Exploring Mechanisms of Cardiac Rhythm Disturbances Using Novel Computational Methods: Prediction, Classification, and Therapy

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Background

Cardiac rhythm disturbances, including arrhythmias and sudden cardiac death (SCD), represent a major worldwide public health problem, accounting for 15–20 % of all deaths. The electrophysiological mechanisms underlying cardiac arrhythmias and SCD are not completely understood. There is still strong debate whether these cardiac disturbances are caused entirely by disorganized rhythms, sustained by multiple wavefronts, or if they are caused by organized drivers with subsequent wave breaks and fibrillatory conduction.
Computational methods for prediction, classification and therapy of cardiac arrhythmias and SCD are of great interest to improve the clinical outcomes of these disorders. However, considerable challenges persist that limit the efficacy and cost-effectiveness of available methodologies. It is therefore vital to develop computational tools to help better understand the underlying mechanisms and improve effectiveness and efficacy of current therapies.

Recent advances in computational power and applications in bioinspired systems including machine learning, big data and statistical mathematics, allow new and more complex architectures with great potential to outperform traditional methods. Novel computational methods applied in electro anatomic mapping, non-invasive imaging, cardiac clinical and optical mapping, and biophysical computational models will help to describe the mechanisms causing the arrhythmias. A Research Topic compiling these novel computational methods in complex cardiac arrhythmias and SCD may significantly contribute to shed light on clinical applications in prediction, classification and therapy, providing unique and critical importance for management of these significant public health issues.

This Research Topic welcomes review papers and original research on the following themes but is not limited to them. Brief research reports providing important insight on preliminary data with limited number of subjects will also be considered for publication.
• Novel signal processing methods in electro anatomic mapping and cardiac imaging with clinical data;
• New algorithms and analytical approaches in optical and electrical mapping in animal models;
• Computational biophysical modelling exploring arrhythmogenic mechanisms and predicting the therapy outcomes;
• Risk stratification of SCD using features from electrocardiogram and patient-specific data;
• Machine learning, big data and statistical mathematics in cardiac signal

Topic Editor ‪G. André Ng received research fellowships from St. Jude Medical (now Abbott) and speaker fees and honoraria from Biosense Webster. All other Topic Editors declare no competing interests with regards to the Research Topic subject.

Keywords: Cardiac Rhythm Disturbances, Biomedical Signal Processing, Computational modelling, Machine Learning, Prediction and Classification

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