Heart Rhythm Insights Into Structural Remodeling in Atrial Tissue: Timed Automata Approach

The heart rhythm of a person following heart transplantation (HTX) is assumed to display an intrinsic cardiac rhythm because it is significantly less influenced by the autonomic nervous system—the main source of heart rate variability in healthy people. Therefore, such a rhythm provides evidence for arrhythmogenic processes developing, usually silently, in the cardiac tissue. A model is proposed to simulate alterations in the cardiac tissue and to observe the effects of these changes on the resulting heart rhythm. The hybrid automata framework used makes it possible to represent reliably and simulate efficiently both the electrophysiology of a cardiac cell and the tissue organization. The curve fitting method used in the design of the hybrid automaton cycle follows the well-recognized physiological phases of the atrial myocyte membrane excitation. Moreover, knowledge of the complex architecture of the right atrium, the ability of the almost free design of intercellular connections makes the automata approach the only one possible. Two particular aspects are investigated: impairment of the impulse transmission between cells and structural changes in intercellular connections. The first aspect models the observed fatigue of cells due to specific cardiac tissue diseases. The second aspect simulates the increase in collagen deposition with aging. Finally, heart rhythms arising from the model are validated with the sinus heart rhythms recorded in HTX patients. The modulation in the impairment of the impulse transmission between cells reveals qualitatively the abnormally high heart rate variability observed in patients living long after HTX.

[1]  Zhihao Jiang,et al.  Cyber–Physical Modeling of Implantable Cardiac Medical Devices , 2012, Proceedings of the IEEE.

[2]  S. Smolka,et al.  Modelling excitable cells using cycle-linear hybrid automata. , 2008, IET systems biology.

[3]  Halina Dobrzynski,et al.  Computational assessment of the functional role of sinoatrial node exit pathways in the human heart , 2017, PloS one.

[4]  Yoshiki Kuramoto,et al.  Chemical Oscillations, Waves, and Turbulence , 1984, Springer Series in Synergetics.

[5]  G. Breithardt,et al.  Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. , 1996 .

[6]  F. Fenton,et al.  Vortex dynamics in three-dimensional continuous myocardium with fiber rotation: Filament instability and fibrillation. , 1998, Chaos.

[7]  E. Ben-Jacob,et al.  Challenges in network science: Applications to infrastructures, climate, social systems and economics , 2012 .

[8]  Natalia A Trayanova,et al.  Mathematical approaches to understanding and imaging atrial fibrillation: significance for mechanisms and management. , 2014, Circulation research.

[9]  Yoram Rudy,et al.  Simulation of the Undiseased Human Cardiac Ventricular Action Potential: Model Formulation and Experimental Validation , 2011, PLoS Comput. Biol..

[10]  Radu Grosu,et al.  Hybrid Automata as a Unifying Framework for Modeling Excitable Cells , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  Stanley Nattel,et al.  Atrial fibrillation compendium: historical context and detailed translational perspective on an important clinical problem. , 2014, Circulation research.

[12]  Henggui Zhang,et al.  Computer Three-Dimensional Reconstruction of the Atrioventricular Node , 2008, Circulation research.

[13]  C. Luo,et al.  A dynamic model of the cardiac ventricular action potential. I. Simulations of ionic currents and concentration changes. , 1994, Circulation research.

[14]  R. FitzHugh Impulses and Physiological States in Theoretical Models of Nerve Membrane. , 1961, Biophysical journal.

[15]  S. Yoshizawa,et al.  An Active Pulse Transmission Line Simulating Nerve Axon , 1962, Proceedings of the IRE.

[16]  Niels Wessel,et al.  Causality in physiological signals , 2016, Physiological measurement.

[17]  D. Makowiec Pacemaker Rhythm Through Networks of Pacemaker Automata --- a Review , 2014 .

[18]  Tobias Galla,et al.  Mechanisms of stochastic onset and termination of atrial fibrillation studied with a cellular automaton model , 2017, Journal of The Royal Society Interface.

[19]  S. Hastings,et al.  Spatial Patterns for Discrete Models of Diffusion in Excitable Media , 1978 .

[20]  Faron Moller,et al.  A Specification Theory of Real-Time Processes , 2017, Concurrency, Security, and Puzzles.

[21]  Stanley Nattel,et al.  Atrial Remodeling and Atrial Fibrillation: Mechanisms and Implications , 2008, Circulation. Arrhythmia and electrophysiology.

[22]  Alvin Shrier,et al.  Global organization of dynamics in oscillatory heterogeneous excitable media. , 2005, Physical review letters.

[23]  I. Efimov,et al.  28 – Mechanisms of Atrioventricular Nodal Excitability and Propagation , 2014 .

[24]  Craig S. Henriquez,et al.  Continuous models fail to capture details of reentry in fibrotic myocardium , 2016, 2016 Computing in Cardiology Conference (CinC).

[25]  H Zhang,et al.  Models of cardiac tissue electrophysiology: progress, challenges and open questions. , 2011, Progress in biophysics and molecular biology.

[26]  Scott A. Smolka,et al.  Efficient Modeling of Excitable Cells Using Hybrid Automata , 2005 .

[27]  M Delmar,et al.  Phase resetting and entrainment of pacemaker activity in single sinus nodal cells. , 1991, Circulation research.

[28]  Mark Strong,et al.  Bayesian Sensitivity Analysis of a Cardiac Cell Model Using a Gaussian Process Emulator , 2015, PloS one.

[29]  G. Lip,et al.  EHRA/HRS/APHRS/SOLAECE expert consensus on atrial cardiomyopathies: Definition, characterization, and clinical implication. , 2016, Heart rhythm.

[30]  Danuta Makowiec Evolving network – simulation study , 2005 .

[31]  Thomas A. Henzinger,et al.  The theory of hybrid automata , 1996, Proceedings 11th Annual IEEE Symposium on Logic in Computer Science.

[32]  Yoram Rudy,et al.  Rate Dependence and Regulation of Action Potential and Calcium Transient in a Canine Cardiac Ventricular Cell Model , 2004, Circulation.

[33]  Nicholas S. Peters,et al.  Simple Model for Identifying Critical Regions in Atrial Fibrillation , 2014, Physical review letters.

[34]  Ezio Bartocci,et al.  Detecting synchronisation of biological oscillators by model checking , 2010, Theor. Comput. Sci..

[35]  Z. Struzik,et al.  Network tools for tracing the dynamics of heart rate after cardiac transplantation , 2016 .

[36]  Jamie I Vandenberg,et al.  Genes and atrial fibrillation: a new look at an old problem. , 2007, Circulation.

[37]  Jason H T Bates,et al.  Emergence of Complex Behavior: An Interactive Model of Cardiac Excitation Provides a Powerful Tool for Understanding Electric Propagation , 2011, Circulation. Arrhythmia and electrophysiology.

[38]  Amir Bashan,et al.  Network physiology reveals relations between network topology and physiological function , 2012, Nature Communications.

[39]  Henggui Zhang,et al.  Three-Dimensional Computer Model of the Right Atrium Including the Sinoatrial and Atrioventricular Nodes Predicts Classical Nodal Behaviours , 2014, PloS one.

[40]  Vadim V Fedorov,et al.  Anatomy and Electrophysiology of the Human AV Node , 2010, Pacing and clinical electrophysiology : PACE.

[41]  S. Chugh,et al.  Arrhythmias After Heart Transplantation: Mechanisms and Management , 2012, Journal of the American Heart Association.

[42]  Tanmay A. Gokhale,et al.  Modeling dynamics in diseased cardiac tissue: Impact of model choice. , 2017, Chaos.

[43]  Piotr Podziemski,et al.  A simple model of the right atrium of the human heart with the sinoatrial and atrioventricular nodes included , 2013, Journal of Clinical Monitoring and Computing.

[44]  Charles S. Peskin,et al.  Mathematical aspects of heart physiology , 1975 .

[45]  J. Michael Textbook of Medical Physiology , 2005 .

[46]  Gary R. Mirams,et al.  Uncertainty and variability in computational and mathematical models of cardiac physiology , 2016, The Journal of physiology.

[47]  Zbigniew R. Struzik,et al.  Chronographic Imprint of Age-Induced Alterations in Heart Rate Dynamical Organization , 2015, Front. Physiol..

[48]  G. Lip,et al.  EHRA/HRS/APHRS/SOLAECE expert consensus on Atrial cardiomyopathies: Definition, characterisation, and clinical implication☆☆☆ , 2016, Journal of arrhythmia.

[49]  David P. Nickerson,et al.  An overview of the CellML API and its implementation , 2010, BMC Bioinformatics.

[50]  Violeta Monasterio,et al.  Limitations in electrophysiological model development and validation caused by differences between simulations and experimental protocols. , 2017, Progress in biophysics and molecular biology.

[51]  Robert H. Anderson,et al.  Atrial structure and fibres: morphologic bases of atrial conduction. , 2002, Cardiovascular research.

[52]  M. Gruchała,et al.  Visualization of Heart Rate Variability of Long-Term Heart Transplant Patient by Transition Networks: A Case Report , 2016, Front. Physiol..

[53]  Zbigniew R. Struzik,et al.  Dynamical Landscape of Heart Rhythm in Long-Term Heart Transplant Recipients: A Way to Discern Erratic Rhythms , 2018, Front. Physiol..

[54]  Elizabeth Cherry,et al.  Models of cardiac cell , 2008, Scholarpedia.

[55]  D. Sánchez-Quintana,et al.  The terminal crest: morphological features relevant to electrophysiology , 2002, Heart.

[56]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

[57]  Rafael Wisniewski,et al.  Complete Abstractions of Dynamical Systems by Timed Automata , 2013 .

[58]  A. C. F. Coster,et al.  Phase Response of Model Sinoatrial Node Cells , 2003, Annals of Biomedical Engineering.

[59]  D. Sánchez-Quintana,et al.  Sinus node revisited in the era of electroanatomical mapping and catheter ablation , 2005, Heart.