Research in cardiac arrhythmias and cardiac electrophysiology is increasingly dependent on computational methods to relate experimental and clinical observations to the underlying mechanisms. Computational methods are essential to process experimental data (e.g., from optical mapping and body-surface potential mapping) and to model the biophysical processes underlying electrophysiology (e.g., inverse solutions of electrocardiography and cell and whole-heart modeling). Processing signals from experimental and clinical recordings can serve to elucidate electrophysiological properties across temporal, spatial and frequency domains. Computational modeling provides an understanding of fundamental aspects of cardiac electrophysiology from a theoretical standpoint. Additionally, patient-specific computational models are increasingly employed to interpret experimental and clinical observations to provide improved approximations of cardiac electrical behavior at an individual level. Advances in computational methodology are thus essential to obtain new insights in cardiac electrophysiology and arrhythmias.
This Research Topic addresses innovations in computational methodology and the synergy they bring with experimental and clinical data for cardiac electrophysiology. The primary aim of the Topic is to highlight state-of-the-art computational advancements that involve experimental or clinical signal analysis for optical and electrical mapping, biophysical modeling of electrophysiology, and inverse solutions. This includes advancements for (clinical) mapping systems (e.g., improved electrogram annotation), improved accuracy of inverse solutions and optical mapping, better understanding of electromechanical interactions, innovation of computational cell/heart models, or new experimental data that support such advancements. Moreover, computational and analytical approaches that aim to improve personalized diagnostics and therapy will also be sought after. Importantly, this Research Topic focuses on research where advancements in computational methods improve synergy with data, and may consequently bring new insights in electrophysiology and arrhythmias.
This research topic has the following computational methodology in scope:
• Body-surface potential mapping and electrocardiographic imaging
• Optical mapping of electrophysiology
• Electrogram processing for invasive and noninvasive recordings
• Cellular and whole-heart modeling of electrophysiology
• Studying the impact of mechanical deformation on electrophysiology
For all of these, relating the advancement in methodology to the synergy with experimental or clinical data in electrophysiology is highly recommended.
Topic editor Matthijs Cluitmans is employed by Phillips Research. Topic Editor Gernot Plank is the co-founder of NumeriCor. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
Research in cardiac arrhythmias and cardiac electrophysiology is increasingly dependent on computational methods to relate experimental and clinical observations to the underlying mechanisms. Computational methods are essential to process experimental data (e.g., from optical mapping and body-surface potential mapping) and to model the biophysical processes underlying electrophysiology (e.g., inverse solutions of electrocardiography and cell and whole-heart modeling). Processing signals from experimental and clinical recordings can serve to elucidate electrophysiological properties across temporal, spatial and frequency domains. Computational modeling provides an understanding of fundamental aspects of cardiac electrophysiology from a theoretical standpoint. Additionally, patient-specific computational models are increasingly employed to interpret experimental and clinical observations to provide improved approximations of cardiac electrical behavior at an individual level. Advances in computational methodology are thus essential to obtain new insights in cardiac electrophysiology and arrhythmias.
This Research Topic addresses innovations in computational methodology and the synergy they bring with experimental and clinical data for cardiac electrophysiology. The primary aim of the Topic is to highlight state-of-the-art computational advancements that involve experimental or clinical signal analysis for optical and electrical mapping, biophysical modeling of electrophysiology, and inverse solutions. This includes advancements for (clinical) mapping systems (e.g., improved electrogram annotation), improved accuracy of inverse solutions and optical mapping, better understanding of electromechanical interactions, innovation of computational cell/heart models, or new experimental data that support such advancements. Moreover, computational and analytical approaches that aim to improve personalized diagnostics and therapy will also be sought after. Importantly, this Research Topic focuses on research where advancements in computational methods improve synergy with data, and may consequently bring new insights in electrophysiology and arrhythmias.
This research topic has the following computational methodology in scope:
• Body-surface potential mapping and electrocardiographic imaging
• Optical mapping of electrophysiology
• Electrogram processing for invasive and noninvasive recordings
• Cellular and whole-heart modeling of electrophysiology
• Studying the impact of mechanical deformation on electrophysiology
For all of these, relating the advancement in methodology to the synergy with experimental or clinical data in electrophysiology is highly recommended.
Topic editor Matthijs Cluitmans is employed by Phillips Research. Topic Editor Gernot Plank is the co-founder of NumeriCor. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
