基于二阶伏特拉级数模型的心脏运动信号快速最小二乘估计

doi:10.3724/SP.J.1146.2012.00866

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摘要心脏手术辅助机器人动态地消除心脏表面手术点与手术工具之间的相对运动，帮助医生在心脏不停跳的情况下完成手术并且极大减小病人术后并发症的影响。由于心脏运动的高带宽、非平稳、非线性的特点，机器人很难快速准确跟踪心脏运动。针对这一问题，该文首先通过双谱分析了解到心脏信号具有明显的源于心跳与呼吸运动相互耦合的非线性特征。接着提出基于非线性二阶伏特拉(Volterra)级数模型心脏运动快速预测算法，为控制算法提供参考信号的超前预测值。采用非线性模型描述心脏运动更加精确地表达了心脏运动的物理本质，快速最小二乘算法保证了系统的实时性。对比实验结果表明，非线性预测算法在均方误差方面优于以往的线性预测算法，大大减小了预测误差，提高了手术机器人相对运动消除能力。

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	梁帆
	孟晓风
	余旸

关键词 ：心脏运动估计, 心脏运动跟踪, 心脏运动非线性模型, 手术辅助机器人, 二阶伏特拉级数, 二次相位耦合

Abstract：The surgery assisted robotic tool helps the surgeon to cancel the relative motion between the beating heart and robotic tool, keeping the heart beating during the surgery, which will lessen post surgery complications for patients. Due to the highly irregular and non-stationary nature of heart motion, the robot is hard to track the beating heart motion. To solve this problem, a characteristic analysis of 3D heart motion data through Bi-spectral tool is used to demonstrate the nonlinearity of coupling between respiration and heartbeat in heart motion. Then an nonlinear Second order Volterra Series (SVS) based fast least square prediction algorithm is proposed to provide the future reference to the controller. The nonlinear model would accurately describe the heart motion and the fast least square algorithm would satisfy the real time needs. The comparative experiment results indicate that the proposed adaptive nonlinear heart motion prediction algorithm outperforms the former algorithms in the term of prediction accuracy. The relative motion cancellation ability of the robot is enhanced and prediction error is largely reduced.

Key words： Heart motion prediction Heart motion tracking Heart motion nonlinear model Surgery-assisted robotics Second order Volterra Series (SVS) Quadratic phase coupling

收稿日期: 2012-07-09

PACS:	R541
	TP242.6

基金资助:

国家自然科学基金创新群体项目(61121003)资助课题

通讯作者: 梁帆 E-mail: bachelormd10@gmail.com

引用本文:

梁帆, 孟晓风, 余旸. 基于二阶伏特拉级数模型的心脏运动信号快速最小二乘估计[J]. 电子与信息学报, 2013, 35(3): 639-644. Liang Fan, Meng Xiao-Feng, Yu Yang. Second Order Volterra Series Model Based Fast Least Square Method for Heart Motion Prediction. , 2013, 35(3): 639-644.

链接本文:

http://jeit.ie.ac.cn/CN/10.3724/SP.J.1146.2012.00866 或 http://jeit.ie.ac.cn/CN/Y2013/V35/I3/639