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BioMed Research International
Volume 2017 (2017), Article ID 1279486, 9 pages
Research Article

Beating Heart Motion Accurate Prediction Method Based on Interactive Multiple Model: An Information Fusion Approach

1Tianjin Key Laboratory of Information Sensing & Intelligent Control, Tianjin University of Technology and Education, Tianjin, China
2Case Center for Imaging Research, Case Western Reserve University, Cleveland, OH 44106, USA
3Department of Radiology, University Hospitals Case Medical Center, Case Western Reserve University, Cleveland, OH 44106, USA
4Technology, Humanities and Social Sciences Department, Guangdong University of Technology, Guangzhou, China
5Department of MIS, Marketing and Digital Business, Rochester Institute of Technology, Rochester, NY 14623-5603, USA

Correspondence should be addressed to Fan Liang

Received 26 November 2016; Revised 15 July 2017; Accepted 10 August 2017; Published 15 October 2017

Academic Editor: Mario U. Manto

Copyright © 2017 Fan Liang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Robot-assisted motion compensated beating heart surgery has the advantage over the conventional Coronary Artery Bypass Graft (CABG) in terms of reduced trauma to the surrounding structures that leads to shortened recovery time. The severe nonlinear and diverse nature of irregular heart rhythm causes enormous difficulty for the robot to realize the clinic requirements, especially under arrhythmias. In this paper, we propose a fusion prediction framework based on Interactive Multiple Model (IMM) estimator, allowing each model to cover a distinguishing feature of the heart motion in underlying dynamics. We find that, at normal state, the nonlinearity of the heart motion with slow time-variant changing dominates the beating process. When an arrhythmia occurs, the irregularity mode, the fast uncertainties with random patterns become the leading factor of the heart motion. We deal with prediction problem in the case of arrhythmias by estimating the state with two behavior modes which can adaptively “switch” from one to the other. Also, we employed the signal quality index to adaptively determine the switch transition probability in the framework of IMM. We conduct comparative experiments to evaluate the proposed approach with four distinguished datasets. The test results indicate that the new proposed approach reduces prediction errors significantly.