Weighting-Based Deep Ensemble Learning for Recognition of Interventionalists’ Hand Motions During Robot-Assisted Intravascular Catheterization

Abstract

Robot-assisted intravascular interventions have evolved as unique treatments approach for cardiovascular diseases. However, the technology currently has low potentials for catheterization skill evaluation, slow learning curve, and inability to transfer experience gained from manual interventions. This study proposes a new weighting-based deep ensemble model for recognizing interventionalists' hand motions in manual and robotic intravascular catheterization. The model has a module of neural layers for extracting features in electromyography data, and an ensemble of machine learning methods for classifying interventionalists' hand gestures as one of the six hand motions used during catheterization. A soft-weighting technique is applied to guide the contributions of each base learners. The model is validated with electromyography data recorded during <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-vitro</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-vivo</i> trials and labeled as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">many-to-one</i> sequences. Results obtained show the proposed model could achieve 97.52% and 47.80% recognition performances on test samples in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-vitro</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-vivo</i> data, respectively. For the latter, transfer learning was applied to update weights from the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-vitro</i> data, and the retrained model was used for recognizing the hand motions in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-vivo</i> data. The weighting-based ensemble was evaluated against the base learners and the results obtained shows it has a more stable performance across the six hand motion classes. Also, the proposed model was compared with four existing methods used for hand motion recognition in intravascular catheterization. The results obtained show our model has the best recognition performances for both the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-vitro</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-vivo</i> catheterization datasets. This study is developed toward increasing interventionalists' skills in robot-assisted catheterization.