1,036 research outputs found

    Validation and Opportunities of Electrocardiographic Imaging: From Technical chievements to Clinical Applications

    Get PDF
    [EN] Electrocardiographic imaging (ECGI) reconstructs the electrical activity of the heart from a dense array of body-surface electrocardiograms and a patient-specific heart-torso geometry. Depending on how it is formulated, ECGI allows the reconstruction of the activation and recovery sequence of the heart, the origin of premature beats or tachycardia, the anchors/hotspots of re-entrant arrhythmias and other electrophysiological quantities of interest. Importantly, these quantities are directly and non-invasively reconstructed in a digitized model of the patient's three-dimensional heart, which has led to clinical interest in ECGI's ability to personalize diagnosis and guide therapy. Despite considerable development over the last decades, validation of ECGI is challenging. Firstly, results depend considerably on implementation choices, which are necessary to deal with ECGI's ill-posed character. Secondly, it is challenging to obtain (invasive) ground truth data of high quality. In this review, we discuss the current status of ECGI validation as well as the major challenges remaining for complete adoption of ECGI in clinical practice. Specifically, showing clinical benefit is essential for the adoption of ECGI. Such benefit may lie in patient outcome improvement, workflow improvement, or cost reduction. Future studies should focus on these aspects to achieve broad adoption of ECGI, but only after the technical challenges have been solved for that specific application/pathology. We propose 'best' practices for technical validation and highlight collaborative efforts recently organized in this field. Continued interaction between engineers, basic scientists, and physicians remains essential to find a hybrid between technical achievements, pathological mechanisms insights, and clinical benefit, to evolve this powerful technique toward a useful role in clinical practice.This study received financial support from the Hein Wellens Fonds, the Cardiovascular Research and Training Institute (CVRTI), the Nora Eccles Treadwell Foundation, the National Institute of General Medical Sciences of the National Institutes of Health (P41GM103545), the National Institutes of Health (NIH HL080093), the French government as part of the Investments of the Future program managed by the National Research Agency (ANR-10-IAHU-04), from the VEGA Grant Agency in Slovakia (2/0071/16), from the Slovak Research and Development Agency (APVV-14-0875), the Fondo Europeo de Desarrollo Regional (FEDER), the Instituto de Salud Carlos III (PI17/01106) and from Conselleria d'Educacio, Investigacio, Cultura i Esport de la Generalitat Valenciana (AICO/2018/267) and NIH grant (HL125998) and National Science Foundation (ACI-1350374).Cluitmans, M.; Brooks, D.; Macleod, RS.; Dossel, O.; Guillem Sánchez, MS.; Van Dam, P.; Svehlikova, J.... (2018). Validation and Opportunities of Electrocardiographic Imaging: From Technical chievements to Clinical Applications. Frontiers in Physiology. 9. https://doi.org/10.3389/fphys.2018.01305S9Andrews, C. M., Srinivasan, N. T., Rosmini, S., Bulluck, H., Orini, M., Jenkins, S., … Rudy, Y. (2017). Electrical and Structural Substrate of Arrhythmogenic Right Ventricular Cardiomyopathy Determined Using Noninvasive Electrocardiographic Imaging and Late Gadolinium Magnetic Resonance Imaging. Circulation: Arrhythmia and Electrophysiology, 10(7). doi:10.1161/circep.116.005105Aras, K., Good, W., Tate, J., Burton, B., Brooks, D., Coll-Font, J., … MacLeod, R. (2015). Experimental Data and Geometric Analysis Repository—EDGAR. Journal of Electrocardiology, 48(6), 975-981. doi:10.1016/j.jelectrocard.2015.08.008Austen, W., Edwards, J., Frye, R., Gensini, G., Gott, V., Griffith, L., … Roe, B. (1975). A reporting system on patients evaluated for coronary artery disease. Report of the Ad Hoc Committee for Grading of Coronary Artery Disease, Council on Cardiovascular Surgery, American Heart Association. Circulation, 51(4), 5-40. doi:10.1161/01.cir.51.4.5Bayley, R. H., & Berry, P. M. (1962). The electrical field produced by the eccentric current dipole in the nonhomogeneous conductor. American Heart Journal, 63(6), 808-820. doi:10.1016/0002-8703(62)90065-0Bear, L. R., Huntjens, P. R., Walton, R. D., Bernus, O., Coronel, R., & Dubois, R. (2018). Cardiac electrical dyssynchrony is accurately detected by noninvasive electrocardiographic imaging. Heart Rhythm, 15(7), 1058-1069. doi:10.1016/j.hrthm.2018.02.024Bear, L. R., LeGrice, I. J., Sands, G. B., Lever, N. A., Loiselle, D. S., Paterson, D. J., … Smaill, B. H. (2018). How Accurate Is Inverse Electrocardiographic Mapping? Circulation: Arrhythmia and Electrophysiology, 11(5). doi:10.1161/circep.117.006108Berger, T., Fischer, G., Pfeifer, B., Modre, R., Hanser, F., Trieb, T., … Hintringer, F. (2006). Single-Beat Noninvasive Imaging of Cardiac Electrophysiology of Ventricular Pre-Excitation. Journal of the American College of Cardiology, 48(10), 2045-2052. doi:10.1016/j.jacc.2006.08.019Berger, T., Pfeifer, B., Hanser, F. F., Hintringer, F., Fischer, G., Netzer, M., … Seger, M. (2011). Single-Beat Noninvasive Imaging of Ventricular Endocardial and Epicardial Activation in Patients Undergoing CRT. PLoS ONE, 6(1), e16255. doi:10.1371/journal.pone.0016255Dubois, R., Pashaei, A., Duchateau, J., & Vigmond, E. (2016). Evaluation of Combined Noninvasive Electrocardiographic Imaging and Phase Mapping approach for Atrial Fibrillation: A Simulation Study. 2016 Computing in Cardiology Conference (CinC). doi:10.22489/cinc.2016.037-540Duchateau, J., Potse, M., & Dubois, R. (2017). Spatially Coherent Activation Maps for Electrocardiographic Imaging. IEEE Transactions on Biomedical Engineering, 64(5), 1149-1156. doi:10.1109/tbme.2016.2593003Erem, B., Brooks, D. H., van Dam, P. M., Stinstra, J. G., & MacLeod, R. S. (2011). Spatiotemporal estimation of activation times of fractionated ECGs on complex heart surfaces. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. doi:10.1109/iembs.2011.6091455Erem, B., van Dam, P. M., & Brooks, D. H. (2014). Identifying Model Inaccuracies and Solution Uncertainties in Noninvasive Activation-Based Imaging of Cardiac Excitation Using Convex Relaxation. IEEE Transactions on Medical Imaging, 33(4), 902-912. doi:10.1109/tmi.2014.2297952Erkapic, D., & Neumann, T. (2015). Ablation of Premature Ventricular Complexes Exclusively Guided by Three-Dimensional Noninvasive Mapping. Cardiac Electrophysiology Clinics, 7(1), 109-115. doi:10.1016/j.ccep.2014.11.010Everett, T. H., Lai-Chow Kok, Vaughn, R. H., Moorman, R., & Haines, D. E. (2001). Frequency domain algorithm for quantifying atrial fibrillation organization to increase defibrillation efficacy. IEEE Transactions on Biomedical Engineering, 48(9), 969-978. doi:10.1109/10.942586Faes, L., & Ravelli, F. (2007). A morphology-based approach to the evaluation of atrial fibrillation organization. IEEE Engineering in Medicine and Biology Magazine, 26(4), 59-67. doi:10.1109/memb.2007.384097Fitzpatrick, A. P., Gonzales, R. P., Lesh, M. D., odin, G. W., Lee, R. J., & Scheinman, M. M. (1994). New algorithm for the localization of accessory atrioventricular connections using a baseline electrocardiogram. Journal of the American College of Cardiology, 23(1), 107-116. doi:10.1016/0735-1097(94)90508-8Geselowitz, D. B. (1989). On the theory of the electrocardiogram. Proceedings of the IEEE, 77(6), 857-876. doi:10.1109/5.29327Geselowitz, D. B. (1992). Description of cardiac sources in anisotropic cardiac muscle. Journal of Electrocardiology, 25, 65-67. doi:10.1016/0022-0736(92)90063-6Ghanem, R. N., Jia, P., Ramanathan, C., Ryu, K., Markowitz, A., & Rudy, Y. (2005). Noninvasive Electrocardiographic Imaging (ECGI): Comparison to intraoperative mapping in patients. Heart Rhythm, 2(4), 339-354. doi:10.1016/j.hrthm.2004.12.022Ghosh, S., Rhee, E. K., Avari, J. N., Woodard, P. K., & Rudy, Y. (2008). Cardiac Memory in Patients With Wolff-Parkinson-White Syndrome. Circulation, 118(9), 907-915. doi:10.1161/circulationaha.108.781658Ghosh, S., Silva, J. N. A., Canham, R. M., Bowman, T. M., Zhang, J., Rhee, E. K., … Rudy, Y. (2011). Electrophysiologic substrate and intraventricular left ventricular dyssynchrony in nonischemic heart failure patients undergoing cardiac resynchronization therapy. Heart Rhythm, 8(5), 692-699. doi:10.1016/j.hrthm.2011.01.017Grace, A., Verma, A., & Willems, S. (2017). Dipole Density Mapping of Atrial Fibrillation. European Heart Journal, 38(1), 5-9. doi:10.1093/eurheartj/ehw585Dorset, D. L. (1996). Electron crystallography. Acta Crystallographica Section B Structural Science, 52(5), 753-769. doi:10.1107/s0108768196005599Haissaguerre, M., Hocini, M., Denis, A., Shah, A. J., Komatsu, Y., Yamashita, S., … Dubois, R. (2014). Driver Domains in Persistent Atrial Fibrillation. Circulation, 130(7), 530-538. doi:10.1161/circulationaha.113.005421HAISSAGUERRE, M., HOCINI, M., SHAH, A. J., DERVAL, N., SACHER, F., JAIS, P., & DUBOIS, R. (2013). Noninvasive Panoramic Mapping of Human Atrial Fibrillation Mechanisms: A Feasibility Report. Journal of Cardiovascular Electrophysiology, 24(6), 711-717. doi:10.1111/jce.12075Han, C., Pogwizd, S. M., Killingsworth, C. R., & He, B. (2011). Noninvasive imaging of three-dimensional cardiac activation sequence during pacing and ventricular tachycardia. Heart Rhythm, 8(8), 1266-1272. doi:10.1016/j.hrthm.2011.03.014Bin He, Guanglin Li, & Xin Zhang. (2003). Noninvasive imaging of cardiac transmembrane potentials within three-dimensional myocardium by means of a realistic geometry anisotropic heart model. IEEE Transactions on Biomedical Engineering, 50(10), 1190-1202. doi:10.1109/tbme.2003.817637Bin He, & Dongsheng Wu. (2001). Imaging and visualization of 3-D cardiac electric activity. IEEE Transactions on Information Technology in Biomedicine, 5(3), 181-186. doi:10.1109/4233.945288Horáček, B. M., Sapp, J. L., Penney, C. J., Warren, J. W., & Wang, J. J. (2011). Comparison of epicardial potential maps derived from the 12-lead electrocardiograms with scintigraphic images during controlled myocardial ischemia. Journal of Electrocardiology, 44(6), 707-712. doi:10.1016/j.jelectrocard.2011.08.009Horáček, B. M., Wang, L., Dawoud, F., Xu, J., & Sapp, J. L. (2015). Noninvasive electrocardiographic imaging of chronic myocardial infarct scar. Journal of Electrocardiology, 48(6), 952-958. doi:10.1016/j.jelectrocard.2015.08.035Jamil-Copley, S., Vergara, P., Carbucicchio, C., Linton, N., Koa-Wing, M., Luther, V., … Kanagaratnam, P. (2015). Application of Ripple Mapping to Visualize Slow Conduction Channels Within the Infarct-Related Left Ventricular Scar. Circulation: Arrhythmia and Electrophysiology, 8(1), 76-86. doi:10.1161/circep.114.001827Janssen, A. M., Potyagaylo, D., Dössel, O., & Oostendorp, T. F. (2017). Assessment of the equivalent dipole layer source model in the reconstruction of cardiac activation times on the basis of BSPMs produced by an anisotropic model of the heart. Medical & Biological Engineering & Computing, 56(6), 1013-1025. doi:10.1007/s11517-017-1715-xKnecht, S., Sohal, M., Deisenhofer, I., Albenque, J.-P., Arentz, T., Neumann, T., … Rostock, T. (2017). Multicentre evaluation of non-invasive biatrial mapping for persistent atrial fibrillation ablation: the AFACART study. EP Europace, 19(8), 1302-1309. doi:10.1093/europace/euw168Kuck, K.-H., Schaumann, A., Eckardt, L., Willems, S., Ventura, R., Delacrétaz, E., … Hansen, P. S. (2010). Catheter ablation of stable ventricular tachycardia before defibrillator implantation in patients with coronary heart disease (VTACH): a multicentre randomised controlled trial. The Lancet, 375(9708), 31-40. doi:10.1016/s0140-6736(09)61755-4Identification of Rotors during Human Atrial Fibrillation Using Contact Mapping and Phase Singularity Detection: Technical Considerations. (2017). IEEE Transactions on Biomedical Engineering, 64(2), 310-318. doi:10.1109/tbme.2016.2554660Leong, K. M. W., Ng, F. S., Yao, C., Roney, C., Taraborrelli, P., Linton, N. W. F., … Varnava, A. M. (2017). ST-Elevation Magnitude Correlates With Right Ventricular Outflow Tract Conduction Delay in Type I Brugada ECG. Circulation: Arrhythmia and Electrophysiology, 10(10). doi:10.1161/circep.117.005107Chenguang Liu, Eggen, M. D., Swingen, C. M., Iaizzo, P. A., & Bin He. (2012). Noninvasive Mapping of Transmural Potentials During Activation in Swine Hearts From Body Surface Electrocardiograms. IEEE Transactions on Medical Imaging, 31(9), 1777-1785. doi:10.1109/tmi.2012.2202914MacLeod, R. S., Ni, Q., Punske, B., Ershler, P. R., Yilmaz, B., & Taccardi, B. (2000). Effects of heart position on the body-surface electrocardiogram. Journal of Electrocardiology, 33, 229-237. doi:10.1054/jelc.2000.20357Metzner, A., Wissner, E., Tsyganov, A., Kalinin, V., Schlüter, M., Lemes, C., … Kuck, K.-H. (2017). Noninvasive phase mapping of persistent atrial fibrillation in humans: Comparison with invasive catheter mapping. Annals of Noninvasive Electrocardiology, 23(4), e12527. doi:10.1111/anec.12527Modre, R., Tilg, B., Fischer, G., Hanser, F., Messnarz, B., Seger, M., … Roithinger, F. X. (2003). Atrial Noninvasive Activation Mapping of Paced Rhythm Data. Journal of Cardiovascular Electrophysiology, 14(7), 712-719. doi:10.1046/j.1540-8167.2003.02558.xNarayan, S. M., Krummen, D. E., Shivkumar, K., Clopton, P., Rappel, W.-J., & Miller, J. M. (2012). Treatment of Atrial Fibrillation by the Ablation of Localized Sources. Journal of the American College of Cardiology, 60(7), 628-636. doi:10.1016/j.jacc.2012.05.022NG, J., KADISH, A. H., & GOLDBERGER, J. J. (2007). Technical Considerations for Dominant Frequency Analysis. Journal of Cardiovascular Electrophysiology, 18(7), 757-764. doi:10.1111/j.1540-8167.2007.00810.xOosterhoff, P., Meijborg, V. M. F., van Dam, P. M., van Dessel, P. F. H. M., Belterman, C. N. W., Streekstra, G. J., … Oostendorp, T. F. (2016). Experimental Validation of Noninvasive Epicardial and Endocardial Activation Imaging. Circulation: Arrhythmia and Electrophysiology, 9(8). doi:10.1161/circep.116.004104Oster, H. S., Taccardi, B., Lux, R. L., Ershler, P. R., & Rudy, Y. (1997). Noninvasive Electrocardiographic Imaging. Circulation, 96(3), 1012-1024. doi:10.1161/01.cir.96.3.1012Oster, H. S., Taccardi, B., Lux, R. L., Ershler, P. R., & Rudy, Y. (1998). Electrocardiographic Imaging. Circulation, 97(15), 1496-1507. doi:10.1161/01.cir.97.15.1496PEDRÓN-TORRECILLA, J., RODRIGO, M., CLIMENT, A. M., LIBEROS, A., PÉREZ-DAVID, E., BERMEJO, J., … GUILLEM, M. S. (2016). Noninvasive Estimation of Epicardial Dominant High-Frequency Regions During Atrial Fibrillation. Journal of Cardiovascular Electrophysiology, 27(4), 435-442. doi:10.1111/jce.12931Ploux, S., Lumens, J., Whinnett, Z., Montaudon, M., Strom, M., Ramanathan, C., … Bordachar, P. (2013). Noninvasive Electrocardiographic Mapping to Improve Patient Selection for Cardiac Resynchronization Therapy. Journal of the American College of Cardiology, 61(24), 2435-2443. doi:10.1016/j.jacc.2013.01.093Potyagaylo, D., Segel, M., Schulze, W. H. W., & Dössel, O. (2013). Noninvasive Localization of Ectopic Foci: A New Optimization Approach for Simultaneous Reconstruction of Transmembrane Voltages and Epicardial Potentials. Lecture Notes in Computer Science, 166-173. doi:10.1007/978-3-642-38899-6_20Punshchykova, O., Švehlíková, J., Tyšler, M., Grünes, R., Sedova, K., Osmančík, P., … Kneppo, P. (2016). Influence of Torso Model Complexity on the Noninvasive Localization of Ectopic Ventricular Activity. Measurement Science Review, 16(2), 96-102. doi:10.1515/msr-2016-0013RAMANATHAN, C., & RUDY, Y. (2001). Electrocardiographic Imaging: II. Effect of Torso Inhomogeneities on Noninvasive Reconstruction of Epicardial Potentials, Electrograms, and Isochrones. Journal of Cardiovascular Electrophysiology, 12(2), 241-252. doi:10.1046/j.1540-8167.2001.00241.xReddy, V. Y., Reynolds, M. R., Neuzil, P., Richardson, A. W., Taborsky, M., Jongnarangsin, K., … Josephson, M. E. (2007). Prophylactic Catheter Ablation for the Prevention of Defibrillator Therapy. New England Journal of Medicine, 357(26), 2657-2665. doi:10.1056/nejmoa065457Rodrigo, M., Climent, A. M., Liberos, A., Fernández-Avilés, F., Berenfeld, O., Atienza, F., & Guillem, M. S. (2017). Technical Considerations on Phase Mapping for Identification of Atrial Reentrant Activity in Direct- and Inverse-Computed Electrograms. Circulation: Arrhythmia and Electrophysiology, 10(9). doi:10.1161/circep.117.005008ROTEN, L., PEDERSEN, M., PASCALE, P., SHAH, A., ELIAUTOU, S., SCHERR, D., … HAÏSSAGUERRE, M. (2012). Noninvasive Electrocardiographic Mapping for Prediction of Tachycardia Mechanism and Origin of Atrial Tachycardia Following Bilateral Pulmonary Transplantation. Journal of Cardiovascular Electrophysiology, 23(5), 553-555. doi:10.1111/j.1540-8167.2011.02250.xRudy, Y. (2013). Noninvasive Electrocardiographic Imaging of Arrhythmogenic Substrates in Humans. Circulation Research, 112(5), 863-874. doi:10.1161/circresaha.112.279315Ghosh, S., Avari, J. N., Rhee, E. K., Woodard, P. K., & Rudy, Y. (2008). Noninvasive electrocardiographic imaging (ECGI) of epicardial activation before and after catheter ablation of the accessory pathway in a patient with Ebstein anomaly. Heart Rhythm, 5(6), 857-860. doi:10.1016/j.hrthm.2008.03.011Rudy, Y., Plonsey, R., & Liebman, J. (1979). The effects of variations in conductivity and geometrical parameters on the electrocardiogram, using an eccentric spheres model. Circulation Research, 44(1), 104-111. doi:10.1161/01.res.44.1.104SALINET, J. L., TUAN, J. H., SANDILANDS, A. J., STAFFORD, P. J., SCHLINDWEIN, F. S., & NG, G. A. (2013). Distinctive Patterns of Dominant Frequency Trajectory Behavior in Drug-Refractory Persistent Atrial Fibrillation: Preliminary Characterization of Spatiotemporal Instability. Journal of Cardiovascular Electrophysiology, 25(4), 371-379. doi:10.1111/jce.12331Dalu, Y. (1978). Relating the multipole moments of the heart to activated parts of the epicardium and endocardium. Annals of Biomedical Engineering, 6(4), 492-505. doi:10.1007/bf02584552Sánchez, C., Bueno-Orovio, A., Pueyo, E., & Rodríguez, B. (2017). Atrial Fibrillation Dynamics and Ionic Block Effects in Six Heterogeneous Human 3D Virtual Atria with Distinct Repolarization Dynamics. Frontiers in Bioengineering and Biotechnology, 5. doi:10.3389/fbioe.2017.00029Sanders, P., Berenfeld, O., Hocini, M., Jaïs, P., Vaidyanathan, R., Hsu, L.-F., … Haïssaguerre, M. (2005). Spectral Analysis Identifies Sites of High-Frequency Activity Maintaining Atrial Fibrillation in Humans. Circulation, 112(6), 789-797. doi:10.1161/circulationaha.104.517011Sapp, J. L., Bar-Tal, M., Howes, A. J., Toma, J. E., El-Damaty, A., Warren, J. W., … Horáček, B. M. (2017). Real-Time Localization of Ventricular Tachycardia Origin From the 12-Lead Electrocardiogram. JACC: Clinical Electrophysiology, 3(7), 687-699. doi:10.1016/j.jacep.2017.02.024Sapp, J. L., Dawoud, F., Clements, J. C., & Horáček, B. M. (2012). Inverse Solution Mapping of Epicardial Potentials. Circulation: Arrhythmia and Electrophysiology, 5(5), 1001-1009. doi:10.1161/circep.111.970160Sapp, J. L., Wells, G. A., Parkash, R., Stevenson, W. G., Blier, L., Sarrazin, J.-F., … Tang, A. S. L. (2016). Ventricular Tachycardia Ablation versus Escalation of Antiarrhythmic Drugs. New England Journal of Medicine, 375(2), 111-121. doi:10.1056/nejmoa1513614Schulze, W. H. W., Chen, Z., Relan, J., Potyagaylo, D., Krueger, M. W., Karim, R., … Dössel, O. (2016). ECG imaging of ventricular tachycardia: evaluation against simultaneous non-contact mapping and CMR-derived grey zone. Medical & Biological Engineering & Computing, 55(6), 979-990. doi:10.1007/s11517-016-1566-xShah, D. C., Jaïs, P., Haïssaguerre, M., Chouairi, S., Takahashi, A., Hocini, M., … Clémenty, J. (1997). Three-dimensional Mapping of the Common Atrial Flutter Circuit in the Right Atrium. Circulation, 96(11), 3904-3912. doi:10.1161/01.cir.96.11.3904Shome, S., & Macleod, R. (s. f.). Simultaneous High-Resolution Electrical Imaging of Endocardial, Epicardial and Torso-Tank Surfaces Under Varying Cardiac Metabolic Load and Coronary Flow. Lecture Notes in Computer Science, 320-329. doi:10.1007/978-3-540-72907-5_33SIMMS, H. D., & GESELOWITZ, D. B. (1995). Computation of Heart Surface Potentials Using the Surface Source Model. Journal of Cardiovascular Electrophysiology, 6(7), 522-531. doi:10.1111/j.1540-8167.1995.tb00425.xSvehlikova, J., Teplan, M., & Tysler, M. (2018). Geometrical constraint of sources in noninvasive localization of premature ventricular contractions. Journal of Electrocardiology, 51(3), 370-377. doi:10.1016/j.jelectrocard.2018.02.013Tsyganov, A., Wissner, E., Metzner, A., Mironovich, S., Chaykovskaya, M., Kalinin, V., … Kuck, K.-H. (2018). Mapping of ventricular arrhythmias using a novel noninvasive epicardial and endocardial electrophysiology system. Journal of Electrocardiology, 51(1), 92-98. doi:10.1016/j.jelectrocard.2017.07.018Umapathy, K., Nair, K., Masse, S., Krishnan, S., Rogers, J., Nash, M. P., & Nanthakumar, K. (2010). Phase Mapping of Cardiac Fibrillation. Circulation: Arrhythmia and Electrophysiology, 3(1), 105-114. doi:10.1161/circep.110.853804Van Dam, P. M., Oostendorp, T. F., Linnenbank, A. C., & van Oosterom, A. (2009). Non-Invasive Imaging of Cardiac Activation and Recovery. Annals of Biomedical Engineering, 37(9), 1739-1756. doi:10.1007/s10439-009-9747-5Van Oosterom, A. (2001). Genesis of the T wave as based on an equivalent surface source model. Journal of Electrocardiology, 34(4), 217-227. doi:10.1054/jelc.2001.28896Van Oosterom, A. (2002). Solidifying the solid angle. Journal of Electrocardiology, 35(4), 181-192. doi:10.1054/jelc.2002.37176Van Oosterom, A. (2004). ECGSIM: an interactive tool for stu

    Incorporation of anisotropic conductivities in EEG source analysis

    Get PDF
    The electroencephalogram (EEG) is a measurement of brain activity over a period of time by placing electrodes at the scalp (surface EEG) or in the brain (depth EEG) and is used extensively in the clinical practice. In the past 20 years, EEG source analysis has been increasingly used as a tool in the diagnosis of neurological disorders (like epilepsy) and in the research of brain functionality. EEG source analysis estimates the origin of brain activity given the electrode potentials measured at the scalp. This involves solving an inverse problem where a forward solution, which depends on the source parameters, is fitted to the given set of electrode potentials. The forward solution are the electrode potentials caused by a source in a given head model. The head model is dependent on the geometry and the conductivity. Often an isotropic conductivity (i.e. the conductivity is equal in all directions) is used, although the skull and white matter have an anisotropic conductivity (i.e. the conductivity can differ depending on the direction the current flows). In this dissertation a way to incorporate the anisotropic conductivities is presented and the effect of not incorporating these anisotropic conductivities is investigated. Spherical head models are simple head models where an analytical solution to the forward problem exists. A small simulation study in a 5 shell spherical head model was performed to investigate the estimation error due to neglecting the anisotropic properties of skull and white matter. The results show that the errors in the dipole location can be larger than 15 mm, which is unacceptable for an accurate dipole estimation in the clinical practice. Therefore, anisotropic conductivities have to be included in the head model. However, these spherical head models are not representative for the human head. Realistic head models are usually made from magnetic resonance scans through segmentation and are a better approximation to the geometry of the human head. To solve the forward problem in these head models numerical methods are needed. In this dissertation we proposed a finite difference technique that can incorporate anisotropic conductivities. Moreover, by using the reciprocity theorem the forward calculation time during an dipole source estimation procedure can be significantly reduced. By comparing the analytical solution for the dipole estimation problem with the one using the numerical method, the anisotropic finite difference with reciprocity method (AFDRM) is validated. Therefore, a cubic grid is made on the 5 shell spherical head model. The electrode potentials are obtained in the spherical head model with anisotropic conductivities by solving the forward problem using the analytical solution. Using these electrode potentials the inverse problem was solved in the spherical head model using the AFDRM. In this way we can determine the location error due to using the numerical technique. We found that the incorporation of anisotropic conductivities results in a larger location error when the head models are fully isotropical conducting. Furthermore, the location error due to the numerical technique is smaller if the cubic grid is made finer. To minimize the errors due to the numerical technique, the cubic grid should be smaller than or equal to 1 mm. Once the numerical technique is validated, a realistic head model can now be constructed. As a cubic grid should be used of at most 1 mm, the use of segmented T1 magnetic resonance images is best suited the construction. The anisotropic conductivities of skull and white matter are added as follows: The anisotropic conductivity of the skull is derived by calculating the normal and tangential direction to the skull at each voxel. The conductivity in the tangential direction was set 10 times larger than the normal direction. The conductivity of the white matter was derived using diffusion weighted magnetic resonance imaging (DW-MRI), a technique that measures the diffusion of water in several directions. As diffusion is larger along the nerve fibers, it is assumed that the conductivity along the nerve fibers is larger than the perpendicular directions to the nerve bundle. From the diffusion along each direction, the conductivity can be derived using two approaches. A simplified approach takes the direction with the largest diffusion and sets the conductivity along that direction 9 times larger than the orthogonal direction. However, by calculating the fractional anisotropy, a well-known measure indicating the degree of anisotropy, we can appreciate that a fractional anisotropy of 0.8715 is an overestimation. In reality, the fractional anisotorpy is mostly smaller and variable throughout the white matter. A realistic approach was therefore presented, which states that the conductivity tensor is a scaling of the diffusion tensor. The volume constraint is used to determine the scaling factor. A comparison between the realistic approach and the simplified approach was made. The results showed that the location error was on average 4.0 mm with a maximum of 10 mm. The orientation error was found that the orientation could range up to 60 degrees. The large orientation error was located at regions where the anisotropic ratio was low using the realistic approach but was 9 using the simplified approach. Furthermore, as the DW-MRI can also be used to measure the anisotropic diffusion in a gray matter voxel, we can derive a conductivity tensor. After investigating the errors due to neglecting these anisotropic conductivities of the gray matter, we found that the location error was very small (average dipole location error: 2.8 mm). The orientation error was ranged up to 40 degrees, although the mean was 5.0 degrees. The large errors were mostly found at the regions that had a high anisotropic ratio in the anisotropic conducting gray matter. Mostly these effects were due to missegmentation or to partial volume effects near the boundary interfaces of the gray and white matter compartment. After the incorporation of the anisotropic conductivities in the realistic head model, simulation studies can be performed to investigate the dipole estimation errors when these anisotropic conductivities of the skull and brain tissues are not taken into account. This can be done by comparing the solution to the dipole estimation problem in a head model with anisotropic conductivities with the one in a head model, where all compartments are isotropic conducting. This way we determine the error when a simplified head model is used instead of a more realistic one. When the anisotropic conductivity of both the skull and white matter or the skull only was neglected, it was found that the location error between the original and the estimated dipole was on average, 10 mm (maximum: 25 mm). When the anisotropic conductivity of the brain tissue was neglected, the location error was much smaller (an average location error of 1.1 mm). It was found that the anisotropy of the skull acts as an extra shielding of the electrical activity as opposed to an isotropic skull. Moreover, we saw that if the dipole is close to a highly anisotropic region, the potential field is changed reasonable in the near vicinity of the location of the dipole. In reality EEG contains noise contributions. These noise contribution will interact with the systematical error by neglecting anisotropic conductivities. The question we wanted to solve was “Is it worthwhile to incorporate anisotropic conductivities, even if the EEG contains noise?” and “How much noise should the EEG contain so that incorporating anisotropic conductivities improves the accuracy of EEG source analysis?”. When considering the anisotropic conductivities of the skull and brain tissues and the skull only, the location error due to the noise and neglecting the anisotropic conductivities is larger then the location error due to noise only. When only neglecting the anisotropic conductivities of the brain tissues only, the location error due to noise is similar to the location error due to noise and neglecting the anisotropic conductivities. When more advanced MR techniques can be used a better model to construct the anisotropic conductivities of the soft brain tissues can be used, which could result in larger errors even in the presence of noise. However, this is subject to further investigation. This suggests that the anisotropic conductivities of the skull should be incorporated. The technique presented in the dissertation can be used to epileptic patients in the presurgical evaluation. In this procedure patients are evaluated by means of medical investigations to determine the cause of the epileptic seizures. Afterwards, a surgical procedure can be performed to render the patient seizure free. A data set from a patiënt was obtained from a database of the Reference Center of Refractory Epilepsy of the Department of Neurology and the Department of Radiology of the Ghent University Hospital (Ghent, Belgium). The patient was monitored with a video/EEG monitoring with scalp and with implanted depth electrodes. An MR image was taken from the patient with the implanted depth electrodes, therefore, we could pinpoint the hippocampus as the onset zone of the epileptic seizures. The patient underwent a resective surgery removing the hippocampus, which rendered the patient seizure free. As DW-MRI images were not available, the head model constructed in chapter 4 and 5 was used. A neuroradiologist aligned the hippocampus in the MR image from which the head model was constructed. A spike was picked from a dataset and was used to estimate the source in a head model where all compartments were isotropic conducting, on one hand, and where the skull and brain tissues were anisotropic conducting, on the other. It was found that using the anisotropic head model, the source was estimated closer to the segmented hippocampus than the isotropic head model. This example shows the possibilities of this technique and allows us to apply it in the clinical practice. Moreover, a thorough validation of the technique has yet to be performed. There is a lot of discussion in the clinical community whether the spikes and epileptical seizures originate from the same origin in the brain. This question can be solved by applying our technique in patient studies

    Non-invasive identification of atrial fibrillation drivers

    Full text link
    Atrial fibrillation (AF) is one of the most common cardiac arrhythmias. Nowadays the fibrillatory process is known to be provoked by the high-frequency reentrant activity of certain atrial regions that propagates the fibrillatory activity to the rest of the atrial tissue, and the electrical isolation of these key regions has demonstrated its effectiveness in terminating the fibrillatory process. The location of the dominant regions represents a major challenge in the diagnosis and treatment of this arrhythmia. With the aim to detect and locate the fibrillatory sources prior to surgical procedure, non-invasive methods have been developed such as body surface electrical mapping (BSPM) which allows to record with high spatial resolution the electrical activity on the torso surface or the electrocardiographic imaging (ECGI) which allows to non-invasively reconstruct the electrical activity in the atrial surface. Given the novelty of these systems, both technologies suffer from a lack of scientific knowledge about the physical and technical mechanisms that support their operation. Therefore, the aim of this thesis is to increase that knowledge, as well as studying the effectiveness of these technologies for the localization of dominant regions in patients with AF. First, it has been shown that BSPM systems are able to noninvasively identify atrial rotors by recognizing surface rotors after band-pass filtering. Furthermore, the position of such surface rotors is related to the atrial rotor location, allowing the distinction between left or right atrial rotors. Moreover, it has been found that the surface electrical maps in AF suffer a spatial smoothing effect by the torso conductor volume, so the surface electrical activity can be studied with a relatively small number of electrodes. Specifically, it has been seen that 12 uniformly distributed electrodes are sufficient for the correct identification of atrial dominant frequencies, while at least 32 leads are needed for non-invasive identification of atrial rotors. Secondly, the effect of narrowband filtering on the effectiveness of the location of reentrant patterns was studied. It has been found that this procedure allows isolating the reentrant electrical activity caused by the rotor, increasing the detection rate for both invasive and surface maps. However, the spatial smoothing caused by the regularization of the ECGI added to the temporal filtering causes a large increase in the spurious reentrant activity, making it difficult to detect real reentrant patterns. However, it has been found that maps provided by the ECGI without temporal filtering allow the correct detection of reentrant activity, so narrowband filtering should be applied for intracavitary or surface signal only. Finally, we studied the stability of the markers used to detect dominant regions in ECGI, such as frequency maps or the rotor presence. It has been found that in the presence of alterations in the conditions of the inverse problem, such as electrical or geometrical noise, these markers are significantly more stable than the ECGI signal morphology from which they are extracted. In addition, a new methodology for error reduction in the atrial spatial location based on the curvature of the curve L has been proposed. The results presented in this thesis showed that BSPM and ECGI systems allows to non-invasively locate the presence of high-frequency rotors, responsible for the maintenance of AF. This detection has been proven to be unambiguous and robust, and the physical and technical mechanisms that support this behavior have been studied. These results indicate that both non-invasive systems provide information of great clinical value in the treatment of AF, so their use can be helpful for selecting and planning atrial ablation procedures.La fibrilación auricular (FA) es una de las arritmias cardiacas más frecuentes. Hoy en día se sabe que el proceso fibrilatorio está provocado por la actividad reentrante a alta frecuencia de ciertas regiones auriculares que propagan la actividad fibrilatoria en el resto del tejido auricular, y se ha demostrado que el aislamiento eléctrico de estas regiones dominantes permite detener el proceso fibrilatorio. La localización de las regiones dominantes supone un gran reto en el diagnóstico y tratamiento de la FA. Con el objetivo de poder localizar las fuentes fibrilatorias con anterioridad al procedimiento quirúrgico, se han desarrollado métodos no invasivos como la cartografía eléctrica de superficie (CES) que registra con gran resolución espacial la actividad eléctrica en la superficie del torso o la electrocardiografía por imagen (ECGI) que permite reconstruir la actividad eléctrica en la superficie auricular. Dada la novedad de estos sistemas, existe una falta de conocimiento científico sobre los mecanismos físicos y técnicos que sustentan su funcionamiento. Por lo tanto, el objetivo de esta tesis es aumentar dicho conocimiento, así como estudiar la eficacia de ambas tecnologías para la localización de regiones dominantes en pacientes con FA. En primer lugar, ha visto que los sistemas CES permiten identificar rotores auriculares mediante el reconocimiento de rotores superficiales tras el filtrado en banda estrecha. Además, la posición de los rotores superficiales está relacionada con la localización de dichos rotores, permitiendo la distinción entre rotores de aurícula derecha o izquierda. Por otra parte, se ha visto que los mapas eléctricos superficiales durante FA sufren una gran suavizado espacial por el efecto del volumen conductor del torso, lo que permite que la actividad eléctrica superficial pueda ser estudiada con un número relativamente reducido de electrodos. Concretamente, se ha visto que 12 electrodos uniformemente distribuidos son suficientes para una correcta identificación de frecuencias dominantes, mientras que son necesarios al menos 32 para una correcta identificación de rotores auriculares. Por otra parte, también se ha estudiado el efecto del filtrado en banda estrecha sobre la eficacia de la localización de patrones reentrantes. Así, se ha visto que este procedimiento permite aislar la actividad eléctrica reentrante provocada por el rotor, aumentando la tasa de detección tanto para señal obtenida de manera invasiva como para los mapas superficiales. No obstante, este filtrado temporal sobre la señal de ECGI provoca un gran aumento de la actividad reentrante espúrea que dificulta la detección de patrones reentrantes reales. Sin embargo, los mapas ECGI sin filtrado temporal permiten la detección correcta de la actividad reentrante, por lo el filtrado debería ser aplicado únicamente para señal intracavitaria o superficial. Por último, se ha estudiado la estabilidad de los marcadores utilizados en ECGI para detectar regiones dominantes, como son los mapas de frecuencia o la presencia de rotores. Se ha visto que en presencia de alteraciones en las condiciones del problema inverso, como ruido eléctrico o geométrico, estos marcadores son significativamente más estables que la morfología de la propia señal ECGI. Además, se ha propuesto una nueva metodología para la reducción del error en la localización espacial de la aurícula basado en la curvatura de la curva L. Los resultados presentados en esta tesis revelan que los sistemas de CES y ECGI permiten localizar de manera no invasiva la presencia de rotores de alta frecuencia. Esta detección es univoca y robusta, y se han estudiado los mecanismos físicos y técnicos que sustentan dicho comportamiento. Estos resultados indican que ambos sistemas no invasivos proporcionan información de gran valor clínico en el tratamiento de la FA, por lo que su uso puede ser de gran ayuda para la selección y planificaciLa fibril·lació auricular (FA) és una de les arítmies cardíaques més freqüents. Hui en dia es sabut que el procés fibrilatori està provocat per l'activitat reentrant de certes regions auriculars que propaguen l'activitat fibril·latoria a la resta del teixit auricular, i s'ha demostrat que l'aïllament elèctric d'aquestes regions dominants permet aturar el procés fibrilatori. La localització de les regions dominants suposa un gran repte en el diagnòstic i tractament d'aquesta arítmia. Amb l'objectiu de poder localitzar fonts fibril·latories amb anterioritat al procediment quirúrgic s'han desenvolupat mètodes no invasius com la cartografia elèctrica de superfície (CES) que registra amb gran resolució espacial l'activitat elèctrica en la superfície del tors o l'electrocardiografia per imatge (ECGI) que permet obtenir de manera no invasiva l'activitat elèctrica en la superfície auricular. Donada la relativa novetat d'aquests sistemes, existeix una manca de coneixement científic sobre els mecanismes físics i tècnics que sustenten el seu funcionament. Per tant, l'objectiu d'aquesta tesi és augmentar aquest coneixement, així com estudiar l'eficàcia d'aquestes tecnologies per a la localització de regions dominants en pacients amb FA. En primer lloc, s'ha vist que els sistemes CES permeten identificar rotors auriculars mitjançant el reconeixement de rotors superficials després del filtrat en banda estreta. A més, la posició dels rotors superficials està relacionada amb la localització d'aquests rotors, permetent la distinció entre rotors de aurícula dreta o esquerra. També s'ha vist que els mapes elèctrics superficials durant FA pateixen un gran suavitzat espacial per l'efecte del volum conductor del tors, el que permet que l'activitat elèctrica superficial pugui ser estudiada amb un nombre relativament reduït d'elèctrodes. Concretament, s'ha vist que 12 elèctrodes uniformement distribuïts són suficients per a una correcta identificació de freqüències dominants auriculars, mentre que són necessaris almenys 32 per a una correcta identificació de rotors auriculars. D'altra banda, també s'ha estudiat l'efecte del filtrat en banda estreta sobre l'eficàcia de la localització de patrons reentrants. Així, s'ha vist que aquest procediment permet aïllar l'activitat elèctrica reentrant provocada pel rotor, augmentant la taxa de detecció tant pel senyal obtingut de manera invasiva com per als mapes superficials. No obstant això, aquest filtrat temporal sobre el senyal de ECGI provoca un gran augment de l'activitat reentrant espúria que dificulta la detecció de patrons reentrants reals. A més, els mapes proporcionats per la ECGI sense filtrat temporal permeten la detecció correcta de l'activitat reentrant, per la qual cosa el filtrat hauria de ser aplicat únicament per a senyal intracavitària o superficial. Per últim, s'ha estudiat l'estabilitat dels marcadors utilitzats en ECGI per a detectar regions auriculars dominants, com són els mapes de freqüència o la presència de rotors. S'ha vist que en presència d'alteracions en les condicions del problema invers, com soroll elèctric o geomètric, aquests marcadors són significativament més estables que la morfologia del mateix senyal ECGI. A més, s'ha proposat una nova metodologia per a la reducció de l'error en la localització espacial de l'aurícula basat en la curvatura de la corba L. Els resultats presentats en aquesta tesi revelen que els sistemes de CES i ECGI permeten localitzar de manera no invasiva la presència de rotors d'alta freqüència. Aquesta detecció és unívoca i robusta, i s'han estudiat els mecanismes físics i tècnics que sustenten aquest comportament. Aquests resultats indiquen que els dos sistemes no invasius proporcionen informació de gran valor clínic en el tractament de la FA, pel que el seu ús pot ser de gran ajuda per a la selecció i planificació de procediments d'ablació auricular.Rodrigo Bort, M. (2016). Non-invasive identification of atrial fibrillation drivers [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/75346TESISPremios Extraordinarios de tesis doctorale

    Bayesian Inference with Combined Dynamic and Sparsity Models: Application in 3D Electrophysiological Imaging

    Get PDF
    Data-driven inference is widely encountered in various scientific domains to convert the observed measurements into information that cannot be directly observed about a system. Despite the quickly-developing sensor and imaging technologies, in many domains, data collection remains an expensive endeavor due to financial and physical constraints. To overcome the limits in data and to reduce the demand on expensive data collection, it is important to incorporate prior information in order to place the data-driven inference in a domain-relevant context and to improve its accuracy. Two sources of assumptions have been used successfully in many inverse problem applications. One is the temporal dynamics of the system (dynamic structure). The other is the low-dimensional structure of a system (sparsity structure). In existing work, these two structures have often been explored separately, while in most high-dimensional dynamic system they are commonly co-existing and contain complementary information. In this work, our main focus is to build a robustness inference framework to combine dynamic and sparsity constraints. The driving application in this work is a biomedical inverse problem of electrophysiological (EP) imaging, which noninvasively and quantitatively reconstruct transmural action potentials from body-surface voltage data with the goal to improve cardiac disease prevention, diagnosis, and treatment. The general framework can be extended to a variety of applications that deal with the inference of high-dimensional dynamic systems

    Accurate skull modeling for EEG source imaging

    Get PDF

    Functional Mapping of Three-Dimensional Electrical Activation in Ventricles

    Get PDF
    University of Minnesota Ph.D. dissertation. 2010. Major: Biomedical Engineering. Advisor: Bin He. 1 computer file (PDF); 139 pages.Ventricular arrhythmias account for nearly 400,000 deaths per year in the United States alone. Electrical mapping of the ventricular activation could facilitate the diagnosis and treatment of arrhythmias, e.g. guiding catheter ablation. To date, both direct mapping and non-contact mapping techniques have been routinely used in electrophysiology labs for obtaining the electrical activity on the endocardial surface. Non-invasive functional mapping methods are also developed to estimate the electrical activity on the epicardium or on both epicardium and endocardium from the body surface measurements. Though successful, the results using above methods are all limited on the surface of the heart and thus cannot directly characterize the cardiac events originating within the myocardial wall. Our group's goal is to develop a functional mapping method to estimate the three-dimensional cardiac electrical activity from either non-invasive body surface potential maps or minimally-invasive intracavitary potential maps, by solving the so-called "inverse problem". Hence the information under the surface of the heart could be revealed to better characterize the cardiac activation. In the present thesis study, the previously developed three-dimensional cardiac electrical imaging (3DCEI) approach has been further investigated. Its function is expanded for not only estimating the global activation sequence but also reconstructing the potential at any myocardial site throughout the ventricle. New algorithms under the 3DCEI scheme are also explored for more powerful mapping capability. The performance of the enhanced 3DCEI approach is rigorously evaluated in both control and diseased swine models when the clinical settings are mimicked. The promising results validate the feasibility of estimating detailed three-dimensional cardiac activation by using the 3DCEI approach, and suggest that 3DCEI has great potential of guiding the clinical management of cardiac arrhythmias in a more efficient way
    corecore