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A comprehensive multimodality heart motion prediction algorithm for robotic‐assisted beating heart surgery
Author(s) -
Mansouri Saeed,
Farahmand Farzam,
Vossoughi Gholamreza,
Ghavidel Alireza Alizadeh
Publication year - 2019
Publication title -
the international journal of medical robotics and computer assisted surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 53
eISSN - 1478-596X
pISSN - 1478-5951
DOI - 10.1002/rcs.1975
Subject(s) - computer science , earlobe , autoregressive model , algorithm , thoracotomy , cardiorespiratory fitness , trajectory , artificial intelligence , cardiology , medicine , mathematics , surgery , statistics , physics , astronomy
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.