This research aims to develop a framework for real-time monitoring of respiratory motion using deep learning techniques.
Developed a cascaded convolutional neural network (CNN) and long short-term memory (LSTM) model.
Implemented artifact suppression methods to enhance data quality.
Tested the framework in clinical environments for respiratory monitoring.
Achieved high-fidelity artifact suppression for cleaner signal processing.
Enabled real-time monitoring of respiratory motion with improved accuracy in clinical settings.
Abstract
This study demonstrates that the proposed deep learning framework provides an efficient solution for high-fidelity artifact suppression and realtime respiratory monitoring in clinical settings.