Towards active tracking of beating heart motion in the presence of arrhythmia for robotic assisted beating heart surgery

Abstract

In robotic assisted beating heart surgery, the control architecture for heart motion tracking has stringent requirements in terms of bandwidth of the motion that needs to be tracked. In order to achieve sufficient tracking accuracy, feed-forward control algorithms, which rely on estimations of upcoming heart motion, have been proposed in the literature. However, performance of these feed-forward motion control algorithms under heart rhythm variations is an important concern. In their past work, the authors have demonstrated the effectiveness of a receding horizon model predictive control-based algorithm, which used generalized adaptive predictors, under constant and slowly varying heart rate conditions. This paper extends these studies to the case when the heart motion statistics change abruptly and significantly, such as during arrhythmias. A feasibility study is carried out to assess the motion tracking capabilities of the adaptive algorithms in the occurrence of arrhythmia during beating heart surgery. Specifically, the tracking performance of the algorithms is evaluated on prerecorded motion data, which is collected in vivo and includes heart rhythm irregularities. The algorithms are tested using both simulations and bench experiments on a three degree-of-freedom robotic test bed. They are also compared with a position-plus-derivative controller as well as a receding horizon model predictive controller that employs an extended Kalman filter algorithm for predicting future heart motion.

Figure 1. Experimental setup for the measurement of the heart motion.

Two sonomicrometer crystals that are sutured on the anterior and posterior surfaces of the heart are used for data collection. Pacemaker leads and sonomicrometer base are also visible in the image.

Figure 2. Power spectral density of the heart motion in the z-direction.

Heart motion modes are inseparable. The frequency axis is set to 12-s arrhythmia data from animal 1 is shown. The spectrum corresponds to motion of the POI located at 0.5 cm on the right side of LAD.

Figure 3. A schematic of the heart motion prediction problem.

The circles represent past observations, now in memory, the is the current observation, and the short curve originating from there is the horizon estimate. The predictor takes the past observations and produces the horizon estimate from past observations.

Figure 4. Tracking results of 183-s arrhythmia data (only a part of the data is presented) from animal 1 for the generalized predictor.

Reference and PHANToM positions, RMS position error and MPC control effort are shown (A) Axis 1 results. (B) Axis 2 results. (C) Axis 3 results.

Figure 5. Tracking results of 183-s arrhythmia data (only a part of the data is presented) from animal 1 for the PD Controller.

Axis 1 results are shown only.