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Lootens S, Janssens I, Van Den Abeele R, Wülfers EM, Bezerra AS, Verstraeten B, Hendrickx S, Okenov A, Nezlobinsky T, Panfilov AV, Vandersickel N. Directed Graph Mapping exceeds Phase Mapping for the detection of simulated 2D meandering rotors in fibrotic tissue with added noise. Comput Biol Med 2024; 171:108138. [PMID: 38401451 DOI: 10.1016/j.compbiomed.2024.108138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 02/09/2024] [Accepted: 02/12/2024] [Indexed: 02/26/2024]
Abstract
Cardiac arrhythmias such as atrial fibrillation (AF) are recognised to be associated with re-entry or rotors. A rotor is a wave of excitation in the cardiac tissue that wraps around its refractory tail, causing faster-than-normal periodic excitation. The detection of rotor centres is of crucial importance in guiding ablation strategies for the treatment of arrhythmia. The most popular technique for detecting rotor centres is Phase Mapping (PM), which detects phase singularities derived from the phase of a signal. This method has been proven to be prone to errors, especially in regimes of fibrotic tissue and temporal noise. Recently, a novel technique called Directed Graph Mapping (DGM) was developed to detect rotational activity such as rotors by creating a network of excitation. This research aims to compare the performance of advanced PM techniques versus DGM for the detection of rotors using 64 simulated 2D meandering rotors in the presence of various levels of fibrotic tissue and temporal noise. Four strategies were employed to compare the performances of PM and DGM. These included a visual analysis, a comparison of F2-scores and distance distributions, and calculating p-values using the mid-p McNemar test. Results indicate that in the case of low meandering, fibrosis and noise, PM and DGM yield excellent results and are comparable. However, in the case of high meandering, fibrosis and noise, PM is undeniably prone to errors, mainly in the form of an excess of false positives, resulting in low precision. In contrast, DGM is more robust against these factors as F2-scores remain high, yielding F2≥0.931 as opposed to the best PM F2≥0.635 across all 64 simulations.
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Affiliation(s)
| | - Iris Janssens
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium
| | | | - Eike M Wülfers
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium
| | | | - Bjorn Verstraeten
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium
| | - Sander Hendrickx
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium
| | - Arstanbek Okenov
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium
| | - Timur Nezlobinsky
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium
| | - Alexander V Panfilov
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium; World-Class Research Center "Digital Biodesign and personalised healthcare", Sechenov University, Moscow 119991, Russia; Laboratory of Computational Biology and Medicine, Ural Federal University, Ekaterinburg 620002, Russia
| | - Nele Vandersickel
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium
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Kong X, Ravikumar V, Mulpuru SK, Roukoz H, Tolkacheva EG. A Data-Driven Preprocessing Framework for Atrial Fibrillation Intracardiac Electrocardiogram Analysis. ENTROPY (BASEL, SWITZERLAND) 2023; 25:332. [PMID: 36832698 PMCID: PMC9955244 DOI: 10.3390/e25020332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 02/03/2023] [Accepted: 02/09/2023] [Indexed: 06/18/2023]
Abstract
Atrial Fibrillation (AF) is the most common cardiac arrhythmia. Signal-processing approaches are widely used for the analysis of intracardiac electrograms (iEGMs), which are collected during catheter ablation from patients with AF. In order to identify possible targets for ablation therapy, dominant frequency (DF) is widely used and incorporated in electroanatomical mapping systems. Recently, a more robust measure, multiscale frequency (MSF), for iEGM data analysis was adopted and validated. However, before completing any iEGM analysis, a suitable bandpass (BP) filter must be applied to remove noise. Currently, no clear guidelines for BP filter characteristics exist. The lower bound of the BP filter is usually set to 3-5 Hz, while the upper bound (BP¯th) of the BP filter varies from 15 Hz to 50 Hz according to many researchers. This large range of BP¯th subsequently affects the efficiency of further analysis. In this paper, we aimed to develop a data-driven preprocessing framework for iEGM analysis, and validate it based on DF and MSF techniques. To achieve this goal, we optimized the BP¯th using a data-driven approach (DBSCAN clustering) and demonstrated the effects of different BP¯th on subsequent DF and MSF analysis of clinically recorded iEGMs from patients with AF. Our results demonstrated that our preprocessing framework with BP¯th = 15 Hz has the best performance in terms of the highest Dunn index. We further demonstrated that the removal of noisy and contact-loss leads is necessary for performing correct data iEGMs data analysis.
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Affiliation(s)
- Xiangzhen Kong
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Vasanth Ravikumar
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Siva K. Mulpuru
- Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN 55905, USA
| | - Henri Roukoz
- Division of Cardiology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Elena G. Tolkacheva
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
- Lillehei Heart Institute, University of Minnesota, Minneapolis, MN 55455, USA
- Institute for Engineering in Medicine, University of Minnesota, Minneapolis, MN 55455, USA
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Prats-Boluda G, Guillem MS, Rodrigo M, Ye-Lin Y, Garcia-Casado J. Identification of atrial fibrillation drivers by means of concentric ring electrodes. Comput Biol Med 2022; 148:105957. [PMID: 35981454 DOI: 10.1016/j.compbiomed.2022.105957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 07/19/2022] [Accepted: 08/06/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND AND OBJECTIVE The prevalence of atrial fibrillation (AF) has tripled in the last 50 years due to population aging. High-frequency (DFdriver) activated atrial regions lead the activation of the rest of the atria, disrupting the propagation wavefront. Fourier based spectral analysis of body surface potential maps have been proposed for DFdriver identification, although these approaches present serious drawbacks due to their limited spectral resolution for short AF epochs and the blurring effect of the volume conductor. Laplacian signals (BC-ECG) from bipolar concentric ring electrodes (CRE) have been shown to outperform the spatial resolution achieved with conventional unipolar recordings. Our aimed was to determine the best DFdriver estimator in endocardial electrograms and to assess the BC-ECG capacity of CRE to quantify AF activity non-invasively. METHODS 31 AF episodes were simulated using realistic tridimensional models of the atria electrical activity and torso. Periodogram and autoregressive (AR) spectral estimators were computed and the percentile (P90th, P95th and P98th) to impose on the dominant frequencies (DFs) across whole atria to define the best DFdriver estimator evaluated. The identification of DFdriver on DFs from BC-ECG and unipolar surface signals with conventional disc electrodes was compared. RESULTS The best DFdriver estimator was P95th and AR order 100. BC-ECG signals allowed better detection of AF activity than unipolar signals, with a significantly greater percentage of electrode locations in which DFdriver was identified (p-value 0.0095). CONCLUSIONS The use of BC-ECG signals for body surface Laplacian potential mapping with CRE could be helpful for better AF diagnosis, prognosis and ablation procedures than those with conventional disk electrodes.
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Affiliation(s)
- Gema Prats-Boluda
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Valencia, Spain.
| | - María S Guillem
- ITACA Institute, Universitat Politècnica de València, Valencia, Spain.
| | - Miguel Rodrigo
- CommLab, Engineering Electronic Department, Universitat de València, Valencia, Spain.
| | - Yiyao Ye-Lin
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Valencia, Spain.
| | - Javier Garcia-Casado
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Valencia, Spain.
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de Groot NMS, Shah D, Boyle PM, Anter E, Clifford GD, Deisenhofer I, Deneke T, van Dessel P, Doessel O, Dilaveris P, Heinzel FR, Kapa S, Lambiase PD, Lumens J, Platonov PG, Ngarmukos T, Martinez JP, Sanchez AO, Takahashi Y, Valdigem BP, van der Veen AJ, Vernooy K, Casado-Arroyo Co-Chair R. 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. Europace 2021; 24:313-330. [PMID: 34878119 DOI: 10.1093/europace/euab254] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 09/21/2021] [Indexed: 11/13/2022] Open
Abstract
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.
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Affiliation(s)
- Natasja M S de Groot
- Department of Cardiology, Erasmus University Medical Centre, Rotterdam, Delft University of Technology, Delft the Netherlands
| | - Dipen Shah
- Cardiology Service, University Hospitals Geneva, Geneva, Switzerland
| | - Patrick M Boyle
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
| | - Elad Anter
- Cardiac Electrophysiology Section, Department of Cardiovascular Medicine, Cleveland Clinic, Cleveland, Ohio, USA
| | - Gari D Clifford
- Department of Biomedical Informatics, Emory University, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, USA
| | - Isabel Deisenhofer
- Department of Electrophysiology, German Heart Center Munich and Technical University of Munich, Munich, Germany
| | - Thomas Deneke
- Department of Cardiology, Rhon-klinikum Campus Bad Neustadt, Germany
| | - Pascal van Dessel
- Department of Cardiology, Medisch Spectrum Twente, Twente, the Netherlands
| | - Olaf Doessel
- Karlsruher Institut für Technologie (KIT), Karlsruhe, Germany
| | - Polychronis Dilaveris
- 1st University Department of Cardiology, National & Kapodistrian University of Athens School of Medicine, Hippokration Hospital, Athens, Greece
| | - Frank R Heinzel
- Department of Internal Medicine and Cardiology, Charité-Universitätsmedizin Berlin, Campus Virchow-Klinikum and DZHK (German Centre for Cardiovascular Research), Berlin, Germany
| | - Suraj Kapa
- Department of Cardiology, Mayo Clinic, Rochester, USA
| | | | - Joost Lumens
- Cardiovascular Research Institute Maastricht (CARIM) Maastricht University, Maastricht, the Netherlands
| | - Pyotr G Platonov
- Department of Cardiology, Clinical Sciences, Lund University, Lund, Sweden
| | - Tachapong Ngarmukos
- Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Juan Pablo Martinez
- Aragon Institute of Engineering Research/IIS-Aragon and University of Zaragoza, Zaragoza, Spain, CIBER Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Zaragoza, Spain
| | - Alejandro Olaya Sanchez
- Department of Cardiology, Hospital San José, Fundacion Universitaia de Ciencas de la Salud, Bogota, Colombia
| | - Yoshihide Takahashi
- Department of Cardiovascular Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | - Bruno P Valdigem
- Department of Cardiology, Hospital Rede D'or São Luiz, hospital Albert einstein and Dante pazzanese heart institute, São Paulo, Brasil
| | - Alle-Jan van der Veen
- Department Circuits and Systems, Delft University of Technology, Delft, the Netherlands
| | - Kevin Vernooy
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Centre, Maastricht, the Netherlands
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Cámara-Vázquez MÁ, Hernández-Romero I, Rodrigo M, Alonso-Atienza F, Figuera C, Morgado-Reyes E, Atienza F, Guillem MS, Climent AM, Barquero-Pérez Ó. Electrocardiographic imaging including intracardiac information to achieve accurate global mapping during atrial fibrillation. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102354] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Pulmonary Vein Activity Organization to Determine Atrial Fibrillation Recurrence: Preliminary Data from a Pilot Study. MATHEMATICS 2020. [DOI: 10.3390/math8101813] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Ablation of pulmonary veins has emerged as a key procedure for normal rhythm restoration in atrial fibrillation patients. However, up to half of ablated Atrial fibrillation (AF) patients suffer recurrences during the first year. In this article, simultaneous intra-atrial recordings registered at pulmonary veins previous to the ablation procedure were analyzed. Spatial cross-correlation and transfer entropy were computed in order to estimate spatial organization. Results showed that, in patients with arrhythmia recurrence, pulmonary vein electrical activity was less correlated than in patients that maintained sinus rhythm. Moreover, correlation function between dipoles showed higher delays in patients with AF recurrence. Results with transfer entropy were consistent with spatial cross-correlation measurements. These results show that arrhythmia drivers located at the pulmonary veins are associated with a higher organization of the electrical activations after the ablation of these sites.
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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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Waveform Integrity in Atrial Fibrillation: The Forgotten Issue of Cardiac Electrophysiology. Ann Biomed Eng 2017; 45:1890-1907. [PMID: 28421394 DOI: 10.1007/s10439-017-1832-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2016] [Accepted: 04/05/2017] [Indexed: 01/17/2023]
Abstract
Atrial fibrillation (AF) is the most common arrhythmia in clinical practice with an increasing prevalence of about 15% in the elderly. Despite other alternatives, catheter ablation is currently considered as the first-line therapy for the treatment of AF. This strategy relies on cardiac electrophysiology systems, which use intracardiac electrograms (EGM) as the basis to determine the cardiac structures contributing to sustain the arrhythmia. However, the noise-free acquisition of these recordings is impossible and they are often contaminated by different perturbations. Although suppression of nuisance signals without affecting the original EGM pattern is essential for any other later analysis, not much attention has been paid to this issue, being frequently considered as trivial. The present work introduces the first thorough study on the significant fallout that regular filtering, aimed at reducing acquisition noise, provokes on EGM pattern morphology. This approach has been compared with more refined denoising strategies. Performance has been assessed both in time and frequency by well established parameters for EGM characterization. The study comprised synthesized and real EGMs with unipolar and bipolar recordings. Results reported that regular filtering altered substantially atrial waveform morphology and was unable to remove moderate amounts of noise, thus turning time and spectral characterization of the EGM notably inaccurate. Methods based on Wavelet transform provided the highest ability to preserve EGM morphology with improvements between 20 and beyond 40%, to minimize dominant atrial frequency estimation error with up to 25% reduction, as well as to reduce huge levels of noise with up to 10 dB better reduction. Consequently, these algorithms are recommended as a replacement of regular filtering to avoid significant alterations in the EGMs. This could lead to more accurate and truthful analyses of atrial activity dynamics aimed at understanding and locating the sources of AF.
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Roney CH, Cantwell CD, Qureshi NA, Chowdhury RA, Dupont E, Lim PB, Vigmond EJ, Tweedy JH, Ng FS, Peters NS. Rotor Tracking Using Phase of Electrograms Recorded During Atrial Fibrillation. Ann Biomed Eng 2017; 45:910-923. [PMID: 27921187 PMCID: PMC5362653 DOI: 10.1007/s10439-016-1766-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2016] [Accepted: 11/08/2016] [Indexed: 11/25/2022]
Abstract
Extracellular electrograms recorded during atrial fibrillation (AF) are challenging to interpret due to the inherent beat-to-beat variability in amplitude and duration. Phase mapping represents these voltage signals in terms of relative position within the cycle, and has been widely applied to action potential and unipolar electrogram data of myocardial fibrillation. To date, however, it has not been applied to bipolar recordings, which are commonly acquired clinically. The purpose of this study is to present a novel algorithm for calculating phase from both unipolar and bipolar electrograms recorded during AF. A sequence of signal filters and processing steps are used to calculate phase from simulated, experimental, and clinical, unipolar and bipolar electrograms. The algorithm is validated against action potential phase using simulated data (trajectory centre error <0.8 mm); between experimental multi-electrode array unipolar and bipolar phase; and for wavefront identification in clinical atrial tachycardia. For clinical AF, similar rotational content (R 2 = 0.79) and propagation maps (median correlation 0.73) were measured using either unipolar or bipolar recordings. The algorithm is robust, uses standard signal processing techniques, and accurately quantifies AF wavefronts and sources. Identifying critical sources, such as rotors, in AF, may allow for more accurate targeting of ablation therapy and improved patient outcomes.
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Affiliation(s)
- Caroline H Roney
- Department of Bioengineering, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, F-33600, Pessac-Bordeaux, France
| | - Chris D Cantwell
- Department of Aeronautics, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK.
| | - Norman A Qureshi
- National Heart and Lung Institute, Imperial College London, 4th floor Imperial Centre for Translational and Experimental Medicine, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
| | - Rasheda A Chowdhury
- National Heart and Lung Institute, Imperial College London, 4th floor Imperial Centre for Translational and Experimental Medicine, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
| | - Emmanuel Dupont
- National Heart and Lung Institute, Imperial College London, 4th floor Imperial Centre for Translational and Experimental Medicine, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
| | - Phang Boon Lim
- National Heart and Lung Institute, Imperial College London, 4th floor Imperial Centre for Translational and Experimental Medicine, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
| | - Edward J Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, F-33600, Pessac-Bordeaux, France
| | - Jennifer H Tweedy
- Department of Bioengineering, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK
| | - Fu Siong Ng
- National Heart and Lung Institute, Imperial College London, 4th floor Imperial Centre for Translational and Experimental Medicine, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
| | - Nicholas S Peters
- National Heart and Lung Institute, Imperial College London, 4th floor Imperial Centre for Translational and Experimental Medicine, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
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Grandi E, Maleckar MM. Anti-arrhythmic strategies for atrial fibrillation: The role of computational modeling in discovery, development, and optimization. Pharmacol Ther 2016; 168:126-142. [PMID: 27612549 DOI: 10.1016/j.pharmthera.2016.09.012] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Atrial fibrillation (AF), the most common cardiac arrhythmia, is associated with increased risk of cerebrovascular stroke, and with several other pathologies, including heart failure. Current therapies for AF are targeted at reducing risk of stroke (anticoagulation) and tachycardia-induced cardiomyopathy (rate or rhythm control). Rate control, typically achieved by atrioventricular nodal blocking drugs, is often insufficient to alleviate symptoms. Rhythm control approaches include antiarrhythmic drugs, electrical cardioversion, and ablation strategies. Here, we offer several examples of how computational modeling can provide a quantitative framework for integrating multiscale data to: (a) gain insight into multiscale mechanisms of AF; (b) identify and test pharmacological and electrical therapy and interventions; and (c) support clinical decisions. We review how modeling approaches have evolved and contributed to the research pipeline and preclinical development and discuss future directions and challenges in the field.
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Affiliation(s)
- Eleonora Grandi
- Department of Pharmacology, University of California Davis, Davis, USA.
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11
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Li W, Yang C, Wang Y, Wang D, Chen Y, Wu Z. Several insights into the preprocessing of electrograms in atrial fibrillation for dominant frequency analysis. Biomed Eng Online 2016; 15:38. [PMID: 27067549 PMCID: PMC4828784 DOI: 10.1186/s12938-016-0157-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2016] [Accepted: 04/04/2016] [Indexed: 02/05/2023] Open
Abstract
Background Dominant frequency (DF) analysis of atrial electrograms has become an important method in characterizing atrial fibrillation (AF). As a classic method, Botteron’s approach is widely used in the preprocessing of frequency analysis during AF. It includes three steps: (1) band-pass filtering at 40–250 Hz, (2) absolute value, and (3) low-pass filtering at 20 Hz. This paper aims to expound the necessity and adjustability of each step. Methods and results Unipolar epicardial mapping signals were recorded during AF from eight mongrel dogs with cholinergic AF model. Episodes of these data were randomly selected to evaluate the impact of different pass bands and the necessity of low-pass filtering with 20 Hz cutoff frequency. Each episode of AF signal is 5 s long with a sampling rate of 2 kHz. Simulated electrograms were adopted to discuss the role of taking absolute value. Furthermore, direct spectral analysis method (FFT et al.) is compared with Botteron’s preprocessing approach. According to our statistical analysis, the pass band of 40–250 Hz was not the best, while 20–100 Hz presented the high accuracy rate of DF. From the comparing result of direct FFT without Botteron’s approach we deduced that the rectification of absolute value was meaningful for the fundamental atrial frequency. The final step, 20 Hz low-pass filter can completely be omitted in DF analysis. In consideration of the demand for real-time distribution of DF in clinical or experimental situations, down-sampling method and the impact of ventricular artifacts on DF was also discussed. Conclusion In the actual application of the three preprocessing steps, the pass band selection of band-pass filter can be adjusted and the rectification of taking absolute value is important. Nevertheless, the final step of 20 Hz low-pass filter is totally unnecessary. In real-time signal processing situations, taking down-sampling method and ignoring the ventricular artifacts can also have high performance in DF analysis of atrial electrograms.
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Affiliation(s)
- Wenhai Li
- Department of Electronic Engineering, Fudan University, Shanghai, 200433, China
| | - Cuiwei Yang
- Department of Electronic Engineering, Fudan University, Shanghai, 200433, China. .,Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, 200433, China. .,Shanghai Engineering Research Center of Assistive Devices, Shanghai, 200433, China.
| | - Yanlei Wang
- Department of Electronic Engineering, Fudan University, Shanghai, 200433, China
| | - Dexi Wang
- Department of Electronic Engineering, Fudan University, Shanghai, 200433, China
| | - Ying Chen
- Department of Electronic Science and Engineering, Nanjing University, Nanjing, 210046, Jiangsu, China
| | - Zhong Wu
- Department of Cardiovascular Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
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Vizza P, Curcio A, Tradigo G, Indolfi C, Veltri P. A framework for the atrial fibrillation prediction in electrophysiological studies. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2015; 120:65-76. [PMID: 25929601 DOI: 10.1016/j.cmpb.2015.04.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2014] [Revised: 03/11/2015] [Accepted: 04/07/2015] [Indexed: 06/04/2023]
Abstract
BACKGROUND AND OBJECTIVE Cardiac arrhythmias are disorders in terms of speed or rhythm in the heart's electrical system. Atrial fibrillation (AFib) is the most common sustained arrhythmia that affects a large number of persons. Electrophysiologic study (EPS) procedures are used to study fibrillation in patients; they consist of inducing a controlled fibrillation in surgical room to analyze electrical heart reactions or to decide for implanting medical devices (i.e., pacemaker). Nevertheless, the spontaneous induction may generate an undesired AFib, which may induce risk for patient and thus a critical issue for physicians. We study the unexpected AFib onset, aiming to identify signal patterns occurring in time interval preceding an event of spontaneous (i.e., not inducted) fibrillation. Profiling such signal patterns allowed to design and implement an AFib prediction algorithm able to early identify a spontaneous fibrillation. The objective is to increase the reliability of EPS procedures. METHODS We gathered data signals collected by a General Electric Healthcare's CardioLab electrophysiology recording system (i.e., a polygraph). We extracted superficial and intracavitary cardiac signals regarding 50 different patients studied at the University Magna Graecia Cardiology Department. By studying waveform (i.e., amplitude and energy) of intracavitary signals before the onset of the arrhythmia, we were able to define patterns related to AFib onsets that are side effects of an inducted fibrillation. RESULTS A framework for atrial fibrillation prediction during electrophysiological studies has been developed. It includes a prediction algorithm to alert an upcoming AFib onset. Tests have been performed on an intracavitary cardiac signals data set, related to patients studied in electrophysiological room. Also, results have been validated by the clinicians, proving that the framework can be useful in case of integration with the polygraph, helping physicians in managing and controlling of patient status during EPS.
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Affiliation(s)
- Patrizia Vizza
- Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Italy
| | - Antonio Curcio
- Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Italy
| | - Giuseppe Tradigo
- Department of Computer Science, Modelling, Electronics and Systems Engineering (DIMES), University of Calabria, Italy
| | - Ciro Indolfi
- Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Italy
| | - Pierangelo Veltri
- Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Italy.
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Bojarnejad M, Blake JR, Bourke J, Shepherd E, Murray A, Langley P. Non-Invasive Estimation Of Left Atrial Dominant Frequency In Atrial Fibrillation From Different Electrode Sites: Insight From Body Surface Potential Mapping. J Atr Fibrillation 2014; 7:1131. [PMID: 27957121 DOI: 10.4022/jafib.1131] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2014] [Revised: 08/13/2014] [Accepted: 09/03/2014] [Indexed: 12/16/2022]
Abstract
The dominant driving sources of atrial fibrillation are often found in the left atrium, but the expression of left atrial activation on the body surface is poorly understood. Using body surface potential mapping and simultaneous invasive measurements of left atrial activation our aim was to describe the expression of the left atrial dominant fibrillation frequency across the body surface. 20 patients in atrial fibrillation were studied. The spatial distributions of the dominant atrial fibrillation frequency across anterior and posterior sites on the body surface were quantified. Their relationship with invasive left atrial dominant fibrillation frequency was assessed by linear regression analysis, and the coefficient of determination was calculated for each body surface site. The correlation between intracardiac and body surface dominant frequency was significantly higher with posterior compared with anterior sites (coefficient of determination 67±8% vs 48±2%, p<0.001). The site with largest coefficient of determination was 79.6% (p<0.001) and was a posterior site. In comparison with the site closest to lead V1 it had a coefficient of determination of 23.0% (p=0.033), and with the posterior body surface site closest to lead V9 had a coefficient of determination of 70.3% (p<0.001). Left atrial dominant fibrillation frequency was more closely represented at posterior body surface sites.
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Affiliation(s)
- Marjan Bojarnejad
- Marjan Bojarnejad, Institute of Cellular Medicine, Newcastle University, Medical School, Newcastle upon Tyne, NE2 4HH. James R Blake, Regional Medical Physics Department, Royal Victoria Infirmary, Newcastle upon Tyne, NE4 1LP. John Bourke, Cardiology Department, Freeman Hospital, Newcastle upon Tyne, NE7 7DN. Ewan Shepherd, Cardiology Department, Freeman Hospital, Newcastle upon Tyne, NE7 7DN. Alan Murray, Institute of Cellular Medicine, Newcastle University, Medical School, Newcastle upon Tyne, NE2 4HH. Philip Langley, School of Engineering, University of Hull, Hull, HU6 7RX
| | - James R Blake
- Marjan Bojarnejad, Institute of Cellular Medicine, Newcastle University, Medical School, Newcastle upon Tyne, NE2 4HH. James R Blake, Regional Medical Physics Department, Royal Victoria Infirmary, Newcastle upon Tyne, NE4 1LP. John Bourke, Cardiology Department, Freeman Hospital, Newcastle upon Tyne, NE7 7DN. Ewan Shepherd, Cardiology Department, Freeman Hospital, Newcastle upon Tyne, NE7 7DN. Alan Murray, Institute of Cellular Medicine, Newcastle University, Medical School, Newcastle upon Tyne, NE2 4HH. Philip Langley, School of Engineering, University of Hull, Hull, HU6 7RX
| | - John Bourke
- Marjan Bojarnejad, Institute of Cellular Medicine, Newcastle University, Medical School, Newcastle upon Tyne, NE2 4HH. James R Blake, Regional Medical Physics Department, Royal Victoria Infirmary, Newcastle upon Tyne, NE4 1LP. John Bourke, Cardiology Department, Freeman Hospital, Newcastle upon Tyne, NE7 7DN. Ewan Shepherd, Cardiology Department, Freeman Hospital, Newcastle upon Tyne, NE7 7DN. Alan Murray, Institute of Cellular Medicine, Newcastle University, Medical School, Newcastle upon Tyne, NE2 4HH. Philip Langley, School of Engineering, University of Hull, Hull, HU6 7RX
| | - Ewan Shepherd
- Marjan Bojarnejad, Institute of Cellular Medicine, Newcastle University, Medical School, Newcastle upon Tyne, NE2 4HH. James R Blake, Regional Medical Physics Department, Royal Victoria Infirmary, Newcastle upon Tyne, NE4 1LP. John Bourke, Cardiology Department, Freeman Hospital, Newcastle upon Tyne, NE7 7DN. Ewan Shepherd, Cardiology Department, Freeman Hospital, Newcastle upon Tyne, NE7 7DN. Alan Murray, Institute of Cellular Medicine, Newcastle University, Medical School, Newcastle upon Tyne, NE2 4HH. Philip Langley, School of Engineering, University of Hull, Hull, HU6 7RX
| | - Alan Murray
- Marjan Bojarnejad, Institute of Cellular Medicine, Newcastle University, Medical School, Newcastle upon Tyne, NE2 4HH. James R Blake, Regional Medical Physics Department, Royal Victoria Infirmary, Newcastle upon Tyne, NE4 1LP. John Bourke, Cardiology Department, Freeman Hospital, Newcastle upon Tyne, NE7 7DN. Ewan Shepherd, Cardiology Department, Freeman Hospital, Newcastle upon Tyne, NE7 7DN. Alan Murray, Institute of Cellular Medicine, Newcastle University, Medical School, Newcastle upon Tyne, NE2 4HH. Philip Langley, School of Engineering, University of Hull, Hull, HU6 7RX
| | - Philip Langley
- Marjan Bojarnejad, Institute of Cellular Medicine, Newcastle University, Medical School, Newcastle upon Tyne, NE2 4HH. James R Blake, Regional Medical Physics Department, Royal Victoria Infirmary, Newcastle upon Tyne, NE4 1LP. John Bourke, Cardiology Department, Freeman Hospital, Newcastle upon Tyne, NE7 7DN. Ewan Shepherd, Cardiology Department, Freeman Hospital, Newcastle upon Tyne, NE7 7DN. Alan Murray, Institute of Cellular Medicine, Newcastle University, Medical School, Newcastle upon Tyne, NE2 4HH. Philip Langley, School of Engineering, University of Hull, Hull, HU6 7RX
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