A comprehensive multimodality heart motion prediction algorithm for robotic‐assisted beating heart surgery

Abstract

BACKGROUND: An essential requirement for performing robotic-assisted surgery on a freely beating heart is a prediction algorithm that can estimate the future heart trajectory. METHOD: Heart motion, respiratory volume (RV) and electrocardiogram (ECG) signal were measured from two dogs during thoracotomy surgery. A comprehensive multimodality prediction algorithm was developed based on the multivariate autoregressive model to incorporate the heart trajectory and cardiorespiratory data with multiple inherent measurement rates explicitly. RESULTS: Experimental results indicated strong relationships between the dominant frequencies of heart motion with RV and ECG. The prediction algorithm revealed a high steady state accuracy, with the root mean square (RMS) errors in the range of 82 to 162 μm for a 300-second interval, less than half of that of the best competitor. CONCLUSION: The proposed multimodality prediction algorithm is promising for practical use in robotic assisted beating heart surgery, considering its capability of providing highly accurate predictions in long horizons.