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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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Vila M, Rivolta MW, Barrios Espinosa CA, Unger LA, Luik A, Loewe A, Sassi R. Recommender system for ablation lines to treat complex atrial tachycardia. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107406. [PMID: 36787660 DOI: 10.1016/j.cmpb.2023.107406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 01/30/2023] [Accepted: 02/03/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Planning the optimal ablation strategy for the treatment of complex atrial tachycardia (CAT) is a time consuming task and is error-prone. Recently, directed network mapping, a technology based on graph theory, proved to efficiently identify CAT based solely on data of clinical interventions. Briefly, a directed network was used to model the atrial electrical propagation and reentrant activities were identified by looking for closed-loop paths in the network. In this study, we propose a recommender system, built as an optimization problem, able to suggest the optimal ablation strategy for the treatment of CAT. METHODS The optimization problem modeled the optimal ablation strategy as that one interrupting all reentrant mechanisms while minimizing the ablated atrial surface. The problem was designed on top of directed network mapping. Considering the exponential complexity of finding the optimal solution of the problem, we introduced a heuristic algorithm with polynomial complexity. The proposed algorithm was applied to the data of i) 6 simulated scenarios including both left and right atrial flutter; and ii) 10 subjects that underwent a clinical routine. RESULTS The recommender system suggested the optimal strategy in 4 out of 6 simulated scenarios. On clinical data, the recommended ablation lines were found satisfactory on 67% of the cases according to the clinician's opinion, while they were correctly located in 89%. The algorithm made use of only data collected during mapping and was able to process them nearly real-time. CONCLUSIONS The first recommender system for the identification of the optimal ablation lines for CAT, based solely on the data collected during the intervention, is presented. The study may open up interesting scenarios for the application of graph theory for the treatment of CAT.
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Affiliation(s)
- Muhamed Vila
- Università degli Studi di Milano, Via Celoria 18, Milan, 20133, Italy
| | - Massimo W Rivolta
- Università degli Studi di Milano, Via Celoria 18, Milan, 20133, Italy.
| | - Cristian A Barrios Espinosa
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, Karlsruhe, 76131, Germany
| | - Laura A Unger
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, Karlsruhe, 76131, Germany
| | - Armin Luik
- Medizinische Klinik IV, Städtisches Klinikum Karlsruhe, Moltkestraße 90, Karlsruhe, 76133, Germany
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, Karlsruhe, 76131, Germany
| | - Roberto Sassi
- Università degli Studi di Milano, Via Celoria 18, Milan, 20133, Italy
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3
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Atrial fibrillation driver identification through regional mutual information networks: a modeling perspective. J Interv Card Electrophysiol 2022; 64:649-660. [PMID: 34981289 PMCID: PMC9470649 DOI: 10.1007/s10840-021-01101-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 12/01/2021] [Indexed: 12/17/2022]
Abstract
Purpose Effective identification of electrical drivers within remodeled tissue is a key for improving ablation treatment for atrial fibrillation. We have developed a mutual information, graph-based approach to identify and propose fault tolerance metric of local efficiency as a distinguishing feature of rotational activation and remodeled atrial tissue. Methods Voltage data were extracted from atrial tissue simulations (2D Karma, 3D physiological, and the Multiscale Cardiac Simulation Framework (MSCSF)) using multi-spline open and parallel regional mapping catheter geometries. Graphs were generated based on varied mutual information thresholds between electrode pairs and the local efficiency for each graph was calculated. Results High-resolution mapping catheter geometries can distinguish between rotational and irregular activation patterns using the derivative of local efficiency as a function of increasing mutual information threshold. The derivative is decreased for rotational activation patterns comparing to irregular activations in both a simplified 2D model (0.0017 ± 1 × 10−4 vs. 0.0032 ± 1 × 10−4, p < 0.01) and a more realistic 3D model (0.00092 ± 5 × 10−5 vs. 0.0014 ± 4 × 10−5, p < 0.01). Average local efficiency derivative can also distinguish between degrees of remodeling. Simulations using the MSCSF model, with 10 vs. 90% remodeling, display distinct derivatives in the grid design parallel spline catheter configuration (0.0015 ± 5 × 10−5 vs. 0.0019 ± 6 × 10−5, p < 0.01) and the flower shaped open spline configuration (0.0011 ± 5 × 10−5 vs. 0.0016 ± 4 × 10−5, p < 0.01). Conclusion A decreased derivative of local efficiency characterizes rotational activation and varies with atrial remodeling. This suggests a distinct communication pattern in cardiac rotational activation detectable via high-resolution regional mapping and could enable identification of electrical drivers for targeted ablation. Supplementary Information The online version contains supplementary material available at 10.1007/s10840-021-01101-z.
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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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Ravikumar V, Annoni E, Parthiban P, Zlochiver S, Roukoz H, Mulpuru SK, Tolkacheva EG. Novel mapping techniques for rotor core detection using simulated intracardiac electrograms. J Cardiovasc Electrophysiol 2021; 32:1268-1280. [PMID: 33570241 DOI: 10.1111/jce.14948] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Revised: 01/27/2021] [Accepted: 02/05/2021] [Indexed: 11/29/2022]
Abstract
BACKGROUND Catheter ablation is associated with limited success rates in patients with persistent atrial fibrillation (AF). Currently, existing mapping systems fail to identify critical target sites for ablation. Recently, we proposed and validated several techniques (multiscale frequency [MSF], Shannon entropy [SE], kurtosis [Kt], and multiscale entropy [MSE]) to identify pivot point of rotors using ex-vivo optical mapping animal experiments. However, the performance of these techniques is unclear for the clinically recorded intracardiac electrograms (EGMs), due to the different nature of the signals. OBJECTIVE This study aims to evaluate the performance of MSF, MSE, SE, and Kt techniques to identify the pivot point of the rotor using unipolar and bipolar EGMs obtained from numerical simulations. METHODS Stationary and meandering rotors were simulated in a 2D human atria. The performances of new approaches were quantified by comparing the "true" core of the rotor with the core identified by the techniques. Also, the performances of all techniques were evaluated in the presence of noise, scar, and for the case of the multielectrode multispline and grid catheters. RESULTS Our results demonstrate that all the approaches are able to accurately identify the pivot point of both stationary and meandering rotors from both unipolar and bipolar EGMs. The presence of noise and scar tissue did not significantly affect the performance of the techniques. Finally, the core of the rotors was correctly identified for the case of multielectrode multispline and grid catheter simulations. CONCLUSION The core of rotors can be successfully identified from EGMs using novel techniques; thus, providing motivation for future clinical implementations.
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Affiliation(s)
- Vasanth Ravikumar
- Department of Electrical Engineering, University of Minnesota, Minneapolis, Minnesota, USA
| | - Elizabeth Annoni
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota, USA
| | - Preethy Parthiban
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota, USA
| | - Sharon Zlochiver
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota, USA
| | - Henri Roukoz
- Division of Cardiovascular, Medical School, University of Minnesota, Minneapolis, Minnesota, USA
| | - Siva K Mulpuru
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Elena G Tolkacheva
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota, USA
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Huang C, Zhu L. Robust evaluation method of communication network based on the combination of complex network and big data. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05264-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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7
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Ng FS, Handa BS, Li X, Peters NS. Toward Mechanism-Directed Electrophenotype-Based Treatments for Atrial Fibrillation. Front Physiol 2020; 11:987. [PMID: 33013435 PMCID: PMC7493660 DOI: 10.3389/fphys.2020.00987] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Accepted: 07/20/2020] [Indexed: 12/19/2022] Open
Abstract
Current treatment approaches for persistent atrial fibrillation (AF) have a ceiling of success of around 50%. This is despite 15 years of developing adjunctive ablation strategies in addition to pulmonary vein isolation to target the underlying arrhythmogenic substrate in AF. A major shortcoming of our current approach to AF treatment is its predominantly empirical nature. This has in part been due to a lack of consensus on the mechanisms that sustain human AF. In this article, we review evidence suggesting that the previous debates on AF being either an organized arrhythmia with a focal driver or a disorganized rhythm sustained by multiple wavelets, may prove to be a false dichotomy. Instead, a range of fibrillation electrophenotypes exists along a continuous spectrum, and the predominant mechanism in an individual case is determined by the nature and extent of remodeling of the underlying substrate. We propose moving beyond the current empirical approach to AF treatment, highlight the need to prescribe AF treatments based on the underlying AF electrophenotype, and review several possible novel mapping algorithms that may be useful in discerning the AF electrophenotype to guide tailored treatments, including Granger Causality mapping.
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Affiliation(s)
- Fu Siong Ng
- National Heart & Lung Institute, Imperial College London, London, United Kingdom
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8
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Handa BS, Li X, Aras KK, Qureshi NA, Mann I, Chowdhury RA, Whinnett ZI, Linton NW, Lim PB, Kanagaratnam P, Efimov IR, Peters NS, Ng FS. Granger Causality-Based Analysis for Classification of Fibrillation Mechanisms and Localization of Rotational Drivers. Circ Arrhythm Electrophysiol 2020; 13:e008237. [PMID: 32064900 PMCID: PMC7069398 DOI: 10.1161/circep.119.008237] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 02/04/2020] [Indexed: 11/16/2022]
Abstract
BACKGROUND The mechanisms sustaining myocardial fibrillation remain disputed, partly due to a lack of mapping tools that can accurately identify the mechanism with low spatial resolution clinical recordings. Granger causality (GC) analysis, an econometric tool for quantifying causal relationships between complex time-series, was developed as a novel fibrillation mapping tool and adapted to low spatial resolution sequentially acquired data. METHODS Ventricular fibrillation (VF) optical mapping was performed in Langendorff-perfused Sprague-Dawley rat hearts (n=18), where novel algorithms were developed using GC-based analysis to (1) quantify causal dependence of neighboring signals and plot GC vectors, (2) quantify global organization with the causality pairing index, a measure of neighboring causal signal pairs, and (3) localize rotational drivers (RDs) by quantifying the circular interdependence of neighboring signals with the circular interdependence value. GC-based mapping tools were optimized for low spatial resolution from downsampled optical mapping data, validated against high-resolution phase analysis and further tested in previous VF optical mapping recordings of coronary perfused donor heart left ventricular wedge preparations (n=12), and adapted for sequentially acquired intracardiac electrograms during human persistent atrial fibrillation mapping (n=16). RESULTS Global VF organization quantified by causality pairing index showed a negative correlation at progressively lower resolutions (50% resolution: P=0.006, R2=0.38, 12.5% resolution, P=0.004, R2=0.41) with a phase analysis derived measure of disorganization, locations occupied by phase singularities. In organized VF with high causality pairing index values, GC vector mapping characterized dominant propagating patterns and localized stable RDs, with the circular interdependence value showing a significant difference in driver versus nondriver regions (0.91±0.05 versus 0.35±0.06, P=0.0002). These findings were further confirmed in human VF. In persistent atrial fibrillation, a positive correlation was found between the causality pairing index and presence of stable RDs (P=0.0005,R2=0.56). Fifty percent of patients had RDs, with a low incidence of 0.9±0.3 RDs per patient. CONCLUSIONS GC-based fibrillation analysis can measure global fibrillation organization, characterize dominant propagating patterns, and map RDs using low spatial resolution sequentially acquired data.
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Affiliation(s)
- Balvinder S. Handa
- National Heart & Lung Institute, Imperial College London, United Kingdom (B.S.H., X.L., N.A.Q., I.M., R.A.C., Z.I.W., N.W.F.L., P.B.L., P.K., N.S.P., F.S.N.)
| | - Xinyang Li
- National Heart & Lung Institute, Imperial College London, United Kingdom (B.S.H., X.L., N.A.Q., I.M., R.A.C., Z.I.W., N.W.F.L., P.B.L., P.K., N.S.P., F.S.N.)
| | - Kedar K. Aras
- Department of Biomedical Engineering, George Washington University, Washington, DC (K.K.A., I.R.E.)
| | - Norman A. Qureshi
- National Heart & Lung Institute, Imperial College London, United Kingdom (B.S.H., X.L., N.A.Q., I.M., R.A.C., Z.I.W., N.W.F.L., P.B.L., P.K., N.S.P., F.S.N.)
| | - Ian Mann
- National Heart & Lung Institute, Imperial College London, United Kingdom (B.S.H., X.L., N.A.Q., I.M., R.A.C., Z.I.W., N.W.F.L., P.B.L., P.K., N.S.P., F.S.N.)
| | - Rasheda A. Chowdhury
- National Heart & Lung Institute, Imperial College London, United Kingdom (B.S.H., X.L., N.A.Q., I.M., R.A.C., Z.I.W., N.W.F.L., P.B.L., P.K., N.S.P., F.S.N.)
| | - Zachary I. Whinnett
- National Heart & Lung Institute, Imperial College London, United Kingdom (B.S.H., X.L., N.A.Q., I.M., R.A.C., Z.I.W., N.W.F.L., P.B.L., P.K., N.S.P., F.S.N.)
| | - Nick W.F. Linton
- National Heart & Lung Institute, Imperial College London, United Kingdom (B.S.H., X.L., N.A.Q., I.M., R.A.C., Z.I.W., N.W.F.L., P.B.L., P.K., N.S.P., F.S.N.)
| | - Phang Boon Lim
- National Heart & Lung Institute, Imperial College London, United Kingdom (B.S.H., X.L., N.A.Q., I.M., R.A.C., Z.I.W., N.W.F.L., P.B.L., P.K., N.S.P., F.S.N.)
| | - Prapa Kanagaratnam
- National Heart & Lung Institute, Imperial College London, United Kingdom (B.S.H., X.L., N.A.Q., I.M., R.A.C., Z.I.W., N.W.F.L., P.B.L., P.K., N.S.P., F.S.N.)
| | - Igor R. Efimov
- Department of Biomedical Engineering, George Washington University, Washington, DC (K.K.A., I.R.E.)
| | - Nicholas S. Peters
- National Heart & Lung Institute, Imperial College London, United Kingdom (B.S.H., X.L., N.A.Q., I.M., R.A.C., Z.I.W., N.W.F.L., P.B.L., P.K., N.S.P., F.S.N.)
- Department of Biomedical Engineering, George Washington University, Washington, DC (K.K.A., I.R.E.)
| | - Fu Siong Ng
- National Heart & Lung Institute, Imperial College London, United Kingdom (B.S.H., X.L., N.A.Q., I.M., R.A.C., Z.I.W., N.W.F.L., P.B.L., P.K., N.S.P., F.S.N.)
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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: 25] [Impact Index Per Article: 5.0] [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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Aronis KN, Berger RD, Calkins H, Chrispin J, Marine JE, Spragg DD, Tao S, Tandri H, Ashikaga H. Is human atrial fibrillation stochastic or deterministic?-Insights from missing ordinal patterns and causal entropy-complexity plane analysis. CHAOS (WOODBURY, N.Y.) 2018; 28:063130. [PMID: 29960392 PMCID: PMC6026026 DOI: 10.1063/1.5023588] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Accepted: 06/11/2018] [Indexed: 06/08/2023]
Abstract
The mechanism of atrial fibrillation (AF) maintenance in humans is yet to be determined. It remains controversial whether cardiac fibrillatory dynamics are the result of a deterministic or a stochastic process. Traditional methods to differentiate deterministic from stochastic processes have several limitations and are not reliably applied to short and noisy data obtained during clinical studies. The appearance of missing ordinal patterns (MOPs) using the Bandt-Pompe (BP) symbolization is indicative of deterministic dynamics and is robust to brief time series and experimental noise. Our aim was to evaluate whether human AF dynamics is the result of a stochastic or a deterministic process. We used 38 intracardiac atrial electrograms during AF from the coronary sinus of 10 patients undergoing catheter ablation of AF. We extracted the intervals between consecutive atrial depolarizations (AA interval) and converted the AA interval time series to their BP symbolic representation (embedding dimension 5, time delay 1). We generated 40 iterative amplitude-adjusted, Fourier-transform (IAAFT) surrogate data for each of the AA time series. IAAFT surrogates have the same frequency spectrum, autocorrelation, and probability distribution with the original time series. Using the BP symbolization, we compared the number of MOPs and the rate of MOP decay in the first 1000 timepoints of the original time series with that of the surrogate data. We calculated permutation entropy and permutation statistical complexity and represented each time series on the causal entropy-complexity plane. We demonstrated that (a) the number of MOPs in human AF is significantly higher compared to the surrogate data (2.7 ± 1.18 vs. 0.39 ± 0.28, p < 0.001); (b) the median rate of MOP decay in human AF was significantly lower compared with the surrogate data (6.58 × 10-3 vs. 7.79 × 10-3, p < 0.001); and (c) 81.6% of the individual recordings had a rate of decay lower than the 95% confidence intervals of their corresponding surrogates. On the causal entropy-complexity plane, human AF lay on the deterministic part of the plane that was located above the trajectory of fractional Brownian motion with different Hurst exponents on the plane. This analysis demonstrates that human AF dynamics does not arise from a rescaled linear stochastic process or a fractional noise, but either a deterministic or a nonlinear stochastic process. Our results justify the development and application of mathematical analysis and modeling tools to enable predictive control of human AF.
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Affiliation(s)
- Konstantinos N. Aronis
- Cardiac Arrhythmia Service, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, USA
| | - Ronald D. Berger
- Cardiac Arrhythmia Service, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, USA
| | - Hugh Calkins
- Cardiac Arrhythmia Service, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, USA
| | - Jonathan Chrispin
- Cardiac Arrhythmia Service, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, USA
| | - Joseph E. Marine
- Cardiac Arrhythmia Service, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, USA
| | - David D. Spragg
- Cardiac Arrhythmia Service, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, USA
| | - Susumu Tao
- Cardiac Arrhythmia Service, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, USA
| | - Harikrishna Tandri
- Cardiac Arrhythmia Service, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, USA
| | - Hiroshi Ashikaga
- Author to whom correspondence should be addressed: . Telephone: 410-955-7534. Fax: 443-873-5019
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