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-μV Gaussian white noise (GWN)] and 4 mm (for 10-μV 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.
Bibliographical noteFunding Information:
Manuscript received June 29, 2000; revised February 26, 2001. This work was supported in part by the National Science Foundation (NSF) under CAREER Award BES-9875344, in part by the American Heart Association under Grant 0140132N, and in part by a grant from the Campus Research Board of the University of Illinois at Chicago. Asterisk indicates corresponding author.
- Body surface potential maps
- Electrocardiographic imaging
- Heart model
- Inverse problem
- Optimization algorithms
- Pace mapping