Prediction of natural guidewire rotation using an sEMG-based NARX neural network

For the treatment of cardiovascular diseases, clinical success of percutaneous coronary intervention is highly dependent on natural technical skills and dexterous manipulation strategies of surgeons. However, the increasing used robotic surgical systems have been designed without considering manipulation techniques, especially surgical behaviors and motion patterns. This has driven research towards exploitation of natural manipulation skills in recent years. In this paper, natural guidewire manipulations are analyzed and predicted using an sEMG-based nonlinear autoregressive neural network with exogenous inputs. The relationship between natural endovascular manipulation and guidewire rotation is built through the network. Two experiments at different rotational speed were performed to verify the effectiveness and robustness of the applied model. The experimental results show that the average predictive root mean error of five subjects is 15.61° at the low speed and 21.85° at the high speed. These favorable results could be of interest to improve existing robotic surgical systems.