus node (SN) and pacing sequences are analysed by calculating the duration of the activity to mark regions with long atrial activitywaves. The intraatrial electrograms have been analysed on the basis of temporal and spatial information. The region specific study may favour the localization of the critical sites in the patient specific atrial anatomy and aid the physician in ascertaining the efficacy of the cardiac therapies. The identification of suitable markers for critical patterns of the depolarization waves may be crucial to guide an effective ablation treatment. In this work a novel study for point-to-point analysis of the intraatrial electrograms was carried out.
T. Oesterlein, G. Lenis, A. Luik, C. Schmitt, and O. Dössel. Periodic component analysis to eliminate ventricular far field artifacts in unipolar atrial electrograms of patients suffering from atrial flutter. In Biomedizinische Technik / Biomedical Engineering, vol. 59(s1) , pp. 14, 2014
T. Oesterlein, G. Lenis, A. Luik, B. Verma, C. Schmitt, and O. Dössel. Removing ventricular far field artifacts in intracardiac electrograms during stable atrial flutter using the periodic component analysis proof of concept study. In Proceedings 41th International Congress on Electrocardiology, pp. 49--52, 2014
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 (􏰋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 􏰋CA instead of PCA (0.98 vs. 0.90, p<0.001). Its benefit for diagnosis is demonstrated on clinical data.
T. Oesterlein, A. Luik, C. Schmitt, and O. Dössel. Neue Möglichkeiten zur Diagnose von Arrhythmien durch Visualisierung der zeitlichen Dynamik von Elektrogrammen. In Deutsche Gesellschaft für Kardiologie 80. Jahrestagung Mannheim, vol. 103(Suppl 1) , pp. V167, 2014
F. Schenkel, T. Oesterlein, A. Luik, C. Schmitt, and O. Dössel. Detection and classification of atrial excitation patterns in intracardiac electrograms with application on biatrial basket catheter measurements. In Biomedizinische Technik / Biomedical Engineering, vol. 59(s1) , pp. 166-169, 2014
Atrial fibrillation is the most frequent cardiac arrhythmia and often shows a progressive development. An important source of supraventricular extrasystoles triggering paroxysmal atrial fibrillation are the pulmonary veins. Electrograms recorded using an intracardiac catheter can help to improve the classification and quantification of the different atrial excitations. This study presents a framework to recognize and quantify different atrial excitation patterns and to merge them into groups using a clustering method on the basis of the local activation time. The resulting templates can be annotated by physicians and used as a training data set for a classifier to allocate following data. On the basis of the classification result statistics about the origin and occurrence rate of the different excitation patterns could be provided.
Complex fractionated atrial electrograms (CFAE) are a target for catheter ablation as they coincide with areas of slow conduction. In this study we simulated different vol- ume fractions of diffuse and patchy fibrosis up to 50 %. Catheter signals for different electrode spacings were cal- culated and characteristic features were compared to a clinical database of CFAE-signals. A linear slowing of global conduction velocities was found independent of the type of fibrosis. For patchy fibrosis, electrograms displayed fractionation, which was not seen for diffuse fibrosis of the same degree. In comparison to clinical data, simulated electrograms showed up to 10 zero crossings per electro- gram, which was also seen for clinical EGMs with medium fractionation (class 2 of 3). For both, clinical (84 %) and simulated (88 %) signals, a significant difference in ampli- tude is present between fractionated and non-fractionated signals.
Creating transmural ablation scars in a reliable way is a key issue in improvement of therapeutical pro- cedures for cardiac arrhythmias. About one third of the patients has to undergo several procedures till arrhythmic episodes are successfully treated. Morphological features of intracardiac electrograms might contribute to evaluate scar transmurality during the ablation procedure. We an- alyzed intracardiac signals before, during and after point- wise ablation in patients with atrial flutter. Unipolar elec- trograms of the distal electrode showed a relative decrease in amplitude of the second extremum of up to 99 % with a mean of 84±20.6 % after the endpoint of ablation.
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.
Local activation time (LAT) maps help to understand the path of electrical excitation in cardiac arrhythmias. They can be generated automatically from intracardiac electrograms using various criteria provided by commercial electroanatomical mapping systems. This study compares existing criteria and a novel method based on the non-linear energy operator (NLEO) with respect to their precision and robustness.
Simultaneous biatrial electroanatomical mapping was performed in a 54 year old woman using two 64-electrode basket catheters. Local activation time (LAT) maps were extracted retrospectively for single atrial excitations during sinus rhythm using the non-linear energy operator (NLEO). Considering both ampltiude and frequency information, the NLEO has shown to be a reliable estimator for the LAT. This paper presents an approach for creating biatrial LAT maps using the NLEO for single atrial excitations. The varying propagation pattern of individual beats reveals the presence and location of supraventricular extrasystoles.
C. Schilling, A. Luik, C. Schmitt, and O. Dössel. Descriptors for complex fractionated atrial electrograms: A comparison of three different approaches. In Journal of Electrocardiology (Proc. ICE 2010), vol. 44(2) , pp. e31, 2011
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 . 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.
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.
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.
M. W. Keller, C. Schilling, A. Luik, C. Schmitt, and O. Dössel. Descriptors for a classification of complex fractionated atrial electrograms as a guidance for catheter ablation of atrial fibrillation. In Biomedizinische Technik / Biomedical Engineering, vol. 55(s1) , pp. 100-103, 2010
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
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.
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.
C. Schilling, A. Luik, C. Schmitt, and O. Dössel. Analysis of intracardiac ECG measured in the coronary sinus. In 4th European Conference of the International Federation for Medical and Biological Engineering, vol. IFMBE Proceedings(22) , pp. 260-263, 2009
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.
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.