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Shi X, Sau A, Li X, Patel K, Bajaj N, Varela M, Wu H, Handa B, Arnold A, Shun-Shin M, Keene D, Howard J, Whinnett Z, Peters N, Christensen K, Jensen HJ, Ng FS. Information theory-based direct causality measure to assess cardiac fibrillation dynamics. J R Soc Interface 2023; 20:20230443. [PMID: 37817583 PMCID: PMC10565370 DOI: 10.1098/rsif.2023.0443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 09/19/2023] [Indexed: 10/12/2023] Open
Abstract
Understanding the mechanism sustaining cardiac fibrillation can facilitate the personalization of treatment. Granger causality analysis can be used to determine the existence of a hierarchical fibrillation mechanism that is more amenable to ablation treatment in cardiac time-series data. Conventional Granger causality based on linear predictability may fail if the assumption is not met or given sparsely sampled, high-dimensional data. More recently developed information theory-based causality measures could potentially provide a more accurate estimate of the nonlinear coupling. However, despite their successful application to linear and nonlinear physical systems, their use is not known in the clinical field. Partial mutual information from mixed embedding (PMIME) was implemented to identify the direct coupling of cardiac electrophysiology signals. We show that PMIME requires less data and is more robust to extrinsic confounding factors. The algorithms were then extended for efficient characterization of fibrillation organization and hierarchy using clinical high-dimensional data. We show that PMIME network measures correlate well with the spatio-temporal organization of fibrillation and demonstrated that hierarchical type of fibrillation and drivers could be identified in a subset of ventricular fibrillation patients, such that regions of high hierarchy are associated with high dominant frequency.
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Affiliation(s)
- Xili Shi
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Arunashis Sau
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - Xinyang Li
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Kiran Patel
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - Nikesh Bajaj
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Marta Varela
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Huiyi Wu
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Balvinder Handa
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - Ahran Arnold
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - Matthew Shun-Shin
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - Daniel Keene
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - James Howard
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - Zachary Whinnett
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - Nicholas Peters
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - Kim Christensen
- Department of Physics, Imperial College London, London, UK
- Centre for Complexity Science, Imperial College London, London, UK
| | - Henrik Jeldtoft Jensen
- Department of Mathematics, Imperial College London, London, UK
- Centre for Complexity Science, Imperial College London, London, UK
- Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan
| | - Fu Siong Ng
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
- Department of Cardiology, Chelsea and Westminster NHS Foundation Trust, London, UK
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2
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Intracardiac ECG pulse localization using overlapping block sparse reconstruction. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.103921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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3
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Vila M, Rocher S, Rivolta MW, Saiz J, Sassi R. Directed Network Mapping Approach to Rotor Localization in Atrial Fibrillation Simulation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:730-733. [PMID: 34891395 DOI: 10.1109/embc46164.2021.9629911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Catheter ablation for atrial fibrillation (AF) is one of the most commonly performed electrophysiology procedures. Despite significant advances in our understanding of AF mechanisms in the last years, ablation outcomes remain suboptimal for many patients, particularly those with persistent or long-standing AF. A possible reason is that ablation techniques mainly focus on anatomic, rather than patient-specific functional targets for ablation. The identification of such ablation targets remains challenging. The purpose of this study is to investigate a novel approach based on directed networks, which allow the automatic detection of important arrhythmia mechanisms, that can be convenient for guiding the ablation strategy. The networks are generated by processing unipolar electrograms (EGMs) collected by the catheters positioned at the different regions of the atria. Network vertices represent the locations of the recordings and edges are determined using cross-covariance time-delay estimation method. The algorithm identifies rotational activity, spreading from vertex to vertex creating a cycle. This work is a simulation study and it uses a highly detailed computational 3D model of human atria in which sustained rotor activation of the atria was achieved. Virtual electrodes were placed on the endocardial surface, and EGMs were calculated at each of these electrodes. The propagation of the electric wave fronts in the atrial myocardium during AF is very complex, so in order to properly capture wave propagation patterns, we split EGMs into multiple short time frames. Then, a specific network for each of these time frames was generated, and the cycles repeating in consecutive networks point us to the stable rotor's location. The respective atrial voltage map served as reference. By detecting a cycle between the same 3 nodes in 19 out of 58 networks, where 10 of these networks were in consecutive time frames, a stable rotor was successfully located.
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Vila M, Rivolta MW, Luongo G, Unger LA, Luik A, Gigli L, Lombardi F, Loewe A, Sassi R. Atrial Flutter Mechanism Detection Using Directed Network Mapping. Front Physiol 2021; 12:749635. [PMID: 34764882 PMCID: PMC8577834 DOI: 10.3389/fphys.2021.749635] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 09/28/2021] [Indexed: 11/13/2022] Open
Abstract
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.
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Affiliation(s)
- Muhamed Vila
- Dipartimento di Informatica, Università degli Studi di Milano, Milan, Italy
| | | | - Giorgio Luongo
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Laura Anna Unger
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Armin Luik
- Medizinische Klinik IV, Städtisches Klinikum Karlsruhe, Karlsruhe, Germany
| | - Lorenzo Gigli
- UOC Malattie Cardiovascolari, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Federico Lombardi
- UOC Malattie Cardiovascolari, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Roberto Sassi
- Dipartimento di Informatica, Università degli Studi di Milano, Milan, Italy
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Masè M, Faes L, Ravelli F. Letter by Masè et al Regarding Article, "Granger Causality-Based Analysis for Classification of Fibrillation Mechanisms and Localization of Rotational Drivers". Circ Arrhythm Electrophysiol 2020; 13:e008675. [PMID: 32809881 DOI: 10.1161/circep.120.008675] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Michela Masè
- Healthcare Research and Innovation Program, IRCS-HTA, Bruno Kessler Foundation, Trento, Italy (M.M.).,Institute of Mountain Emergency Medicine, Eurac Research, Bolzano, Italy (M.M.)
| | - Luca Faes
- Department of Engineering, University of Palermo, Italy (L.F.)
| | - Flavia Ravelli
- Laboratory of Biophysics and Biosignals, Department of Physics, University of Trento, Italy (F.R.)
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Koneshloo A, Du D, Du Y. An Uncertainty Modeling Framework for Intracardiac Electrogram Analysis. Bioengineering (Basel) 2020; 7:E62. [PMID: 32604784 PMCID: PMC7355499 DOI: 10.3390/bioengineering7020062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 06/22/2020] [Accepted: 06/24/2020] [Indexed: 11/16/2022] Open
Abstract
Intracardiac electrograms (EGMs) are electrical signals measured within the chambers of the heart, which can be used to locate abnormal cardiac tissue and guide catheter ablations to treat cardiac arrhythmias. EGMs may contain large amounts of uncertainty and irregular variations, which pose significant challenges in data analysis. This study aims to introduce a statistical approach to account for the data uncertainty while analyzing EGMs for abnormal electrical impulse identification. The activation order of catheter sensors was modeled with a multinomial distribution, and maximum likelihood estimations were done to track the electrical wave conduction path in the presence of uncertainty. Robust optimization was performed to locate the electrical impulses based on the local conduction velocity and the geodesic distances between catheter sensors. The proposed algorithm can identify the focal sources when the electrical conduction is initiated by irregular electrical impulses and involves wave collisions, breakups, and spiral waves. The statistical modeling framework can efficiently deal with data uncertainties and provide a reliable estimation of the focal source locations. This shows the great potential of a statistical approach for the quantitative analysis of the stochastic activity of electrical waves in cardiac disorders and suggests future investigations integrating statistical methods with a deterministic geometry-based method to achieve advanced diagnostic performance.
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Affiliation(s)
- Amirhossein Koneshloo
- Department of Industrial, Manufacturing and Systems Engineering, Texas Tech University, Lubbock, TX 79409, USA
| | - Dongping Du
- Department of Industrial, Manufacturing and Systems Engineering, Texas Tech University, Lubbock, TX 79409, USA
| | - Yuncheng Du
- Department of Chemical & Biomolecular Engineering, Clarkson University, Potsdam, NY 13699, USA
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7
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Vandersickel N, Van Nieuwenhuyse E, Van Cleemput N, Goedgebeur J, El Haddad M, De Neve J, Demolder A, Strisciuglio T, Duytschaever M, Panfilov AV. Directed Networks as a Novel Way to Describe and Analyze Cardiac Excitation: Directed Graph Mapping. Front Physiol 2019; 10:1138. [PMID: 31551814 PMCID: PMC6746922 DOI: 10.3389/fphys.2019.01138] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Accepted: 08/19/2019] [Indexed: 12/31/2022] Open
Abstract
Networks provide a powerful methodology with applications in a variety of biological, technological and social systems such as analysis of brain data, social networks, internet search engine algorithms, etc. To date, directed networks have not yet been applied to characterize the excitation of the human heart. In clinical practice, cardiac excitation is recorded by multiple discrete electrodes. During (normal) sinus rhythm or during cardiac arrhythmias, successive excitation connects neighboring electrodes, resulting in their own unique directed network. This in theory makes it a perfect fit for directed network analysis. In this study, we applied directed networks to the heart in order to describe and characterize cardiac arrhythmias. Proof-of-principle was established using in-silico and clinical data. We demonstrated that tools used in network theory analysis allow determination of the mechanism and location of certain cardiac arrhythmias. We show that the robustness of this approach can potentially exceed the existing state-of-the art methodology used in clinics. Furthermore, implementation of these techniques in daily practice can improve the accuracy and speed of cardiac arrhythmia analysis. It may also provide novel insights in arrhythmias that are still incompletely understood.
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Affiliation(s)
- Nele Vandersickel
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium
| | | | - Nico Van Cleemput
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Jan Goedgebeur
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
- Computer Science Department, University of Mons, Mons, Belgium
| | - Milad El Haddad
- Ghent University Hospital Heart Center, Ghent University, Ghent, Belgium
| | - Jan De Neve
- Department of Data Analysis, Ghent University, Ghent, Belgium
| | - Anthony Demolder
- Ghent University Hospital Heart Center, Ghent University, Ghent, Belgium
| | | | - Mattias Duytschaever
- Ghent University Hospital Heart Center, Ghent University, Ghent, Belgium
- Cardiology Department, AZ Sint-Jan, Bruges, Belgium
| | - Alexander V. Panfilov
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium
- Laboratory of Computational Biology and Medicine, Ural Federal University, Ekaterinburg, Russia
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8
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Luengo D, Rios-Munoz G, Elvira V, Sanchez C, Artes-Rodriguez A. Hierarchical Algorithms for Causality Retrieval in Atrial Fibrillation Intracavitary Electrograms. IEEE J Biomed Health Inform 2018; 23:143-155. [PMID: 29994646 DOI: 10.1109/jbhi.2018.2805773] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Multichannel intracavitary electrograms (EGMs) are acquired at the electrophysiology laboratory to guide radio frequency catheter ablation of patients suffering from atrial fibrillation. These EGMs are used by cardiologists to determine candidate areas for ablation (e.g., areas corresponding to high dominant frequencies or complex fractionated electrograms). In this paper, we introduce two hierarchical algorithms to retrieve the causal interactions among these multiple EGMs. Both algorithms are based on Granger causality, but other causality measures can be easily incorporated. In both cases, they start by selecting a root node, but they differ on the way in which they explore the set of signals to determine their cause-effect relationships: either testing the full set of unexplored signals (GS-CaRe) or performing a local search only among the set of neighbor EGMs (LS-CaRe). The ensuing causal model provides important information about the propagation of the electrical signals inside the atria, uncovering wavefronts and activation patterns that can guide cardiologists towards candidate areas for catheter ablation. Numerical experiments, on both synthetic signals and annotated real-world signals, show the good performance of the two proposed approaches.
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9
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Lp (p ≤ 1) Norm Partial Directed Coherence for Directed Network Analysis of Scalp EEGs. Brain Topogr 2018; 31:738-752. [DOI: 10.1007/s10548-018-0624-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Accepted: 01/17/2018] [Indexed: 10/18/2022]
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10
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Granger Causality and Jensen-Shannon Divergence to Determine Dominant Atrial Area in Atrial Fibrillation. ENTROPY 2018; 20:e20010057. [PMID: 33265143 PMCID: PMC7512253 DOI: 10.3390/e20010057] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Revised: 12/27/2017] [Accepted: 01/05/2018] [Indexed: 01/08/2023]
Abstract
Atrial fibrillation (AF) is already the most commonly occurring arrhythmia. Catheter pulmonary vein ablation has emerged as a treatment that is able to make the arrhythmia disappear; nevertheless, recurrence to arrhythmia is very frequent. In this study, it is proposed to perform an analysis of the electrical signals recorded from bipolar catheters at three locations, pulmonary veins and the right and left atria, before to and during the ablation procedure. Principal Component Analysis (PCA) was applied to reduce data dimension and Granger causality and divergence techniques were applied to analyse connectivity along the atria, in three main regions: pulmonary veins, left atrium (LA) and right atrium (RA). The results showed that, before the procedure, patients with recurrence in the arrhythmia had greater connectivity between atrial areas. Moreover, during the ablation procedure, in patients with recurrence in the arrhythmial both atria were more connected than in patients that maintained sinus rhythms. These results can be helpful for procedures designing to end AF.
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11
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Alcaine A, Mase M, Cristoforetti A, Ravelli F, Nollo G, Laguna P, Martinez JP, Faes L. A Multi-Variate Predictability Framework to Assess Invasive Cardiac Activity and Interactions During Atrial Fibrillation. IEEE Trans Biomed Eng 2017; 64:1157-1168. [DOI: 10.1109/tbme.2016.2592953] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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12
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Basket-Type Catheters: Diagnostic Pitfalls Caused by Deformation and Limited Coverage. BIOMED RESEARCH INTERNATIONAL 2016; 2016:5340574. [PMID: 28070511 PMCID: PMC5187596 DOI: 10.1155/2016/5340574] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Accepted: 10/27/2016] [Indexed: 11/17/2022]
Abstract
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.
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13
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Abstract
The sequence of myocardial electrical activation during fibrillation is complex and changes with each cycle. Phase analysis represents the electrical activation-recovery process as an angle. Lines of equal phase converge at a phase singularity at the center of rotation of a reentrant wave, and the identification of reentry and tracking of reentrant wavefronts can be automated. We examine the basic ideas behind phase analysis. With the exciting prospect of using phase analysis of atrial electrograms to guide ablation in the human heart, we highlight several recent developments in preprocessing electrograms so that phase can be estimated reliably.
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Affiliation(s)
- Richard H Clayton
- Insigneo Institute for in-silico medicine and Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello Street, Sheffield S1 4DP, UK.
| | - Martyn P Nash
- Auckland Bioengineering Institute and Engineering Science, University of Auckland, Uniservices House, Level 7, Room 439-715, 70 Symonds Street, Auckland 1010, New Zealand
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14
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Rodrigo M, Pedrón-Torecilla J, Hernández I, Liberos A, Climent AM, Guillem MS. Data analysis in cardiac arrhythmias. Methods Mol Biol 2014; 1246:217-35. [PMID: 25417089 DOI: 10.1007/978-1-4939-1985-7_14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2023]
Abstract
Cardiac arrhythmias are an increasingly present in developed countries and represent a major health and economic burden. The occurrence of cardiac arrhythmias is closely linked to the electrical function of the heart. Consequently, the analysis of the electrical signal generated by the heart tissue, either recorded invasively or noninvasively, provides valuable information for the study of cardiac arrhythmias. In this chapter, novel cardiac signal analysis techniques that allow the study and diagnosis of cardiac arrhythmias are described, with emphasis on cardiac mapping which allows for spatiotemporal analysis of cardiac signals.Cardiac mapping can serve as a diagnostic tool by recording cardiac signals either in close contact to the heart tissue or noninvasively from the body surface, and allows the identification of cardiac sites responsible of the development or maintenance of arrhythmias. Cardiac mapping can also be used for research in cardiac arrhythmias in order to understand their mechanisms. For this purpose, both synthetic signals generated by computer simulations and animal experimental models allow for more controlled physiological conditions and complete access to the organ.
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Affiliation(s)
- Miguel Rodrigo
- BIO-ITACA, Universitat Politècnica de València, Edificio 8G, Camino de Vera, S/N, 46022, Valencia, Spain
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15
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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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16
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Zeemering S, Peeters R, van Hunnik A, Verheule S, Schotten U. Identification of recurring wavefront propagation patterns in atrial fibrillation using basis pursuit. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2013:2928-31. [PMID: 24110340 DOI: 10.1109/embc.2013.6610153] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
High density contact electrogram data of atrial fibrillation (AF) contain detailed information on recurring activation patterns and dominant signaling pathways. Current methods to analyze these patterns and pathways rely mainly on supervised atrial deflection annotation and wave reconstruction. In this study, we developed a new algorithm to automatically identify recurring patterns and dominant pathways without the need for annotation. A sparse multivariate autoregression model was estimated on short segments of synchronous unipolar electrograms to extract the dominant interactions between electrograms at different recording electrodes. Sparsity of the electrode interaction matrices at several time-lags was maximized by applying a distance-weighted basis pursuit algorithm. Dominant interactions were identified by computing the mean interaction matrix over a number of consecutive time segments. The algorithm was evaluated on high-density recordings with 234 electrodes and 2.4mm electrode spacing in the left and right atrial free wall of a goat model of AF. The method was able to identify relevant patterns of AF, including wave trains, repetitive breakthrough waves and rotating wave activity.
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