About this Research Topic
This Research Topic is part of the Atrial Fibrillation: Technology for Diagnosis, Monitoring, and Treatment series:
Atrial Fibrillation: Technology for Diagnosis, Monitoring, and Treatment, Volume II
Cardiovascular diseases are the leading cause of death accounting for about 30% of deaths worldwide according to the World Health Organization. Among these diseases, the incidence and prevalence of pathologies related to atrial fibrillation (AF) are reaching epidemic proportions today and are predicted to further surge in the future. AF is to date the most common sustained cardiac rhythm disorder estimated to affect 1.5% - 2% of the general population with a prevalence that increases with age and reaches nearly 10% in octogenarians. AF is poorly understood and its therapy is suboptimal, leading to a major societal burden with immense financial costs associated with the care of patients; the total annual costs of AF care are approximately $7 billion in the US and roughly €13.5 billion in the European Union.
Despite its high incidence and intensive efforts, the ability to treat AF seems not to improve its age-adjusted mortality rate. AF is a multi-factorial disease and the challenges facing improved AF therapies are naturally multi-disciplinary. Our goal is to provide the broad clinical and basic research community original and review papers on technological challenges and advances for improved diagnosis, monitoring and management of AF. We aim to publish a collection of articles that will inform the community on novel technology, which include novel devices, methods and algorithms, with demonstrated or predicted potential to improve the understanding and therapy of AF.
The aim of this special issue is to cover recent advances and novel research trends in the detection, monitoring and management of AF as well as in understanding the mechanisms underlying this disease. We welcome original research and review articles focused on biomedical engineering aspects of research and clinical challenges in AF. Areas covered in this Research Topic may include, but are not limited to:
• Atrial arrhythmia mechanisms: abnormal impulse propagation, electrical remodeling, coupling and fibrosis.
• AF mapping devices and algorithms
• Pacing, defibrillation, and ablation of AF.
• Electrocardiographic markers of AF.
• Analysis and characterization of AF progression.
• Signal processing and machine learning approaches for AF.
• Monitoring of AF in large populations using wearable devices.
• Multi-parametric AF detectors.
• Ablation and non-ablation therapies of AF.
• Anticoagulation technology
• Surgical approaches in AF.
Topic Editor Omer Berenfeld received research grants from Abbott, Medtronic, Inc. and CoreMap, Inc., was co-founder and Scientific Officer of Rhythm Solutions, Inc., is a non-active Research and Development Director for S.A.S. Volta Medical, is a non-active consultant to Acutus Medical, and is a co-founder of Cor-Dx LLC. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
Atrial Fibrillation: Technology for Diagnosis, Monitoring, and Treatment, Volume II
Cardiovascular diseases are the leading cause of death accounting for about 30% of deaths worldwide according to the World Health Organization. Among these diseases, the incidence and prevalence of pathologies related to atrial fibrillation (AF) are reaching epidemic proportions today and are predicted to further surge in the future. AF is to date the most common sustained cardiac rhythm disorder estimated to affect 1.5% - 2% of the general population with a prevalence that increases with age and reaches nearly 10% in octogenarians. AF is poorly understood and its therapy is suboptimal, leading to a major societal burden with immense financial costs associated with the care of patients; the total annual costs of AF care are approximately $7 billion in the US and roughly €13.5 billion in the European Union.
Despite its high incidence and intensive efforts, the ability to treat AF seems not to improve its age-adjusted mortality rate. AF is a multi-factorial disease and the challenges facing improved AF therapies are naturally multi-disciplinary. Our goal is to provide the broad clinical and basic research community original and review papers on technological challenges and advances for improved diagnosis, monitoring and management of AF. We aim to publish a collection of articles that will inform the community on novel technology, which include novel devices, methods and algorithms, with demonstrated or predicted potential to improve the understanding and therapy of AF.
The aim of this special issue is to cover recent advances and novel research trends in the detection, monitoring and management of AF as well as in understanding the mechanisms underlying this disease. We welcome original research and review articles focused on biomedical engineering aspects of research and clinical challenges in AF. Areas covered in this Research Topic may include, but are not limited to:
• Atrial arrhythmia mechanisms: abnormal impulse propagation, electrical remodeling, coupling and fibrosis.
• AF mapping devices and algorithms
• Pacing, defibrillation, and ablation of AF.
• Electrocardiographic markers of AF.
• Analysis and characterization of AF progression.
• Signal processing and machine learning approaches for AF.
• Monitoring of AF in large populations using wearable devices.
• Multi-parametric AF detectors.
• Ablation and non-ablation therapies of AF.
• Anticoagulation technology
• Surgical approaches in AF.
Topic Editor Omer Berenfeld received research grants from Abbott, Medtronic, Inc. and CoreMap, Inc., was co-founder and Scientific Officer of Rhythm Solutions, Inc., is a non-active Research and Development Director for S.A.S. Volta Medical, is a non-active consultant to Acutus Medical, and is a co-founder of Cor-Dx LLC. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
Keywords: atrial disease, risk stratification, electrical remodelling, treatment planning, electrocardiographic markers, machine learning, numerical simulation
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