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

Mansouri, S ; Sharif University of Technology | 2019

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  1. Type of Document: Article
  2. DOI: 10.1002/rcs.1975
  3. Publisher: John Wiley and Sons Ltd , 2019
  4. Abstract:
  5. 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
  6. Keywords:
  7. Animal test in vivo ; Beating heart surgery ; Biological signals ; Heart motion prediction ; Respiratory volume ; Algorithm ; Animal experiment ; Controlled study ; Dog ; Heart movement ; Lung volume ; Measurement error ; Nonhuman ; Off pump surgery ; Prediction ; Robot assisted surgery ; Steady state ; Thoracotomy ; Animal ; Heart surgery ; Human ; Procedures ; Robotic surgical procedure ; Algorithms ; Animals ; Cardiac Surgical Procedures ; Dogs ; Electrocardiography ; Humans ; Male ; Motion ; Robotic Surgical Procedures
  8. Source: International Journal of Medical Robotics and Computer Assisted Surgery ; Volume 15, Issue 2 , 2019 ; 14785951 (ISSN)
  9. URL: https://onlinelibrary.wiley.com/doi/abs/10.1002/rcs.1975