Prediction of heartbeat motion with a generalized adaptive filter

Franke, Timothy J.; Bebek, Özkan; Çavuşoğlu, M. Cenk

doi:10.1109/robot.2008.4543652

Cited by 18 publications

(19 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The presented one step adaptive filter and the generalized adaptive filter are initially proposed by Franke et al . in [9] and [10] respectively. The analysis of these predictors are extended in this paper.…”

Section: Introductionmentioning

confidence: 99%

Heart Motion Prediction Based on Adaptive Estimation Algorithms for Robotic-Assisted Beating Heart Surgery

et al. 2013

Self Cite

View full text Add to dashboard Cite

Robotic assisted beating heart surgery aims to allow surgeons to operate on a beating heart without stabilizers as if the heart is stationary. The robot actively cancels heart motion by closely following a point of interest (POI) on the heart surface—a process called Active Relative Motion Canceling (ARMC). Due to the high bandwidth of the POI motion, it is necessary to supply the controller with an estimate of the immediate future of the POI motion over a prediction horizon in order to achieve sufficient tracking accuracy. In this paper, two least-square based prediction algorithms, using an adaptive filter to generate future position estimates, are implemented and studied. The first method assumes a linear system relation between the consecutive samples in the prediction horizon. On the contrary, the second method performs this parametrization independently for each point over the whole the horizon. The effects of predictor parameters and variations in heart rate on tracking performance are studied with constant and varying heart rate data. The predictors are evaluated using a 3 degrees of freedom test-bed and prerecorded in-vivo motion data. Then, the one-step prediction and tracking performances of the presented approaches are compared with an Extended Kalman Filter predictor. Finally, the essential features of the proposed prediction algorithms are summarized.

show abstract

Section: Introductionmentioning

confidence: 99%

Heart Motion Prediction Based on Adaptive Estimation Algorithms for Robotic-Assisted Beating Heart Surgery

et al. 2013

Self Cite

View full text Add to dashboard Cite

show abstract

“…Frank et al proposed a new Autoregressive (AR) model based adaptive prediction technique [8,9] that is less susceptible to the variation of the heart motion period and robust to noise. However, this AR model is still a linear model that cannot describe some of the nonlinear dynamics of heart motion.…”

Section: Related Workmentioning

confidence: 99%

Heart Motion Prediction in Robotic-Assisted Beating Heart Surgery: A Nonlinear Fast Adaptive Approach

Wang

et al. 2013

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

Off-pump Coronary Artery Bypass Graft (CABG) surgery outperforms traditional on-pump surgery because the assisted robotic tools can alleviate the relative motion between the beating heart and robotic tools. Therefore, it is possible for the surgeon to operate on the beating heart and thus lessens post surgery complications for the patients. Due to the highly irregular and non-stationary nature of heart motion, it is critical that the beating heart motion is predicted in the modelbased track control procedures. It is technically preferable to model heart motion in a nonlinear way because the characteristic analysis of 3D heart motion data through Bi-spectral analysis and Fourier methods demonstrates the involved nonlinearity of heart motion. We propose an adaptive nonlinear heart motion model based on the Volterra Series in this paper. We also design a fast lattice structure to achieve computational-efficiency for realtime online predictions. We argue that the quadratic term of the Volterra Series can improve the prediction accuracy by covering sharp change points and including the motion with sufficient detail. The experiment results indicate that the adaptive nonlinear heart motion prediction algorithm outperforms the autoregressive (AR) and the time-varying Fourier-series models in terms of the root mean square of the prediction error and the prediction error in extreme cases.

show abstract

“…

V X = {false[x_{t + 1}, x_{t + 2}, \dots, x_{t + N} false]}^{T},

and adaptively learns the relationship, V , using a least squares method [17]. An advantage of each of these methods is that they do not parameterize the motion, reducing the risk of not accurately characterizing the underlying dynamics.…”

Section: Related Workmentioning

confidence: 99%

Toward onboard estimation of physiological phase for an epicardial crawling robot

Wood

Schwartzman

Zenati

et al. 2012

2012 4th IEEE RAS &Amp; EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

View full text Add to dashboard Cite

HeartLander is a miniature mobile robot which adheres to and crawls over the surface of the beating heart to provide therapies in a minimally invasive manner. Although HeartLander inherently provides a stable operating platform, the motion of the surface of the heart remains an important factor in the operation of the robot. The quasi-periodic motion of the heart due to physiological cycles, respiration and the heartbeat, affects the ability of the robot to move, as well as localize accurately. In order to improve locomotion efficiency, as well as register different locations on the heart in physiological phase, two methods of identifying physiological phases are presented: sliding-window-based and model-based. In the sliding-window-based approach a vector of previous measurements is compared to previously learned motion templates to determine the current physiological phases, while the model-based approach learns a Fourier series model of the motion, and uses this model to estimate the current physiological phases using an Extended Kalman Filter (EKF). The two methods, while differing in approach, produce similarly accurate results on data recorded from animal experiments in vivo.

show abstract

Prediction of heartbeat motion with a generalized adaptive filter

Cited by 18 publications

References 9 publications

Heart Motion Prediction Based on Adaptive Estimation Algorithms for Robotic-Assisted Beating Heart Surgery

Heart Motion Prediction Based on Adaptive Estimation Algorithms for Robotic-Assisted Beating Heart Surgery

Heart Motion Prediction in Robotic-Assisted Beating Heart Surgery: A Nonlinear Fast Adaptive Approach

Toward onboard estimation of physiological phase for an epicardial crawling robot

Contact Info

Product

Resources

About