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Accepted (AM)Open Access (License Unspecified), Open
Journal article
A Deep Learning Model for Automated Classification of Intraoperative Continuous EMG
IEEE transactions on medical robotics and bionics, v 3(1), pp 44-52
01 Feb 2021
PMID: 33997657
Abstract
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.
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Details
- Title
- A Deep Learning Model for Automated Classification of Intraoperative Continuous EMG
- Creators
- Xuefan Zha - Carnegie Mellon UniversityLeila Wehbe - Carnegie Mellon UniversityRobert J. Sclabassi - Allegheny Health NetworkZachary Mace - Allegheny Health NetworkYe Liang - Allegheny Health NetworkAlexander Yu - Allegheny Health NetworkJody Leonardo - Allegheny Health NetworkBoyle C. Cheng - Allegheny Health NetworkTodd A. Hillman - Pittsburgh Ear AssociatesDouglas A. Chen - Pittsburgh Ear AssociatesCameron N. Riviere - Carnegie Mellon University
- Publication Details
- IEEE transactions on medical robotics and bionics, v 3(1), pp 44-52
- Publisher
- IEEE
- Number of pages
- 9
- Grant note
- R01EB000526; R21EB012209; R01HL078839; R01EB024564 / U.S. National Institutes of Health; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Surgery; Neurosurgery
- Web of Science ID
- WOS:000896660300005
- Scopus ID
- 2-s2.0-85109485107
- Other Identifier
- 991021930321304721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Engineering, Biomedical
- Robotics