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Identifying Atrial Fibrillation Mechanisms for Personalized Medicine
2021
Journal of Clinical Medicine
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We conclude by describing approaches to improve ablation, including the emergence of several mapping systems that are in use today. ...
Atrial fibrillation (AF) is a major cause of heart failure and stroke. The early maintenance of sinus rhythm has been shown to reduce major cardiovascular endpoints, yet is difficult to achieve. ...
Real-Time Electrogram Analysis for Drivers of Atrial Fibrillation (RADAR): using the coronary sinus as a reference, this system sorts and compiles electrograms recorded in small regions using a standard ...
doi:10.3390/jcm10235679
pmid:34884381
pmcid:PMC8658178
fatcat:ivdm5gvdlvgx3lag7wwid52lzy
How machine learning is impacting research in atrial fibrillation: Implications for risk prediction and future management
2021
Cardiovascular Research
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There has been an exponential growth of artificial intelligence (AI) and machine learning (ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has been mainly driven ...
We also found a fraction of studies using other data modalities, and others centered in aims such as risk prediction, AF management, and others. ...
The study from Ghrissi et al. 119 resulted in a model to automatically identify ablation sites based on their spatiotemporal dispersion, which is the delay of the cardiac activation observed in intracardiac ...
doi:10.1093/cvr/cvab169
pmid:33982064
pmcid:PMC8477792
fatcat:bpq7phl3ejeohkuslbwud3azzm
Future Directions for Mapping Atrial Fibrillation
2022
Arrhythmia & Electrophysiology Review
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Mapping for AF focuses on the identification of regions of interest that may guide management and – in particular – ablation therapy. ...
In patients in whom AF is caused by disorganised waves with no spatial predilection, as proposed in the multiwavelet theory for AF, mapping would be of less benefit. ...
A major focus is to reduce subjectivity in map reading, for which machine learning has had some success. 34 AF sources may fluctuate if they compete with concurrent sites. 23 From optical maps of human ...
doi:10.15420/aer.2021.52
pmid:35734143
pmcid:PMC9194915
fatcat:enap2ba3sje3nowxayayb4bhwi
Editorial: Recent Advances in Understanding the Basic Mechanisms of Atrial Fibrillation Using Novel Computational Approaches
2019
Frontiers in Physiology
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Atrial fibrillation (AF) is the most common sustained heart rhythm disturbance, associated with substantial morbidity and mortality (Andrade et al., 2014) . ...
The poor clinical outcomes are primarily due to a lack of basic understanding of the AF mechanism and quantitative tools to optimize treatment strategies in a clinical setting (Haissaguerre et al., 2007 ...
Meanwhile, machine learning is proving to be a promising tool for helping us to understand AF. ...
doi:10.3389/fphys.2019.01065
pmid:31551796
pmcid:PMC6736575
fatcat:6qoucnypynhynnip45kh3dlsly
Temporal stability and specificity of high bipolar electrogram entropy regions in sustained atrial fibrillation: Implications for mapping
2019
Journal of Electrocardiology
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The potential utility of entropy (En) for atrial fibrillation (AF) mapping has been demonstrated in previous studies by multiple groups, where an association between high bipolar electrogram (EGM) entropy ...
In the current study, we sought to objectively measure the temporal stability and specificity of bipolar EGM entropy in medium to long term recordings using three studies: i) a human basket catheter AF ...
Acknowledgement of funding Computer simulation demonstrated an association between high En and cross propagation regions. ...
doi:10.1016/j.jelectrocard.2018.11.014
pmid:30580097
fatcat:w2d3d2me7vcn5lhnfvzx7rexka
Complexity of Atrial Fibrillation Electrograms Through Nonlinear Signal Analysis: In Silico Approach
[chapter]
2017
Interpreting Cardiac Electrograms - From Skin to Endocardium
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Identification of atrial fibrillation (AF) mechanisms could improve the rate of ablation success. ...
Furthermore, these fibrillatory patterns can be simulated using virtual models. The combination of features using machine learning tools can be used for identifying arrhythmogenic sources of AF. ...
Impact of type of atrial fibrillation and repeat catheter ablation on long-term freedom from atrial fibrillation: Results from a multicenter study. ...
doi:10.5772/intechopen.69475
fatcat:iajkabfuvnehrlsaosrnsewnpq
Computer-Aided Clinical Decision Support Systems for Atrial Fibrillation
[chapter]
2016
Computer-aided Technologies - Applications in Engineering and Medicine
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The chapter begins with a brief description of DSS in general and then introduces DSS that are used for various clinical applications. ...
Finally, a couple of clinical DSS used today in regard with AF are discussed, along with some proposed methods for potential implementation of clinical DSS for detection of AF, prediction of an AF treatment ...
Computer-Aided Clinical Decision Support Systems for Atrial Fibrillation http://dx.doi.org/10.5772/65620 Ablation of Atrial Fibrillation. ...
doi:10.5772/65620
fatcat:sxuifbhucfbmtkdsv5js6xdtf4
Classification of Fibrillation Organisation Using Electrocardiograms to Guide Mechanism-Directed Treatments
2021
Frontiers in Physiology
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: In this study, we proposed a novel ECG classification framework to differentiate fibrillation organisation levels. ...
We investigated whether global fibrillation organisation, a surrogate for fibrillation mechanism, can be determined from electrocardiograms (ECGs) using band-power (BP) feature analysis and machine learning.Methods ...
All authors: critical revision of the article and final approval. ...
doi:10.3389/fphys.2021.712454
pmid:34858198
pmcid:PMC8632359
fatcat:trzhug5ks5h7vevswbh7hu4oju
Computational models of atrial fibrillation: achievements, challenges and perspectives for improving clinical care
2021
Cardiovascular Research
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Despite significant advances in its detection, understanding and management, atrial fibrillation (AF) remains a highly prevalent cardiac arrhythmia with major impact on morbidity and mortality of millions ...
The variability in presentation and wide range of mechanisms involved in initiation, maintenance and progression of AF, as well as its associated adverse outcomes, make the early identification of causal ...
clinical characteristics. 143 For data-driven models used directly in clinical decision-making, there is an increasing emphasis on explainable machine learning models. 163 Explainable models may help ...
doi:10.1093/cvr/cvab138
pmid:33890620
pmcid:PMC8208751
fatcat:xprpebn44zh6rpy35m3vz74iha
Understanding the Beat-to-Beat Variations of P-Waves Morphologies in AF Patients During Sinus Rhythm: A Scoping Review of the Atrial Simulation Studies
2019
Frontiers in Physiology
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and finally the identification of relevant articles based on the reference list of the studies. ...
the pathophysiological mechanisms of arrhythmias, such as Atrial Fibrillation (AF). ...
Zemzemi et al. (2012) proposed one such approach based in machine learning techniques where the simulated ECGs, computed by the consideration of atria and torso model, were used for the training of the ...
doi:10.3389/fphys.2019.00742
pmid:31275161
pmcid:PMC6591370
fatcat:fdo56ru4snfcrp7ax6fk7x6iki
2017 HRS/EHRA/ECAS/APHRS/SOLAECE expert consensus statement on catheter and surgical ablation of atrial fibrillation
2017
Europace
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Acknowledgments The authors acknowledge the support of Jun Dong, MD, PhD; Kan Fang, MD; and Mark Fellman at the Division of Cardiovascular Devices, Center for Devices and Radiological Health, U.S. ...
Food and Drug Administration (FDA) during the preparation of this document. This document does not necessarily represent the opinions, policies, or recommendations of the FDA. e120 Guidelines ...
trial of a membrane-active antiarrhythmic medication. 377, 378, 379 The Medical ANtiarrhythmic Treatment or Radiofrequency Ablation in Paroxysmal Atrial Fibrillation (MANTRA-PAF) 494 trial compared ...
doi:10.1093/europace/eux274
pmid:29016840
pmcid:PMC5834122
fatcat:f2jwwqubl5hchjpzhlf3gr2key
WITHDRAWN: 2017 HRS/EHRA/ECAS/APHRS/SOLAECE expert consensus statement on catheter and surgical ablation of atrial fibrillation
2017
Journal of Arrhythmia
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Atrial Fibrillation Ablation and Autonomic Modulation via Thoracoscopic Surgery (AFACT) study compared the outcomes of thoracoscopic surgical AF ablation in 240 patients with advanced AF at a single European ...
Dominant Frequency Mapping An emergent property of the complex spatiotemporal dynamics is that during AF, the local cycle length (atrial fibrillation cycle length [AFCL]) varies depending on electrode ...
IIb C-EO 417 Indications for surgical ablation of atrial fibrillation
C. ...
doi:10.1016/j.joa.2017.07.001
fatcat:llyr7aswjrbnvamq6irmpc3oqm
2017 HRS/EHRA/ECAS/APHRS/SOLAECE expert consensus statement on catheter and surgical ablation of atrial fibrillation
2017
Heart Rhythm
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#Representative of the Society of Thoracic Surgeons (STS) **Representative of the Canadian Heart Rhythm Society (CHRS) † †Representative of the Japanese Heart Rhythm Society (JHRS) zzRepresentative of ...
(SOLAECE) xRepresentative of the Asia Pacific Heart Rhythm Society (APHRS) kRepresentative of the American College of Cardiology (ACC) {Representative of the European Cardiac Arrhythmia Society (ECAS) ...
Food and Drug Administration (FDA) during the preparation of this document. This document does not necessarily represent the opinions, policies, or recommendations of the FDA. ...
doi:10.1016/j.hrthm.2017.05.012
pmid:28506916
pmcid:PMC6019327
fatcat:jlsfdqyl3rfcjk4y3ygm3x427i
Identification of Spatiotemporal Dispersion Electrograms in Persistent Atrial Fibrillation Ablation Using Maximal Voltage Absolute Values
2021
2020 28th European Signal Processing Conference (EUSIPCO)
unpublished
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This new protocol targets areas of spatiotemporal dispersion (STD) in the atria as potential AF drivers. ...
Atrial fibrillation (AF) is a sustained arrhythmia whose mechanisms are still largely unknown. ...
Identification of Spatiotemporal Dispersion Electrograms in Persistent Atrial Fibrillation Ablation Using Maximal Voltage Absolute Values Amina Ghrissi 1 , Fabien Squara 1,2 , Vicente Zarzoso 1 and Johan ...
doi:10.23919/eusipco47968.2020.9287681
fatcat:z6y22n3fhbg2boygfuyejwlhmq
2020 Index IEEE Transactions on Biomedical Engineering Vol. 67
2020
IEEE Transactions on Biomedical Engineering
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TBME April 2020 1105-1113 Narayanan, A.M., and Bertrand, A., Analysis of Miniaturization Effects and Channel Selection Strategies for EEG Sensor Networks With Application to Auditory Attention Detection ...
EEG Sensor; TBME Jan. 2020 203-212 Nakanishi, M., Wang, Y., Wei, C., Chiang, K., and Jung, T., Facilitating Calibration in High-Speed BCI Spellers via Leveraging Cross-Device Shared Latent Responses; ...
., +, TBME Sept. 2020 2585-2593 ECG-Derived Respiratory Rate in Atrial Fibrillation. ...
doi:10.1109/tbme.2020.3048339
fatcat:y7zxxew27fgerapsnrhh54tm7y
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