Rom Rami, Erel Jacob, Glikson Michael, Rosenblum Kobi, Ginosar Ran, Hayes David L
AI Semi Ltd, Granot, Israel.
Pacing Clin Electrophysiol. 2005 Nov;28(11):1168-73. doi: 10.1111/j.1540-8159.2005.40007.x.
We report the results of a simulation of an adaptive cardiac resynchronization therapy (CRT) device performing biventricular pacing in which the atrioventricular (AV) delay and interventricular (VV) interval parameters are changed dynamically in response to data provided by the simulated IEGMs and simulated hemodynamic sensors. A learning module, an artificial neural network, performs the adaptive part of the algorithm supervised by an algorithmic deterministic module, internally or externally from the implanted CRT or CRT-D. The simulated cardiac output obtained with the adaptive CRT device is considerably higher (30%) especially with higher heart rates than in the nonadaptive CRT mode and is likely to be translated into improvement in quality of life of patients with congestive heart failure.
我们报告了一种进行双心室起搏的自适应心脏再同步治疗（CRT）设备的模拟结果，其中房室（AV）延迟和心室间（VV）间期参数根据模拟心内电信号图（IEGMs）和模拟血流动力学传感器提供的数据动态变化。一个学习模块，即人工神经网络，在算法确定性模块的监督下执行算法的自适应部分，该确定性模块可在植入的CRT或CRT-D内部或外部运行。与非自适应CRT模式相比，使用自适应CRT设备获得的模拟心输出量显著更高（30%），尤其是在心率较高时，并且这很可能转化为充血性心力衰竭患者生活质量的改善。
