Heart Motion Measurement and Prediction for Robotic Assisted Beating Heart Surgery

Mansouri, Saeed | 2018

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  1. Type of Document: Ph.D. Dissertation
  2. Language: Farsi
  3. Document No: 51429 (08)
  4. University: Sharif University of Technology
  5. Department: Mechanical Engineering
  6. Advisor(s): Farahmand, Farzam; Vossoughi, Gholamreza
  7. Abstract:
  8. An essential requirement for performing robotic assisted surgery on a freely beating heart is a prediction algorithm which can estimate the future heart trajectory with a high accuracy in a long horizon. The main objective of this research was measurement and prediction of the heart motion for robotic assisted beating heart surgery. In this study, first the feasibility of a stereo infrared tracking system for measuring the free beating heart motion was investigated by experiments on a heart motion simulator. Simulator experiments revealed a high tracking accuracy when the capturing times were synchronized and the tracker pointed at the target from an appropriate distance.Then, the heart motion and biological signals, i.e. respiratory flow and ECG signal, were measured from two dogs subjected to thoracotomy surgery. On each specimen, two sets of experiments were performed; the first one at a constant heart rate and the second one at a varying heart rate. Considering the experimental results, 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. An independent predictor was also adopted to make the algorithm computationally efficient. A hybrid amplitude modulation-(AM) and autoregressive- (AR) based algorithm was also developed to enable estimating the global and local oscillations of the beating heart, raised from its major and minor physiological activities.The AM model was equipped with an estimator of the heartbeat frequency to compensate for the heart rate variations. The accuracy and horizon , as well as the computational cost of the algorithm were then compared with those of the previously proposed methods, by implementing on the experimental datasets. The RMS error of the MVAR-based algorithm was in the range of 82-162m, less than half of that of the best competitor. The RMS error of the AM-AR algorithm ranged between 165 and 361 m for varying heart rate motion, which was substantially less than the errors reported by the previous studies. The proposed algorithms have a superior efficacy in comparison with the previously proposed methods of heart motion prediction. With the capability of providing highly accurate predictions in long horizons in real-time, both model are promising for practical use in robotic assisted freely beating heart surgery
  9. Keywords:
  10. Biological Signals ; Beating Heart Surgery ; Robotic Surgery ; Heart Motion Measuring ; In-Vivo Animal Experiment ; Heart Motion Prediction ; Varying Heart Rate

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