Le Rolle Virginie, Carrault Guy, Richard Pierre-Yves, Pibarot Philippe, Durand Louis-Gilles, Hernández Alfredo I
INSERM, U642, 35000, Rennes, France.
Acta Biotheor. 2009 Dec;57(4):457-78. doi: 10.1007/s10441-009-9092-y. Epub 2009 Oct 29.
The ventricular pressure profile is characteristic of the cardiac contraction progress and is useful to evaluate the cardiac performance. In this contribution, a tissue-level electromechanical model of the left ventricle is proposed, to assist the interpretation of left ventricular pressure waveforms. The left ventricle has been modeled as an ellipsoid composed of twelve mechano-hydraulic sub-systems. The asynchronous contraction of these twelve myocardial segments has been represented in order to reproduce a realistic pressure profiles. To take into account the different energy domains involved, the tissue-level scale and to facilitate the building of a modular model, multiple formalisms have been used: Bond Graph formalism for the mechano-hydraulic aspects and cellular automata for the electrical activation. An experimental protocol has been defined to acquire ventricular pressure signals from three pigs, with different afterload conditions. Evolutionary Algorithms have been used to identify the model parameters in order to minimize the error between experimental and simulated ventricular pressure signals. Simulation results show that the model is able to reproduce experimental ventricular pressure. In addition, electro-mechanical activation times have been determined in the identification process. For example, the maximum electrical activation time is reached, respectively, 96.5, 139.3 and 131.5 ms for the first, second, and third pigs. These preliminary results are encouraging for the application of the model on non-invasive data like ECG, arterial pressure or myocardial strain.
心室压力曲线是心脏收缩过程的特征,有助于评估心脏功能。在本研究中,提出了一种左心室组织水平的机电模型,以辅助解释左心室压力波形。左心室被建模为一个由十二个机械液压子系统组成的椭球体。为了再现逼真的压力曲线,对这十二个心肌节段的异步收缩进行了模拟。为了考虑不同的能量域、组织水平尺度并便于构建模块化模型,使用了多种形式主义:用于机械液压方面的键合图形式主义和用于电激活的细胞自动机。定义了一个实验方案,以获取三只猪在不同后负荷条件下的心室压力信号。使用进化算法识别模型参数,以最小化实验和模拟心室压力信号之间的误差。模拟结果表明,该模型能够再现实验心室压力。此外,在识别过程中确定了机电激活时间。例如,第一只、第二只和第三只猪的最大电激活时间分别为96.5、139.3和131.5毫秒。这些初步结果对于将该模型应用于心电图、动脉压或心肌应变等非侵入性数据是令人鼓舞的。