A Deep Learning Model for Automated Classification of Intraoperative Continuous EMG.
Xuefan Zha1, Leila Wehbe1, Robert J Sclabassi2
1Carnegie Mellon University, Pittsburgh, PA, USA.
IEEE Transactions on Medical Robotics and Bionics
|May 17, 2021
Summary
A new CNN-LSTM model automates electromyogram (EMG) classification for intraoperative neurophysiological monitoring (IONM). This AI approach enhances surgical safety by reducing errors and surgeon workload during high-risk procedures.
Area of Science:
- Neuroscience
- Artificial Intelligence
- Medical Technology
Background:
- Intraoperative neurophysiological monitoring (IONM) uses electrophysiological methods to safeguard nerves during high-risk surgeries.
- Current IONM is hindered by communication delays, subjective signal interpretation, and inter-rater variability.
Purpose of the Study:
- To develop an automated system for classifying electromyogram (EMG) waveforms during IONM.
- To address limitations in current IONM practices through advanced signal processing and machine learning.
Main Methods:
- A hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model was developed for EMG waveform classification.
- A data normalization preprocessing pipeline was implemented to manage multi-subject data.
- Model robustness was evaluated under various artifact processing techniques.
Main Results:
- The proposed CNN-LSTM model achieved 89.54% accuracy and 94.23% sensitivity in cross-patient evaluation.
- This performance surpassed several benchmark modeling methods.
- The model effectively captured complex EMG patterns amidst electrical noise and movement artifacts.
Conclusions:
- The CNN-LSTM model demonstrates significant potential for automated continuous EMG classification in IONM.
- This technology can enhance surgical safety by minimizing cognitive load and inter-rater variability for surgeons.
