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Identifying Atrial Fibrillation Mechanisms for Personalized Medicine

Brototo Deb, Prasanth Ganesan, Ruibin Feng, Sanjiv M. Narayan
2021 Journal of Clinical Medicine  
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

Ivan Olier, Sandra Ortega-Martorell, Mark Pieroni, Gregory Y H Lip
2021 Cardiovascular Research  
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

Junaid AB Zaman, Andrew A Grace, Sanjiv M Narayan
2022 Arrhythmia & Electrophysiology Review  
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

Jichao Zhao, Oleg Aslanidi, Pawel Kuklik, Geoffrey Lee, Gary Tse, Steven Niederer, Edward J. Vigmond
2019 Frontiers in Physiology  
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

Dhani Dharmaprani, Andrew D. McGavigan, Darius Chapman, Rayed Kutlieh, Shivshankar Thanigaimani, Lukah Dykes, Jonathan Kalman, Prashanthan Sanders, Kenneth Pope, Pawel Kuklik, Anand N. Ganesan
2019 Journal of Electrocardiology  
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]

Catalina Tobón, Andrés Orozco‐Duque, Juan P. Ugarte, Miguel Becerra, Javier Saiz
2017 Interpreting Cardiac Electrograms - From Skin to Endocardium  
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]

Prasanth Ganesan, Mark Sterling, Steven Ladavich, Behnaz Ghoraani
2016 Computer-aided Technologies - Applications in Engineering and Medicine  
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

Xinyang Li, Xili Shi, Balvinder S. Handa, Arunashis Sau, Bowen Zhang, Norman A. Qureshi, Zachary I. Whinnett, Nick W. F. Linton, Phang Boon Lim, Prapa Kanagaratnam, Nicholas S. Peters, Fu Siong Ng
2021 Frontiers in Physiology  
: 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

Jordi Heijman, Henry Sutanto, Harry J G M Crijns, Stanley Nattel, Natalia A Trayanova
2021 Cardiovascular Research  
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

Dimitrios Filos, Dimitrios Tachmatzidis, Nicos Maglaveras, Vassilios Vassilikos, Ioanna Chouvarda
2019 Frontiers in Physiology  
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

Hugh Calkins, Gerhard Hindricks, Riccardo Cappato, Young-Hoon Kim, Eduardo B Saad, Luis Aguinaga, Joseph G Akar, Vinay Badhwar, Josep Brugada, John Camm, Peng-Sheng Chen, Shih-Ann Chen (+59 others)
2017 Europace  
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

Hugh Calkins, Gerhard Hindricks, Riccardo Cappato, Young-Hoon Kim, Eduardo B. Saad, Luis Aguinaga, Joseph G. Akar, Vinay Badhwar, Josep Brugada, John Camm, Peng-Sheng Chen, Shih-Ann Chen (+48 others)
2017 Journal of Arrhythmia  
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

Hugh Calkins, Gerhard Hindricks, Riccardo Cappato, Young-Hoon Kim, Eduardo B. Saad, Luis Aguinaga, Joseph G. Akar, Vinay Badhwar, Josep Brugada, John Camm, Peng-Sheng Chen, Shih-Ann Chen (+48 others)
2017 Heart Rhythm  
#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

Amina Ghrissi, Fabien Squara, Johan Montagnat, Vicente Zarzoso
2021 2020 28th European Signal Processing Conference (EUSIPCO)   unpublished
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  
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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