Critical appraisal of technologies to assess electrical activity during atrial fibrillation: a position paper from the European Heart Rhythm Association and European Society of Cardiology Working Group on eCardiology in collaboration with the Heart Rhythm Society, Asia Pacific Heart Rhythm Society, Latin American Heart Rhythm Society and Computing in Cardiology

We aim to provide a critical appraisal of basic concepts underlying signal recording and processing technologies applied for (i) atrial fibrillation (AF) mapping to unravel AF mechanisms and/or identifying target sites for AF therapy and (ii) AF detection, to optimize usage of technologies, stimulate research aimed at closing knowledge gaps, and developing ideal AF recording and processing technologies. Recording and processing techniques for assessment of electrical activity during AF essential for diagnosis and guiding ablative therapy including body surface electrocardiograms (ECG) and endo- or epicardial electrograms (EGM) are evaluated. Discussion of (i) differences in uni-, bi-, and multi-polar (omnipolar/Laplacian) recording modes, (ii) impact of recording technologies on EGM morphology, (iii) global or local mapping using various types of EGM involving signal processing techniques including isochronal-, voltage- fractionation-, dipole density-, and rotor mapping, enabling derivation of parameters like atrial rate, entropy, conduction velocity/direction, (iv) value of epicardial and optical mapping, (v) AF detection by cardiac implantable electronic devices containing various detection algorithms applicable to stored EGMs, (vi) contribution of machine learning (ML) to further improvement of signals processing technologies. Recording and processing of EGM (or ECG) are the cornerstones of (body surface) mapping of AF. Currently available AF recording and processing technologies are mainly restricted to specific applications or have technological limitations. Improvements in AF mapping by obtaining highest fidelity source signals (e.g. catheter–electrode combinations) for signal processing (e.g. filtering, digitization, and noise elimination) is of utmost importance. Novel acquisition instruments (multi-polar catheters combined with improved physical modelling and ML techniques) will enable enhanced and automated interpretation of EGM recordings in the near future

Spatiotemporal characteristics of atrial fibrillation electrograms: a novel marker for arrhythmia stability and termination

Background: Sequentially mapped complex fractionated atrial electrograms (CFAE) and dominant frequency (DF) sites have been targeted during catheter ablation for atrial fibrillation (AF). However, these strategies have yielded variable success and have not been shown to correlate consistently with AF dynamics. Here, we evaluated whether the spatiotemporal stability of CFAE and DF may be a better marker of AF sustenance and termination. Methods: Eighteen sheep with 12 weeks of "one-kidney, one-clip" hypertension underwent open-chest studies. A total of 42 self-terminating (28–100 s) and 6 sustained (>15 min) AF episodes were mapped using a custom epicardial plaque and analyzed in 4-s epochs for CFAE, using the NavX CFE-m algorithm, and DF, using a Fast Fourier Transform. The spatiotemporal stability index (STSI) was calculated using the intraclass correlation coefficient of consecutive AF epochs. Results: A total of 67,733 AF epochs were analyzed. During AF initiation, mean CFE-m and the STSI of CFE-m/DF were similar between sustained and self-terminating episodes, although median DF was higher in sustained AF (p=0.001). During sustained AF, the STSI of CFE-m increased significantly (p=0.02), whereas mean CFE-m (p=0.5), median DF (p=0.07), and the STSI of DF remained unchanged (p=0.5). Prior to AF termination, the STSI of CFE-m was significantly lower (p<0.001), with a physiologically non-significant decrease in median DF (−0.3 Hz, p=0.006) and no significant changes in mean CFE-m (p=0.14) or the STSI of DF (p=0.06). Conclusions: Spatiotemporal stabilization of CFAE favors AF sustenance and its destabilization heralds AF termination. The STSI of CFE-m is more representative of AF dynamics than are the STSI of DF, sequential mean CFE-m, or median DF

Characterization of complex fractionated atrial electrograms by sample entropy: An international multi-center study

Atrial fibrillation (AF) is the most commonly clinically-encountered arrhythmia. Catheter ablation of AF is mainly based on trigger elimination and modification of the AF substrate. Substrate mapping ablation of complex fractionated atrial electrograms (CFAEs) has emerged to be a promising technique. To improve substrate mapping based on CFAE analysis, automatic detection algorithms need to be developed in order to simplify and accelerate the ablation procedures. According to the latest studies, the level of fractionation has been shown to be promisingly well estimated from CFAE measured during radio frequency (RF) ablation of AF. The nature of CFAE is generally nonlinear and nonstationary, so the use of complexity measures is considered to be the appropriate technique for the analysis of AF records. This work proposes the use of sample entropy (SampEn), not only as a way to discern between non-fractionated and fractionated atrial electrograms (A-EGM), but also as a tool for characterizing the degree of A-EGM regularity, which is linked to changes in the AF substrate and to heart tissue damage. The use of SampEn combined with a blind parameter estimation optimization process enables the classification between CFAE and non-CFAE with statistical significance (p < 0:001), 0.89 area under the ROC, 86% specificity and 77% sensitivity over a mixed database of A-EGM combined from two independent CFAE signal databases, recorded during RF ablation of AF in two EU countries (542 signals in total). On the basis of the results obtained in this study, it can be suggested that the use of SampEn is suitable for real-time support during navigation of RF ablation of AF, as only 1.5 seconds of signal segments need to be analyzed

Complexity of Atrial Fibrillation Electrograms Through Nonlinear Signal Analysis: In Silico Approach

Identification of atrial fibrillation (AF) mechanisms could improve the rate of ablation success. However, the incomplete understanding of those mechanisms makes difficult the decision of targeting sites for ablation. This work is focused on the importance of EGM analysis for detecting and modulating rotors to guide ablation procedures and improve its outcomes. Virtual atrial models are used to show how nonlinear measures can be used to generate electroanatomical maps to detect critical sites in AF. A description of the atrial cell mathematical models, and the procedure of coupling them within two‐dimensional and three‐dimensional virtual atrial models in order to simulate arrhythmogenic mechanisms, is given. Mathematical modeling of unipolar and bipolar electrogramas (EGM) is introduced. It follows a discussion of EGM signal processing. Nonlinear descriptors, such as approximate entropy and multifractal analysis, are used to study the dynamical behavior of EGM signals, which are not well described by a linear law. Our results evince that nonlinear analysis of EGM can provide information about the dynamics of rotors and other mechanisms of AF. Furthermore, these fibrillatory patterns can be simulated using virtual models. The combination of features using machine learning tools can be used for identifying arrhythmogenic sources of AF

Short-Time Estimation of Fractionation in Atrial Fibrillation with Coarse-Grained Correlation Dimension for Mapping the Atrial Substrate

[EN] Atrial ¿brillation (AF) is currently the most common cardiac arrhythmia, with catheter ablation (CA) of the pulmonary veins (PV) being its ¿rst line therapy. Ablation of complex fractionated atrial electrograms (CFAEs) outside the PVs has demonstrated improved long-term results, but their identi¿cation requires a reliable electrogram (EGM) fractionation estimator. This study proposes a technique aimed to assist CA procedures under real-time settings. The method has been tested on three groups of recordings: Group 1 consisted of 24 highly representative EGMs, eight of each belonging to a different AF Type. Group 2 contained the entire dataset of 119 EGMs, whereas Group 3 contained 20 pseudo-real EGMs of the special Type IV AF. Coarse-grained correlation dimension (CGCD) was computed at epochs of 1 s duration, obtaining a classi¿cation accuracy of 100% in Group 1 and 84.0¿85.7% in Group 2, using 10-fold cross-validation. The receiver operating characteristics (ROC) analysis for highly fractionated EGMs, showed 100% speci¿city and sensitivity in Group 1 and 87.5% speci¿city and 93.6% sensitivity in Group 2. In addition, 100% of the pseudo-real EGMs were correctly identi¿ed as Type IV AF. This method can consistently express the fractionation level of AF EGMs and provides better performance than previous works. Its ability to compute fractionation in short-time can agilely detect sudden changes of AF Types and could be used for mapping the atrial substrate, thus assisting CA procedures under real-time settings for atrial substrate modi¿cation.This research has been supported by grants DPI2017-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from JCCM and AICO/2019/036 from GVA.Vraka, A.; Hornero, F.; Bertomeu-Gonzalez, V.; Osca, J.; Alcaraz, R.; Rieta, JJ. (2020). Short-Time Estimation of Fractionation in Atrial Fibrillation with Coarse-Grained Correlation Dimension for Mapping the Atrial Substrate. Entropy. 22(2):1-20. https://doi.org/10.3390/e22020232S120222Go, A. S., Hylek, E. M., Phillips, K. A., Chang, Y., Henault, L. E., Selby, J. V., & Singer, D. E. (2001). Prevalence of Diagnosed Atrial Fibrillation in Adults. JAMA, 285(18), 2370. doi:10.1001/jama.285.18.2370Goette, A., Honeycutt, C., & Langberg, J. J. (1996). Electrical Remodeling in Atrial Fibrillation. Circulation, 94(11), 2968-2974. doi:10.1161/01.cir.94.11.2968Chugh, S. S., Roth, G. A., Gillum, R. F., & Mensah, G. A. (2014). Global Burden of Atrial Fibrillation in Developed and Developing Nations. Global Heart, 9(1), 113. doi:10.1016/j.gheart.2014.01.004Cappato, R., Calkins, H., Chen, S.-A., Davies, W., Iesaka, Y., Kalman, J., … Biganzoli, E. (2010). Updated Worldwide Survey on the Methods, Efficacy, and Safety of Catheter Ablation for Human Atrial Fibrillation. Circulation: Arrhythmia and Electrophysiology, 3(1), 32-38. doi:10.1161/circep.109.859116Cox, J. L., Canavan, T. E., Schuessler, R. B., Cain, M. E., Lindsay, B. D., Stone, C., … Boineau, J. P. (1991). The surgical treatment of atrial fibrillation. The Journal of Thoracic and Cardiovascular Surgery, 101(3), 406-426. doi:10.1016/s0022-5223(19)36723-6Haïssaguerre, M., Jaïs, P., Shah, D. C., Takahashi, A., Hocini, M., Quiniou, G., … Clémenty, J. (1998). Spontaneous Initiation of Atrial Fibrillation by Ectopic Beats Originating in the Pulmonary Veins. New England Journal of Medicine, 339(10), 659-666. doi:10.1056/nejm199809033391003Kornej, J., Schumacher, K., Zeynalova, S., Sommer, P., Arya, A., Weiß, M., … Hindricks, G. (2019). Time-dependent prediction of arrhythmia recurrences during long-term follow-up in patients undergoing catheter ablation of atrial fibrillation: The Leipzig Heart Center AF Ablation Registry. Scientific Reports, 9(1). doi:10.1038/s41598-019-43644-2YOSHIDA, K., ULFARSSON, M., TADA, H., CHUGH, A., GOOD, E., KUHNE, M., … ORAL, H. (2008). Complex Electrograms Within the Coronary Sinus: Time- and Frequency-Domain Characteristics, Effects of Antral Pulmonary Vein Isolation, and Relationship to Clinical Outcome in Patients with Paroxysmal and Persistent Atrial Fibrillation. Journal of Cardiovascular Electrophysiology, 19(10), 1017-1023. doi:10.1111/j.1540-8167.2008.01175.xKonings, K. T., Kirchhof, C. J., Smeets, J. R., Wellens, H. J., Penn, O. C., & Allessie, M. A. (1994). High-density mapping of electrically induced atrial fibrillation in humans. Circulation, 89(4), 1665-1680. doi:10.1161/01.cir.89.4.1665Rolf, S., Kircher, S., Arya, A., Eitel, C., Sommer, P., Richter, S., … Piorkowski, C. (2014). Tailored Atrial Substrate Modification Based on Low-Voltage Areas in Catheter Ablation of Atrial Fibrillation. Circulation: Arrhythmia and Electrophysiology, 7(5), 825-833. doi:10.1161/circep.113.001251Takahashi, Y., O’Neill, M. D., Hocini, M., Dubois, R., Matsuo, S., Knecht, S., … Haïssaguerre, M. (2008). Characterization of Electrograms Associated With Termination of Chronic Atrial Fibrillation by Catheter Ablation. Journal of the American College of Cardiology, 51(10), 1003-1010. doi:10.1016/j.jacc.2007.10.056Atienza, F., Almendral, J., Jalife, J., Zlochiver, S., Ploutz-Snyder, R., Torrecilla, E. G., … Berenfeld, O. (2009). Real-time dominant frequency mapping and ablation of dominant frequency sites in atrial fibrillation with left-to-right frequency gradients predicts long-term maintenance of sinus rhythm. Heart Rhythm, 6(1), 33-40. doi:10.1016/j.hrthm.2008.10.024Nademanee, K., McKenzie, J., Kosar, E., Schwab, M., Sunsaneewitayakul, B., Vasavakul, T., … Ngarmukos, T. (2004). A new approach for catheter ablation of atrial fibrillation: mapping of the electrophysiologic substrate. Journal of the American College of Cardiology, 43(11), 2044-2053. doi:10.1016/j.jacc.2003.12.054Ciaccio, E. J., Biviano, A. B., Whang, W., Coromilas, J., & Garan, H. (2011). A new transform for the analysis of complex fractionated atrial electrograms. BioMedical Engineering OnLine, 10(1), 35. doi:10.1186/1475-925x-10-35Ciaccio, E. J., Biviano, A. B., & Garan, H. (2013). Computational method for high resolution spectral analysis of fractionated atrial electrograms. Computers in Biology and Medicine, 43(10), 1573-1582. doi:10.1016/j.compbiomed.2013.07.033TSAI, W.-C., LIN, Y.-J., TSAO, H.-M., CHANG, S.-L., LO, L.-W., HU, Y.-F., … CHEN, S.-A. (2010). The Optimal Automatic Algorithm for the Mapping of Complex Fractionated Atrial Electrograms in Patients With Atrial Fibrillation. Journal of Cardiovascular Electrophysiology, 21(1), 21-26. doi:10.1111/j.1540-8167.2009.01567.xTeh, A. W., Kistler, P. M., Lee, G., Medi, C., Heck, P. M., Spence, S. J., … Kalman, J. M. (2011). The relationship between complex fractionated electrograms and atrial low-voltage zones during atrial fibrillation and paced rhythm. Europace, 13(12), 1709-1716. doi:10.1093/europace/eur197Lin, Y.-J., Lo, M.-T., Chang, S.-L., Lo, L.-W., Hu, Y.-F., Chao, T.-F., … Chen, S.-A. (2016). Benefits of Atrial Substrate Modification Guided by Electrogram Similarity and Phase Mapping Techniques to Eliminate Rotors and Focal Sources Versus Conventional Defragmentation in Persistent Atrial Fibrillation. JACC: Clinical Electrophysiology, 2(6), 667-678. doi:10.1016/j.jacep.2016.08.005Verma, A., Jiang, C., Betts, T. R., Chen, J., Deisenhofer, I., Mantovan, R., … Sanders, P. (2015). Approaches to Catheter Ablation for Persistent Atrial Fibrillation. New England Journal of Medicine, 372(19), 1812-1822. doi:10.1056/nejmoa1408288Ammar-Busch, S., Reents, T., Knecht, S., Rostock, T., Arentz, T., Duytschaever, M., … Deisenhofer, I. (2018). Correlation between atrial fibrillation driver locations and complex fractionated atrial electrograms in patients with persistent atrial fibrillation. Pacing and Clinical Electrophysiology, 41(10), 1279-1285. doi:10.1111/pace.13483Almeida, T. P., Chu, G. S., Salinet, J. L., Vanheusden, F. J., Li, X., Tuan, J. H., … Schlindwein, F. S. (2016). Minimizing discordances in automated classification of fractionated electrograms in human persistent atrial fibrillation. Medical & Biological Engineering & Computing, 54(11), 1695-1706. doi:10.1007/s11517-016-1456-2De Bakker, J. M. T., & Wittkampf, F. H. M. (2010). The Pathophysiologic Basis of Fractionated and Complex Electrograms and the Impact of Recording Techniques on Their Detection and Interpretation. Circulation: Arrhythmia and Electrophysiology, 3(2), 204-213. doi:10.1161/circep.109.904763Luca, A., Buttu, A., Pruvot, E., Pascale, P., Bisch, L., & Vesin, J.-M. (2016). Nonlinear analysis of right atrial electrograms predicts termination of persistent atrial fibrillation within the left atrium by catheter ablation. Physiological Measurement, 37(3), 347-359. doi:10.1088/0967-3334/37/3/347Corana, A., Casaleggio, A., Rolando, C., & Ridella, S. (1991). Efficient computation of the correlation dimension from a time series on a LIW computer. Parallel Computing, 17(6-7), 809-820. doi:10.1016/s0167-8191(05)80068-7Fraser, A. M., & Swinney, H. L. (1986). Independent coordinates for strange attractors from mutual information. Physical Review A, 33(2), 1134-1140. doi:10.1103/physreva.33.1134Martínez-Iniesta, M., Ródenas, J., Alcaraz, R., & Rieta, J. J. (2017). Waveform Integrity in Atrial Fibrillation: The Forgotten Issue of Cardiac Electrophysiology. Annals of Biomedical Engineering, 45(8), 1890-1907. doi:10.1007/s10439-017-1832-6Theiler, J., Eubank, S., Longtin, A., Galdrikian, B., & Doyne Farmer, J. (1992). Testing for nonlinearity in time series: the method of surrogate data. Physica D: Nonlinear Phenomena, 58(1-4), 77-94. doi:10.1016/0167-2789(92)90102-sNakamura, T., Small, M., & Hirata, Y. (2006). Testing for nonlinearity in irregular fluctuations with long-term trends. Physical Review E, 74(2). doi:10.1103/physreve.74.026205SHAPIRO, S. S., & WILK, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3-4), 591-611. doi:10.1093/biomet/52.3-4.591Mandelbrot, B. (1961). Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling (Ingram Olkin, Sudhist G. Ghurye, Wassily Hoeffding, William G. Madow, and Henry B. Mann, eds.). SIAM Review, 3(1), 80-80. doi:10.1137/1003016Mann, H. B., & Whitney, D. R. (1947). On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other. The Annals of Mathematical Statistics, 18(1), 50-60. doi:10.1214/aoms/1177730491Křemen, V., Lhotská, L., Macaš, M., Čihák, R., Vančura, V., Kautzner, J., & Wichterle, D. (2008). A new approach to automated assessment of fractionation of endocardial electrograms during atrial fibrillation. Physiological Measurement, 29(12), 1371-1381. doi:10.1088/0967-3334/29/12/002Haley, C. L., Gula, L. J., Miranda, R., Michael, K. A., Baranchuk, A. M., Simpson, C. S., … Redfearn, D. P. (2012). Validation of a novel algorithm for quantification of the percentage of signal fractionation in atrial fibrillation. EP Europace, 15(3), 447-452. doi:10.1093/europace/eus361Nollo, G., Marconcini, M., Faes, L., Bovolo, F., Ravelli, F., & Bruzzone, L. (2008). An Automatic System for the Analysis and Classification of Human Atrial Fibrillation Patterns from Intracardiac Electrograms. IEEE Transactions on Biomedical Engineering, 55(9), 2275-2285. doi:10.1109/tbme.2008.923155Kirchner, M., Faes, L., Olivetti, E., Riccardi, R., Scaglione, M., Gaita, F., & Antolini, R. (s. f.). Local electrical characterisation of human atrial fibrillation. Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163). doi:10.1109/cic.2000.898567Cirugeda–Roldán, E., Novak, D., Kremen, V., Cuesta–Frau, D., Keller, M., Luik, A., & Srutova, M. (2015). Characterization of Complex Fractionated Atrial Electrograms by Sample Entropy: An International Multi-Center Study. Entropy, 17(12), 7493-7509. doi:10.3390/e17117493Corino, V. D. A., Rivolta, M. W., Sassi, R., Lombardi, F., & Mainardi, L. T. (2013). Ventricular activity cancellation in electrograms during atrial fibrillation with constraints on residuals’ power. Medical Engineering & Physics, 35(12), 1770-1777. doi:10.1016/j.medengphy.2013.07.010Rieta, J. J., Hornero, F., Alcaraz, R., & Moratal, D. (2007). Ventricular Artifacts Cancellation from Atrial Epicardial Recordings in Atrial Tachyarrhythmias. 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. doi:10.1109/iembs.2007.4353849Williams, G. (1997). Chaos Theory Tamed. doi:10.1201/9781482295412Havstad, J. W., & Ehlers, C. L. (1989). Attractor dimension of nonstationary dynamical systems from small data sets. Physical Review A, 39(2), 845-853. doi:10.1103/physreva.39.84

Endocardial activation mapping of human atrial fibrillation

Successful ablation of arrhythmias depends upon interpretation of the mechanism. However, in persistent atrial fibrillation (AF) ablation is currently directed towards the mechanism that initiates paroxysmal AF. We sought to address the hypothesis that atrial activation patterns during persistent AF may help determine the underlying mechanism. Activation mapping of AF wavefronts is labor intensive and often restricted to short time segments in limited atrial locations. RETRO-Mapping was developed to identify uniform wavefronts that occur during AF, and summate all wavefront vectors on to an orbital plot. Uniform wavefronts were mapped using RETRO-Mapping during sinus rhythm, atrial tachycardia, and atrial fibrillation, and validated against detailed manual analysis of the same wavefronts with conventional isochronal mapping. RETRO-Mapping was found to have comparable accuracy to isochronal mapping. RETRO-Mapping was then used to investigate atrial activation patterns during persistent AF. Atrial activation patterns demonstrated evidence of spatiotemporal stability over long time periods. Orbital plots created at different time points in the same location remained unchanged. Together with this important discovery, both fractionation and bipolar voltage were also demonstrated to express stability over time. Spatiotemporal stability during persistent AF enables sequential mapping as an acceptable technique. This property also allowed the development of a method for displaying sequentially mapped locations on a single map – RETRO-Choropleth Map. These findings go against the multiple wavelet hypothesis with random activation. Having gained insights in to these stable activation patterns, extensive analysis was undertaken to identify the presence of focal activation. Focal activations were identified during persistent AF. RETRO-Mapping was used to show that adjacent activation patterns were not related to focal activations. Lastly, the effect of pulmonary vein isolation (PVI) was studied by mapping atrial activation patterns before and after PVI. RETRO-Mapping showed that PVI leads to increased organisation of AF in most patients, supporting a mechanistic role of the pulmonary veins in persistent AF. In conclusion, a new technique has been developed and validated for automated activation mapping of persistent AF. These techniques could be used to guide additional ablation strategies beyond PVI for patients with persistent AF.Open Acces

Automated algorithm-driven methods of localising drivers of persistent atrial fibrillation using atrial fibrillation cycle length and atrial fibrillation voltage

The assessment of atrial fibrillation cycle length has played a role in the development of atrial fibrillation ablation by pulmonary vein isolation (PVI) and has also been used to assess response to ablation. Areas of rapid rotational activity in the left atrium have been implied to act as drivers of persistent atrial fibrillation and several methods have been developed to identify these potential drivers. Unprocessed atrial fibrillation electrograms show large variation in cycle length and signal amplitude. Current methods of localising driver regions rely on complex pattern recognition and subjective assessment of operators. The main hypotheses of this thesis were as follows: 1) a technique can be developed to ascertain a clinically relevant, dominant cycle length for any AF segment, 2) the automated technique, can be used to map rapid and regular activity in the left atrium, 3) a patient-tailored definition of rapid activity and low AF voltage, calculated based on patient-specific parameters is feasible; 4) paired with automated low voltage substrate analysis, dominant cycle length analysis is able to provide a framework for localising drivers of AF that is objective, transparent and requires no complex pattern recognition of subjective judgement. To test the hypotheses, a technique was developed based on manual annotation of real-world AF electrograms that was able to ascertain cycle length independent of missing segments or variable cycle length or signal amplitude. Following this, an automated algorithm was validated to determine dominant cycle length. In the following chapter, the nature of AF cycle length was investigated by investigating the patterns of rapid activity with extended AF segments and the concept of patient-tailored definitions of rapid activity was introduced. In the subsequent analysis, the effect of PVI was examined on AF voltage and the AF cycle length, focusing on rapid and regular areas and low voltage zones, and their changes. The last chapter utilised the accumulated information to test the sensitivity and specificity of a percentile-based, patient-tailored approach to low AF voltage and to present an objective, automated method of localising rapid and regular areas within low voltage zones within the left atrium. In summary, it is feasible to assess and locate rapid and regular areas, and localise low voltage zones in persistent AF with a completely automated algorithm, and patient-tailored definitions of low voltage rapid AF activity are a preferable alternative to absolute cut offs.Open Acces

Characterization of Cardiac Electrogram Signals During Atrial Fibrillation

Atrial fibrillation (AF) is the most common cardiac arrhythmia in United States. The most popular treatment for AF is a percutaneous procedure called catheter ablation. Current AF ablation procedures unfortunately have a poor success rate, primarily because the mechanisms involved in AF are incompletely understood even today. Intra-atrial electrograms have previously been shown to provide information on the mechanisms of AF. This thesis focuses on two such mechanisms – AF-sustaining sites known as sustained rotational activities (RotAs), and atrial tissue with unique electrical properties known as myocardial scars. Catheter ablation procedures today construct the 3D electroanatomic map of the left atrium (LA) by maneuvering a conventional Multipolar Diagnostic Catheter (MPDC) along the LA endocardial surface. These procedures are limited to pulmonary vein isolation and other linear ablation performed on various regions of the left atrium (such as roof and mitral isthmus) where the regions are decided based on the atrial anatomy. However, it remains unclear how to utilize the information provided by the MPDC to analyze and characterize the RotAs and scars. Previous electrogram characterization studies mainly use a single bipole rather than MPDCs to characterize the electrograms based on features such as cycle length or dominant frequency from the time or frequency domain. In this thesis we developed novel techniques for investigating the above mentioned mechanisms using signal analysis, mathematical modeling, numerical simulation and clinical experiments, all utilizing MPDC recordings. First, the variations in the total conduction delay (TCD) from MPDC electrograms as the MPDC moves towards a RotA source was investigated. Second, the maximum peak-to-peak amplitudes of MPDC electrograms recorded during AF and NSR were analyzed. This thesis provides insights into methods of characterization of cardiac electrograms and the findings of this thesis could address the current challenges in AF ablation