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Zeemering S, van Hunnik A, van Rosmalen F, Bonizzi P, Scaf B, Delhaas T, Verheule S, Schotten U. A Novel Tool for the Identification and Characterization of Repetitive Patterns in High-Density Contact Mapping of Atrial Fibrillation. Front Physiol 2020; 11:570118. [PMID: 33178041 PMCID: PMC7593698 DOI: 10.3389/fphys.2020.570118] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Accepted: 09/22/2020] [Indexed: 01/19/2023] Open
Abstract
Introduction Electrical contact mapping provides a detailed view of conduction patterns in the atria during atrial fibrillation (AF). Identification of repetitive wave front propagation mechanisms potentially initiating or sustaining AF might provide more insights into temporal and spatial distribution of candidate AF mechanism and identify targets for catheter ablation. We developed a novel tool based on recurrence plots to automatically identify and characterize repetitive conduction patterns in high-density contact mapping of AF. Materials and Methods Recurrence plots were constructed by first transforming atrial electrograms recorded by a multi-electrode array to activation-phase signals and then quantifying the degree of similarity between snapshots of the activation-phase in the electrode array. An AF cycle length dependent distance threshold was applied to discriminate between repetitive and non-repetitive snapshots. Intervals containing repetitive conduction patterns were detected in a recurrence plot as regions with a high recurrence rate. Intervals that contained similar repetitive patterns were then grouped into clusters. To demonstrate the ability to detect and quantify the incidence, duration and size of repetitive patterns, the tool was applied to left and right atrial recordings in a goat model of different duration of persistent AF [3 weeks AF (3 wkAF, n = 8) and 22 weeks AF (22 wkAF, n = 8)], using a 249-electrode mapping array (2.4 mm inter-electrode distance). Results Recurrence plots identified frequent recurrences of activation patterns in all recordings and indicated a strong correlation between recurrence plot threshold and AF cycle length. Prolonged AF duration was associated with shorter repetitive pattern duration [mean maximum duration 3 wkAF: 74 cycles, 95% confidence interval (54-94) vs. 22 wkAF: 41 cycles (21-62), p = 0.03], and smaller recurrent regions within repetitive patterns [3 wkAF 1.7 cm2 (1.0-2.3) vs. 22 wkAF 0.5 cm2 (0.0-1.2), p = 0.02]. Both breakthrough patterns and re-entry were identified as repetitive conduction patterns. Conclusion Recurrence plots provide a novel way to delineate high-density contact mapping of AF. Dominant repetitive conduction patterns were identified in a goat model of sustained AF. Application of the developed methodology using the new generation of multi-electrode catheters could identify additional targets for catheter ablation of AF.
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Affiliation(s)
- Stef Zeemering
- Department of Physiology, Maastricht University Medical Center, Cardiovascular Research Institute Maastricht, Maastricht, Netherlands
| | - Arne van Hunnik
- Department of Physiology, Maastricht University Medical Center, Cardiovascular Research Institute Maastricht, Maastricht, Netherlands
| | - Frank van Rosmalen
- Department of Biomedical Engineering, Maastricht University Medical Center, Cardiovascular Research Institute Maastricht, Maastricht, Netherlands
| | - Pietro Bonizzi
- Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, Netherlands
| | - Billy Scaf
- Department of Physiology, Maastricht University Medical Center, Cardiovascular Research Institute Maastricht, Maastricht, Netherlands
| | - Tammo Delhaas
- Department of Biomedical Engineering, Maastricht University Medical Center, Cardiovascular Research Institute Maastricht, Maastricht, Netherlands
| | - Sander Verheule
- Department of Physiology, Maastricht University Medical Center, Cardiovascular Research Institute Maastricht, Maastricht, Netherlands
| | - Ulrich Schotten
- Department of Physiology, Maastricht University Medical Center, Cardiovascular Research Institute Maastricht, Maastricht, Netherlands
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Cluitmans M, Brooks DH, MacLeod R, Dössel O, Guillem MS, van Dam PM, Svehlikova J, He B, Sapp J, Wang L, Bear L. Validation and Opportunities of Electrocardiographic Imaging: From Technical Achievements to Clinical Applications. Front Physiol 2018; 9:1305. [PMID: 30294281 PMCID: PMC6158556 DOI: 10.3389/fphys.2018.01305] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2018] [Accepted: 08/29/2018] [Indexed: 11/23/2022] Open
Abstract
Electrocardiographic imaging (ECGI) reconstructs the electrical activity of the heart from a dense array of body-surface electrocardiograms and a patient-specific heart-torso geometry. Depending on how it is formulated, ECGI allows the reconstruction of the activation and recovery sequence of the heart, the origin of premature beats or tachycardia, the anchors/hotspots of re-entrant arrhythmias and other electrophysiological quantities of interest. Importantly, these quantities are directly and non-invasively reconstructed in a digitized model of the patient's three-dimensional heart, which has led to clinical interest in ECGI's ability to personalize diagnosis and guide therapy. Despite considerable development over the last decades, validation of ECGI is challenging. Firstly, results depend considerably on implementation choices, which are necessary to deal with ECGI's ill-posed character. Secondly, it is challenging to obtain (invasive) ground truth data of high quality. In this review, we discuss the current status of ECGI validation as well as the major challenges remaining for complete adoption of ECGI in clinical practice. Specifically, showing clinical benefit is essential for the adoption of ECGI. Such benefit may lie in patient outcome improvement, workflow improvement, or cost reduction. Future studies should focus on these aspects to achieve broad adoption of ECGI, but only after the technical challenges have been solved for that specific application/pathology. We propose 'best' practices for technical validation and highlight collaborative efforts recently organized in this field. Continued interaction between engineers, basic scientists, and physicians remains essential to find a hybrid between technical achievements, pathological mechanisms insights, and clinical benefit, to evolve this powerful technique toward a useful role in clinical practice.
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Affiliation(s)
- Matthijs Cluitmans
- Department of Cardiology, Cardiovascular Research Institute Maastricht Maastricht University, Maastricht, Netherlands
| | - Dana H. Brooks
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States
| | - Rob MacLeod
- Biomedical Engineering Department, Scientific Computing and Imaging Institute (SCI), and Cardiovascular Research and Training Institute (CVRTI), The University of Utah, Salt Lake City, UT, United States
| | - Olaf Dössel
- Karlsruhe Institute of Technology, Karlsruhe, Germany
| | | | - Peter M. van Dam
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Center, Nijmegen, Netherlands
| | - Jana Svehlikova
- Institute of Measurement Science, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Bin He
- Department of Biomedical Engineering Carnegie Mellon University, Pittsburgh, PA, United States
| | - John Sapp
- QEII Health Sciences Centre and Department of Medicine, Dalhousie University, Halifax, NS, Canada
| | - Linwei Wang
- Rochester Institute of Technology, Rochester, NY, United States
| | - Laura Bear
- IHU LIRYC, Fondation Bordeaux Université, Inserm U1045 and Université de Bordeaux, Bordeaux, France
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Sánchez C, Bueno-Orovio A, Pueyo E, Rodríguez B. Atrial Fibrillation Dynamics and Ionic Block Effects in Six Heterogeneous Human 3D Virtual Atria with Distinct Repolarization Dynamics. Front Bioeng Biotechnol 2017; 5:29. [PMID: 28534025 PMCID: PMC5420585 DOI: 10.3389/fbioe.2017.00029] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Accepted: 04/18/2017] [Indexed: 12/19/2022] Open
Abstract
Atrial fibrillation (AF) usually manifests as reentrant circuits propagating through the whole atria creating chaotic activation patterns. Little is yet known about how differences in electrophysiological and ionic properties between patients modulate reentrant patterns in AF. The goal of this study is to quantify how variability in action potential duration (APD) at different stages of repolarization determines AF dynamics and their modulation by ionic block using a set of virtual whole-atria human models. Six human whole-atria models are constructed based on the same anatomical structure and fiber orientation, but with different electrophysiological phenotypes. Membrane kinetics for each whole-atria model are selected with distinct APD characteristics at 20, 50, and 90% repolarization, from an experimentally calibrated population of human atrial action potential models, including AF remodeling and acetylcholine parasympathetic effects. Our simulations show that in all whole-atria models, reentrant circuits tend to organize around the pulmonary veins and the right atrial appendage, thus leading to higher dominant frequency (DF) and more organized activation in the left atrium than in the right atrium. Differences in APD in all phases of repolarization (not only APD90) yielded quantitative differences in fibrillation patterns with long APD associated with slower and more regular dynamics. Long APD50 and APD20 were associated with increased interatrial conduction block and interatrial differences in DF and organization index, creating reentry instability and self-termination in some cases. Specific inhibitions of IK1, INaK, or INa reduce DF and organization of the arrhythmia by enlarging wave meandering, reducing the number of secondary wavelets, and promoting interatrial block in all six virtual patients, especially for the phenotypes with short APD at 20, 50, and/or 90% repolarization. This suggests that therapies aiming at prolonging the early phase of repolarization might constitute effective antiarrhythmic strategies for the pharmacological management of AF. In summary, simulations report significant differences in atrial fibrillatory dynamics resulting from differences in APD at all phases of repolarization.
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Affiliation(s)
- Carlos Sánchez
- Biosignal Interpretation and Computational Simulation (BSICoS), I3A and IIS, University of Zaragoza, Zaragoza, Spain.,Defense University Centre (CUD), General Military Academy of Zaragoza (AGM), Zaragoza, Spain
| | | | - Esther Pueyo
- Biosignal Interpretation and Computational Simulation (BSICoS), I3A and IIS, University of Zaragoza, Zaragoza, Spain.,Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Zaragoza, Spain
| | - Blanca Rodríguez
- Department of Computer Science, University of Oxford, Oxford, UK
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Gordon D, Goldberger JJ, Arora R, Aistrup GL, Ng J. Searching for "order" in atrial fibrillation using electrogram morphology recurrence plots. Comput Biol Med 2015; 65:220-8. [PMID: 26255963 DOI: 10.1016/j.compbiomed.2015.07.018] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2015] [Revised: 07/01/2015] [Accepted: 07/20/2015] [Indexed: 11/25/2022]
Abstract
BACKGROUND Bipolar electrograms recorded during atrial fibrillation (AF) can have an appearance of chaotic/random behavior. The aim of this study was to use a novel electrogram morphology recurrence (EMR) analysis to quantify the level of order in the morphology patterns in AF. METHODS Rapid atrial pacing was performed in seven dogs at 600bpm for 3 weeks leading to sustained AF. Open chest high density electrical recordings were made in multiple atrial sites. EMR plots of bipolar electrograms at each site were created by cross-correlating morphologies of each detected activations with morphologies of every other activation. The following features of the EMR plots were quantified: recurrence rate (RR), determinism (DET), laminarity (LAM), average diagonal line length (L), trapping time (TT), divergence (DIV), and Shannon׳s entropy (ENTR). For each recording site, these measures were calculated for the normal sequence of morphologies and also after random shuffling of the electrogram orders. RESULTS Electrograms recordings from a total of 3961 sites had average cycle lengths of 104±22ms resulting in an average of 100±19 activations detected per 10-s recording and an average RR of 0.38±0.28 (range 0.02-1.00). Shuffling the order of the activation morphologies resulted in significant decreases in DET, LAM, L, TT, and ENTR and significant increases in DIV. CONCLUSIONS EMR plots of AF electrograms show varying rates of recurrence with patterns that suggest an underlying deterministic structure to the activation sequences. A better understanding of AF dynamics could lead to improved methods in mapping and treating AF.
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Affiliation(s)
- David Gordon
- Feinberg Cardiovascular Research Institute, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
| | - Jeffrey J Goldberger
- Feinberg Cardiovascular Research Institute, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
| | - Rishi Arora
- Feinberg Cardiovascular Research Institute, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
| | - Gary L Aistrup
- Feinberg Cardiovascular Research Institute, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
| | - Jason Ng
- Feinberg Cardiovascular Research Institute, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
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Ravelli F, Masè M. Computational mapping in atrial fibrillation: how the integration of signal-derived maps may guide the localization of critical sources. ACTA ACUST UNITED AC 2014; 16:714-23. [DOI: 10.1093/europace/eut376] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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Pagana G, Galleani L, Gross S, Roch MR, Pastore E, Poggio M, Quaranta G. Time-frequency analysis of the endocavitarian signal in paroxysmal atrial fibrillation. IEEE Trans Biomed Eng 2012; 59:2838-44. [PMID: 22875240 DOI: 10.1109/tbme.2012.2211596] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
We apply the time-frequency analysis to the endocavitarian signal of patients suffering from paroxysmal atrial fibrillation. The time-frequency spectrum reveals the components of the endocavitarian signal. These components are located in the regions of the time-frequency domain that differ for in-rhythm and in-atrial fibrillation signals. By using experimental data, we perform a statistical study of these regions, and we obtain their average value. The difference in the shape of these regions is caused by the re-entry circuits that characterize atrial fibrillation. We propose a propagation model for atrial fibrillation based on the re-entry circuits, which explains the shape of the time-frequency spectrum.
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Richter U, Faes L, Ravelli F, Sornmo L. Propagation Pattern Analysis During Atrial Fibrillation Based on Sparse Modeling. IEEE Trans Biomed Eng 2012; 59:1319-28. [DOI: 10.1109/tbme.2012.2187054] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Ravelli F, Masè M. A time-domain approach for the identification of atrial fibrillation drivers. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2011; 2011:5527-5530. [PMID: 22255590 DOI: 10.1109/iembs.2011.6091410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
The localization of atrial fibrillation (AF) driver sources, characterized by rapid and regular electrical activity, is crucial for an effective ablation treatment. This work proposes a double-criteria approach for the identification of AF drivers based on a time-domain evaluation of atrial rate and AF organization. These two features are quantified by the measurement of atrial cycle length (ACL) and wave-similarity (WS). Based on ACL/WS formalism, AF drivers can be operatively defined as sites displaying electrical activity with high-rate and high-similarity (HR AND HS). The capability of ACL/WS analysis to identify AF driver sites and distinguish them from non-critical areas is shown in representative examples. The double-criteria evaluation for the identification of AF drivers, provided by our time-domain approach, might open new perspectives for the development of electrogram-guided ablation strategies in the single patient.
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Affiliation(s)
- Flavia Ravelli
- Biosignals and Biophysics Laboratory, Department of Physics and BIOtech, University of Trento, Trento, Italy.
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Houben RPM, de Groot NMS, Allessie MA. Analysis of Fractionated Atrial Fibrillation Electrograms by Wavelet Decomposition. IEEE Trans Biomed Eng 2010; 57:1388-98. [DOI: 10.1109/tbme.2009.2037974] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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10
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Right atrial organization and wavefront analysis in atrial fibrillation. Med Biol Eng Comput 2009; 47:1237-46. [DOI: 10.1007/s11517-009-0540-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2009] [Accepted: 09/17/2009] [Indexed: 11/26/2022]
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Nollo G, Marconcini M, Faes L, Bovolo F, Ravelli F, Bruzzone L. An automatic system for the analysis and classification of human atrial fibrillation patterns from intracardiac electrograms. IEEE Trans Biomed Eng 2008; 55:2275-85. [PMID: 18713697 DOI: 10.1109/tbme.2008.923155] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This paper presents an automatic system for the analysis and classification of atrial fibrillation (AF) patterns from bipolar intracardiac signals. The system is made up of: 1) a feature-extraction module that defines and extracts a set of measures potentially useful for characterizing AF types on the basis of their degree of organization; 2) a feature-selection module (based on the Jeffries-Matusita distance and a branch and bound search algorithm) identifying the best subset of features for discriminating different AF types; and 3) a support vector machine technique-based classification module that automatically discriminates the AF types according to the Wells' criteria. The automatic system was applied on 100 intracardiac AF signal strips and on a selection of 11 representative features, demonstrating: a) the possibility to properly identify the most significant features for the discrimination of AF types; b) higher accuracy (97.7% using the seven most informative features) than the traditional maximum likelihood classifier; and c) effectiveness in AF classification also with few training samples (accuracy = 88.3% with only five training signals). Finally, the system identifies a combination of indices characterizing changes of morphology of atrial activation waves and perturbation of the isoelectric line as the most effective in separating the AF types.
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Affiliation(s)
- Giandomenico Nollo
- Biophysics and Biosignals Laboratory, Department of Physics, University of Trento, 38050 Trento, Italy.
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Mainardi L, Sörnmo L, Cerutti S. Understanding Atrial Fibrillation: The Signal Processing Contribution, Part II. ACTA ACUST UNITED AC 2008. [DOI: 10.2200/s00153ed1v01y200809bme025] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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