Sensitivity Analysis of Ventricular Activation and Electrocardiogram in Tailored Models of Heart-Failure Patients - Archive ouverte HAL Access content directly
Journal Articles Medical and Biological Engineering and Computing Year : 2018

Sensitivity Analysis of Ventricular Activation and Electrocardiogram in Tailored Models of Heart-Failure Patients

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Abstract

Cardiac resynchronization therapy is not effective in a variable proportion of heart failure patients. An accurate knowledge of each patient's electroanatomical features could be helpful to determine the most appropriate treatment. The goal of this study was to analyze and quantify the sensitivity of left ventricular (LV) activation and the electrocardiogram (ECG) to changes in 39 parameters used to tune realistic anatomical-electrophysiological models of the heart. Electrical activity in the ventricles was simulated using a reaction-diffusion equation. To simulate cellular electrophysiology, the Ten Tusscher-Panfilov 2006 model was used. Intracardiac electrograms and 12-lead ECGs were computed by solving the bidomain equation. Parameters showing the highest sensitivity values were similar in the six patients studied. QRS complex and LV activation times were modulated by the sodium current, the cell surface-to-volume ratio in the LV, and tissue conductivities. The T-wave was modulated by the calcium and rectifier-potassium currents, and the cell surface-to-volume ratio in both ventricles. We conclude that homogeneous changes in ionic currents entail similar effects in all ECG leads, whereas the effects of changes in tissue properties show larger inter-lead variability. The effects of parameter variations are highly consistent between patients and most of the model tuning could be performed with only ~10 parameters.
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hal-01567886 , version 1 (24-07-2017)

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Carlos Sánchez, Gabriele d'Ambrosio, Francesco Maffessanti, Enrico G Caiani, Frits W Prinzen, et al.. Sensitivity Analysis of Ventricular Activation and Electrocardiogram in Tailored Models of Heart-Failure Patients. Medical and Biological Engineering and Computing, 2018, 56, pp.491-504. ⟨10.1007/s11517-017-1696-9⟩. ⟨hal-01567886⟩

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