A Deep Learning Model for Automated Classification of Intraoperative Continuous EMG

• Published in: IEEE transactions on medical robotics and bionics Vol. 3; no. 1; pp. 44 - 52
• Main Authors: Zha, Xuefan, Wehbe, Leila, Sclabassi, Robert J., Mace, Zachary, Liang, Ye V., Yu, Alexander, Leonardo, Jody, Cheng, Boyle C., Hillman, Todd A., Chen, Douglas A., Riviere, Cameron N.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Automation; Classification; convolutional neural networks; Damage prevention; Deep learning; Electrical noise; Electromyography; Injuries; intraoperative neuromonitoring; Monitoring; Nerves; pattern recognition; Spectrogram; Surgery; Task analysis; Waveforms; Electromyography; Pattern recognition; Convolutional neural networks; Intraoperative Neuromonitoring
• ISSN: 2576-3202; 2576-3202
• DOI: 10.1109/TMRB.2020.3048255

Objective: Intraoperative neurophysiological monitoring (IONM) is the use of electrophysiological methods during certain high-risk surgeries to assess the functional integrity of nerves in real time and alert the surgeon to prevent damage. However, the efficiency of IONM in current practice is limited by latency of verbal communications, inter-rater variability, and the subjective manner in which electrophysiological signals are described. Methods: In an attempt to address these shortcomings, we investigate automated classification of free-running electromyogram (EMG) waveforms during IONM. We propose a hybrid model with a convolutional neural network (CNN) component and a long short-term memory (LSTM) component to better capture complicated EMG patterns under conditions of both electrical noise and movement artifacts. Moreover, a preprocessing pipeline based on data normalization is used to handle classification of data from multiple subjects. To investigate model robustness, we also analyze models under different methods for processing of artifacts. Results: Compared with several benchmark modeling methods, CNN-LSTM performs best in classification, achieving accuracy of 89.54% and sensitivity of 94.23% in cross-patient evaluation. Conclusion: The CNN-LSTM model shows promise for automated classification of continuous EMG in IONM. Significance: This technique has potential to improve surgical safety by reducing cognitive load and inter-rater variability.