Аннотация:Up to 30% of chronic heart failure (CHF) patients undergoing cardiac resynchronization therapy (CRT) do not respond to the treatment. Low CRT response can be associated with inadequate pacing lead positioning in ventricles not accounting for individual structural abnormalities in myocardium. Therefore, patient stratification for CRT, operation planning and optimization of CRT device settings remain a challenge. Personalized heart computer models based on clinical data may be useful for the prediction of the optimal zones for bi-ventricular pacing. Accurate model personalization is computationally expensive and time-consuming task depending on the criteria for parameter identification problem and numerical approach to solve the task. The aim of the study is to compare results of bi-ventricular pacing simulations on a patient specific model of the heart geometry with different parameter sets for myocardial conductivity based on different parameter identification approaches. We used CT images of a patient heart with annotated zone of the post-infarction scar for building a detailed anatomical model of the ventricles. For identification of myocardial conductivity parameters in the model, we used data from clinical surface ECG recordings from 191 electrodes on the torso. Two different approaches for solving identification problem were examined (uniform conductivity versus dissimilar local conductivity) and model simulations of the ventricular activation and ECG were compared under varied combinations of myocardial segments for bi-ventricular stimulation. We showed that the location of ventricular segments providing for optimal response to pacing in terms of a shortest total activation time, QRS complex duration, and low electrical dissynchrony indices may differ depending on the myocardial conductivity profile. Our results suggest that the structural and functional parameters of myocardial tissue are essential for accurate prediction of the optimal electrode location that could be used in the clinical practice for pre-operational CRT planning.