Influence of Torso Model Complexity on the Noninvasive Localization of Ectopic Ventricular Activity

Abstract Location of premature ectopic ventricular activity was assessed noninvasively in five patients using integral body surface potential maps and inverse solution in terms of a single dipole. Precision of the inverse solution was studied using three different torso models: homogeneous torso model, inhomogeneous torso model including lungs and heart ventricles and inhomogeneous torso model including lungs, heart ventricles and atria, aorta and pulmonary artery. More stable results were obtained using the homogeneous model. However, in some patients the location of the resulting dipole representing the focus of ectopic activity was shifted between solutions using the homogeneous and inhomogeneous models. Comparison of solutions with inhomogeneous torso models did not show significantly different dispersions, but localization of the focus was better when a torso model including atria and arteries was used. The obtained results suggest that presented noninvasive localization of the ectopic focus can be used to shorten the time needed for successful ablation and to increase its success rate.

[1]  R. Westra,et al.  Noninvasive reconstruction of cardiac electrical activity: update on current methods, applications and challenges , 2015, Netherlands Heart Journal.

[2]  M. Tysler,et al.  Noninvasive localization of ectopic ventricular activity using BSPM and different patient torso models , 2015, 2015 IEEE 35th International Conference on Electronics and Nanotechnology (ELNANO).

[3]  Frank Bogun,et al.  Radiofrequency ablation of frequent, idiopathic premature ventricular complexes: comparison with a control group without intervention. , 2007, Heart rhythm.

[4]  M. Cerqueira,et al.  Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. A statement for healthcare professionals from the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association. , 2002, Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology.

[5]  Linwei Wang,et al.  Sensitivity of Noninvasive Cardiac Electrophysiological Imaging to Variations in Personalized Anatomical Modeling , 2015, IEEE Transactions on Biomedical Engineering.

[6]  Xin Zhang,et al.  Three-dimensional myocardial activation imaging in a rabbit model , 2006, IEEE Transactions on Biomedical Engineering.

[7]  M. Tysler,et al.  Influence of Torso Model Accuracy on the Noninvasive Localization of Heart Pathologies , 2013 .

[8]  Y. Rudy,et al.  Electrocardiographic Imaging: II. Effect of Torso Inhomogeneities on Noninvasive Reconstruction of Epicardial Potentials, Electrograms, and Isochrones , 2001, Journal of cardiovascular electrophysiology.

[9]  Dana H. Brooks,et al.  Quantitative comparison of two cardiac electrical imaging methods to localize pacing sites , 2015, 2015 Computing in Cardiology Conference (CinC).

[10]  R Hoekema,et al.  On selecting a body surface mapping procedure. , 1999, Journal of Electrocardiology.

[11]  P. Š. ovíček,et al.  Isopotential ECG Imaging Correctly Identified Endocardial Ectopic Activation Site in the Case of Arrhythmia from Right Ventricular Outflow Tract , 2009 .

[12]  Laura Bear,et al.  Effect of the torso conductivity heterogeneities on the ECGI inverse problem solution , 2015, 2015 Computing in Cardiology Conference (CinC).

[13]  Dana H. Brooks,et al.  Wavefront-based models for inverse electrocardiography , 2006, IEEE Transactions on Biomedical Engineering.

[14]  Peter R Johnston,et al.  Application of robust Generalised Cross-Validation to the inverse problem of electrocardiology , 2016, Comput. Biol. Medicine.

[15]  Milan Tysler,et al.  Portable Device for High Resolution ECG Mapping , 2007 .

[16]  Olaf Dössel,et al.  Ranking the Influence of Tissue Conductivities on Forward-Calculated ECGs , 2010, IEEE Transactions on Biomedical Engineering.

[17]  J. A. Abildskov,et al.  Limited Lead Selection for Estimation of Body Surface Potential Maps in Electrocardiography , 1978, IEEE Transactions on Biomedical Engineering.

[18]  B. He,et al.  Estimation of Global Ventricular Activation Sequences by Noninvasive Three‐Dimensional Electrical Imaging: Validation Studies in a Swine Model During Pacing , 2008, Journal of cardiovascular electrophysiology.

[19]  A. W. M. van der Graaf,et al.  A priori model independent inverse potential mapping: the impact of electrode positioning , 2015, Clinical Research in Cardiology.

[20]  A Prakosa,et al.  Methodology for image-based reconstruction of ventricular geometry for patient-specific modeling of cardiac electrophysiology. , 2014, Progress in biophysics and molecular biology.

[21]  I. L. Freeston,et al.  Modeling of an Active Nerve Fiber in a Finite Volume Conductor and Its Application to the Calculation of Surface Action Potentials , 1979, IEEE Transactions on Biomedical Engineering.

[22]  M. Cerqueira,et al.  Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. A statement for healthcare professionals from the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association. , 2002, Circulation.

[23]  Bin He,et al.  Equivalent Moving Dipole Localization of Cardiac Ectopic Activity in a Swine Model During Pacing , 2010, IEEE Transactions on Information Technology in Biomedicine.

[24]  George A. Kyriacou,et al.  Inverse Problem of ECG for Different Equivalent Cardiac Sources , 2007 .

[25]  M. Cerqueira,et al.  Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart: A statement for healthcare professionals from the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association , 2002, The international journal of cardiovascular imaging.

[26]  L.K. Cheng,et al.  Construction of Patient Specific Geometries Suitable for the Inverse Problem of Electrocardiography , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[27]  I. Maros,et al.  Computational Aspects of Electrocardiological Inverse Solutions , 2015 .