Cardiac Propagation Pattern Mapping With Vector Field for Helping Tachyarrhythmias Diagnosis With Clinical Tridimensional Electro-Anatomical Mapping Tools

Ventricular (VT) and atrial (AT) tachycardias are some of the most common clinical cardiac arrhythmias. For ablation of tachycardia substrates, two clinical diagnosis methods are used: invasive electroanatomical mapping for an accurate diagnosis using electrograms (EGMs) acquired with intracardiac catheters, and localized on the surface mesh of the studied cavities; and noninvasive electrocardiographic imaging (ECGi) for a global view of the arrhythmia, with EGMs mathematically reconstructed from body surface electrocardiograms using 3-D cardio-thoracic surface meshes obtained from CT-scans. In clinics, VT and AT are diagnosed by studying activation time maps that depict the propagation of the activation wavefront on the cardiac mesh. Nevertheless, slow conduction areas—a well-known proarrhythmic feature for tachycardias—and tachycardia specific propagation patterns are not easily identifiable with these maps. Therefore, local characterization of the activation wavefront propagation can be helpful for improving VT and AT diagnoses. The purpose of this study is to develop a method to locally characterize the activation wavefront propagation for clinical data. For this, a conduction velocity vector field is estimated and analyzed using divergence and curl mathematical operators. The workflow was first validated on a simulated database from computer models, and then applied to a clinical database obtained from ECGi to improve AT diagnosis. The results show the relevancy and the efficacy of the proposed method to guide ablation of tachyarrhythmias.

[1]  M. Nash,et al.  Method for quantifiying conduction velocity during ventricular fibrillation. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  Y. Rudy,et al.  Noninvasive Electrocardiographic Imaging , 1999 .

[3]  W. Rappel,et al.  Determining conduction patterns on a sparse electrode grid: Implications for the analysis of clinical arrhythmias. , 2016, Physical review. E.

[4]  Y. Rudy,et al.  Basic mechanisms of cardiac impulse propagation and associated arrhythmias. , 2004, Physiological reviews.

[5]  D. Rosenbaum,et al.  Role of Structural Barriers in the Mechanism of Alternans-Induced Reentry , 2000, Circulation research.

[6]  Laura Bear,et al.  Combined signal averaging and electrocardiographic imaging method to non-invasively identify atrial and ventricular tachycardia mechanisms , 2016, 2016 Computing in Cardiology Conference (CinC).

[7]  Martyn P. Nash,et al.  Evidence for Multiple Mechanisms in Human Ventricular Fibrillation , 2006, Circulation.

[8]  Mark Potse,et al.  Spatially Coherent Activation Maps for Electrocardiographic Imaging , 2017, IEEE Transactions on Biomedical Engineering.

[9]  S. Swiryn,et al.  A method for determining high-resolution activation time delays in unipolar cardiac mapping , 1996, IEEE Transactions on Biomedical Engineering.

[10]  R. Ideker,et al.  Estimation of conduction velocity vector fields from epicardial mapping data , 1998, IEEE Transactions on Biomedical Engineering.

[11]  Spencer J. Sherwin,et al.  Techniques for automated local activation time annotation and conduction velocity estimation in cardiac mapping , 2015, Comput. Biol. Medicine.

[12]  José Jalife,et al.  Role of Conduction Velocity Restitution and Short-Term Memory in the Development of Action Potential Duration Alternans in Isolated Rabbit Hearts , 2008, Circulation.

[13]  S Nattel,et al.  Ionic targets for drug therapy and atrial fibrillation-induced electrical remodeling: insights from a mathematical model. , 1999, Cardiovascular research.

[14]  S. Labarthe,et al.  Global and Directional Activation maps for cardiac mapping in electrophysiology , 2012, 2012 Computing in Cardiology.

[15]  Dana H. Brooks,et al.  Estimation of Cardiac Conduction Velocities Using Small Data Sets , 2004, Annals of Biomedical Engineering.

[16]  Tamara N. Fitzgerald,et al.  Identification of cardiac rhythm features by mathematical analysis of vector fields , 2005, IEEE Transactions on Biomedical Engineering.

[17]  Ashok J. Shah,et al.  Validation of novel 3-dimensional electrocardiographic mapping of atrial tachycardias by invasive mapping and ablation: a multicenter study. , 2013, Journal of the American College of Cardiology.

[18]  G Plank,et al.  Computational tools for modeling electrical activity in cardiac tissue. , 2003, Journal of electrocardiology.

[19]  M. Courtemanche,et al.  Ionic mechanisms underlying human atrial action potential properties: insights from a mathematical model. , 1998, The American journal of physiology.

[20]  J. Jalife,et al.  High-Resolution Optical Mapping of the Right Bundle Branch in Connexin40 Knockout Mice Reveals Slow Conduction in the Specialized Conduction System , 2000, Circulation research.

[21]  Rémi Dubois,et al.  Cardiac electrical dyssynchrony is accurately detected by noninvasive electrocardiographic imaging. , 2018, Heart rhythm.

[22]  Raymond E. Ideker,et al.  Estimation of 3-D conduction velocity vector fields from cardiac mapping data , 1998, Computers in Cardiology 1998. Vol. 25 (Cat. No.98CH36292).