Applicability of Body Surface Potential Map in Computerized Optimization of Biventricular Pacing

Abstract

Biventricular pacing (BVP) could be improved by identifying the patient-specific optimal electrode positions. Body surface potential map (BSPM) is a non-invasive technique for obtaining the electrophysiology and pathology of a patient. The study proposes the use of BSPM as input for an automated non-invasive strategy based on a personalized computer model of the heart, to identify the patient pathology and specific optimal treatment with BVP devices. The anatomy of a patient suffering from left bundle branch block and myocardial infarction is extracted from a series of MR data sets. The clinical measurements of BSPM are used to parameterize the computer model of the heart to represent the individual pathology. Cardiac electrophysiology is simulated with ten Tusscher cell model and excitation propagation is calculated with adaptive cellular automaton, at physiological and pathological conduction levels. The optimal electrode configurations are identified by evaluating the QRS error between healthy and pathology case with/without pacing. Afterwards, the simulated ECGs for optimal pacing are compared to the post-implantation clinically measured ECGs. Both simulation and clinical optimization methods identified the right ventricular (RV) apex and the LV posterolateral regions as being the optimal electrode configuration for the patient. The QRS duration is reduced both in measured and simulated ECG after implantation with 20 and 14%, respectively. The optimized electrode positions found by simulation are comparable to the ones used in hospital. The similarity in QRS duration reduction between measured and simulated ECG signals indicates the success of the method. The computer model presented in this work is a suitable tool to investigate individual pathologies. The personalized model could assist therapy planning of BVP in patients with congestive heart failure. The proposed method could be used as prototype for further clinically oriented investigations of computerized optimization of biventricular pacing.

Computational Modeling for Cardiac Resynchronization Therapy

“…Briefly, the approach consists of simulating electrical activation in a realistic BiV anatomy for different AVD and VVD as well as several different lead locations and minimizing the error between the obtained activation sequences for each case against a simulated physiological case to determine which combination of AVD, VVD, and lead location yields the best acute response to CRT [ 70 – 73 , 81 ]. Such models have demonstrated that patient-specific optimization of lead location and AVD and VVD can improve CRT efficacy and impact treatment success [ 31 , 70 , 73 ] and that the use of body surface potential maps can further improve in-silico CRT optimization [ 82 ].…”

Guest Editorial Special Issue on Medical Imaging and Image Computing in Computational Physiology

“…Physiological signals, particularly those obtained through seamless sensing through wearable or home monitoring [85]- [88] and those obtained through invasive interventional sensors [89]- [92] can provide essential information to personalized models [93]. The former, contribute relevant information on the biorhythms, lifestyle or environmental factors from patients while the latter provide information on the immediate physiological response to specific therapeutic actions thus enabling therapy optimization during delivery.…”

A framework for personalization of computational models of the human atria

“…The most prominent efforts in this field are the optimization of RF-ablation in case of AF [13], [14] and the optimization of cardiac resynchronization therapy [15]- [18].…”