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

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 in-vitro and in-vivo trials and labeled as many-to-one sequences. Results obtained show the proposed model could achieve 97.52% and 47.80% recognition performances on test samples in the in-vitro and in-vivo data, respectively. For the latter, transfer learning was applied to update weights from the in-vitro data, and the retrained model was used for recognizing the hand motions in the in-vivo 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 in-vitro and in-vivo catheterization datasets. This study is developed toward increasing interventionalists' skills in robot-assisted catheterization.

Automated summary

This study proposes a weighting-based deep ensemble model for recognizing hand motions in intravascular catheterization. The model extracts features from electromyography data and uses machine learning methods to classify hand gestures. Results show the model achieves high recognition performances and has stable performance across different hand motion classes.