Heart Motion Uncertainty Compensation Prediction Method for Robot Assisted Beating Heart Surgery - Master-slave Kalman Filters Approach
Heart Motion Uncertainty Compensation Prediction Method for Robot Assisted Beating Heart Surgery - Master-slave Kalman Filters Approach
复制标题
机器人辅助心脏跳动手术的心脏运动不确定性补偿预测方法——主从卡尔曼滤波器方法
DOI：
10.1007/s10916-014-0052-y
复制
发表时间：
2014-05-01
影响因子：
5.3
通讯作者：
Wu, Xingli
中科院分区：
文献类型：
--
作者：
Liang, Fan;Yu, Yang;Wu, Xingli
机器人辅助冠状动脉旁路移植术（Robot Assisted Coronary Artery Bypass Graft，CABG）通过主动消除心脏表面感兴趣点（Point of Interest，POI）与手术工具之间的相对运动，使心脏在手术过程中保持跳动。心脏跳动运动固有的非线性和多样性给机器人满足苛刻的跟踪控制要求带来了巨大的障碍。新奇提出了一种基于跳动心脏运动非线性自适应预测（NAP）算法的主从式卡尔曼滤波器。在研究中，我们将心脏跳动运动描述为与数学部分相关的非线性和与非数学部分相关的不确定性的结合。具体来说，首先，我们通过二次调制正弦曲线的心脏运动的非线性模型和估计它的主卡尔曼滤波器。其次，通过从卡尔曼滤波器自适应地改变过程噪声的协方差，从而包含不确定性心脏运动。我们进行比较实验，以评估所提出的方法与四个不同的数据集。结果表明，新的方法减少了至少30 μ m的预测误差。此外，新方法表现出良好的鲁棒性测试，其中两种心律失常数据集从MIT-BIH心律失常数据库进行评估。
Robot Assisted Coronary Artery Bypass Graft (CABG) allows the heart keep beating in the surgery by actively eliminating the relative motion between point of interest (POI) on the heart surface and surgical tool. The inherited nonlinear and diverse nature of beating heart motion gives a huge obstacle for the robot to meet the demanding tracking control requirements. In this paper, we novelty propose a Master-slave Kalman Filter based on beating heart motion Nonlinear Adaptive Prediction (NAP) algorithm. In the study, we describe the beating heart motion as the combination of nonlinearity relating mathematics part and uncertainty relating non-mathematics part. Specifically, first, we model the nonlinearity of the heart motion via quadratic modulated sinusoids and estimate it by a Master Kalman Filter. Second, we involve the uncertainty heart motion by adaptively change the covariance of the process noise through the slave Kalman Filter. We conduct comparative experiments to evaluate the proposed approach with four distinguished datasets. The results indicate that the new approach reduces prediction errors by at least 30 mu m. Moreover, the new approach performs well in robustness test, in which two kinds of arrhythmia datasets from MIT-BIH arrhythmia database are assessed.