Localization of the site of origin of cardiac activation by means of a heart-model-based electrocardiographic imaging approach.
We have developed a new approach to solve the inverse problem of electrocardiography in terms of heart model parameters. The inverse solution of the electrocardiogram (ECG) inverse problem is defined, in the present study, as the parameters of the heart model, which are closely related to the physiological and pathophysiological status of the heart, and is estimated by using an optimization system of heart model parameters, instead of solving the matrix equation relating the body surface ECGs and equivalent cardiac sources. An artificial neural network based preliminary diagnosis system has been developed to limit the searching space of the optimization algorithm and to initialize the model parameters in the computer heart model. The optimal heart model parameters were obtained by minimizing the objective functions, as functions of the observed and model-generated body surface ECGs. We have tested the feasibility of the newly developed technique in localizing the site of origin of cardiac activation using a pace mapping protocol. The present computer simulation results show that, the present approach for localization of the site of origin of ventricular activation achieved an averaged localization error of about 3 mm [for 5-muV Gaussian white noise (GWN)] and 4 mm (for 10-muV GWN), with standard deviation of the localization errors of being about 1.5 mm. The present simulation study suggests that this newly developed approach provides a robust inverse solution, circumventing the difficulties of the ECG inverse problem, and may become an important alternative to other ECG inverse solutions.
Li
G
G
University of Illinois at Chicago, Department of EECS, 60607, USA.
He
B
B
D000465:Algorithms; D018780:Body Surface Potential Mapping; D002304:Cardiac Pacing, Artificial; D003198:Computer Simulation; D004562:Electrocardiography / Q000379:methods*; D006329:Heart Conduction System / Q000502:physiology*; D006801:Humans; D008433:Mathematics; D008955:Models, Cardiovascular; D016571:Neural Networks, Computer; D016276:Ventricular Function