A Haptics Feedback Based-LSTM Predictive Model for Pericardiocentesis Therapy Using Public Introperative Data

Proposing a robust and fast real-time medical procedure, operating remotely is always a challenging task, due mainly to the effect of delay and dropping of the speed of networks, on operations. If a further stage of prediction is properly designed on remotely operated systems, many difficulties could be tackled. Hence, in this paper, an accurate predictive model, calculating haptics feedback in percutaneous heart biopsy is investigated. A one-layer Long Short-Term Memory based (LSTM-based) Recurrent Neural Network, which is a natural fit for understanding haptics time series data, is utilised. An offline learning procedure is proposed to build the model, followed by an online procedure to operate on new experiments, remotely fed to the system. Statistical analyses prove that the error variation of the model is significantly narrow, showing the robustness of the model. Moreover, regarding computational costs, it takes 0.7 ms to predict a time step further online, which is quick enough for real-time haptic interaction.