Document Type
Article
Publication Date
Fall 3-10-2025
Abstract
Intraoperative neuromonitoring (IONM) plays a critical role in minimizing nerve damage during surgeries by providing real-time feedback on neural integrity. This study evaluated models associated with deep learning and machine learning models for electromyography classification of signal during intraoperative neuromonitoring (IONM). The CNNLSTM model achieved the highest accuracy (85.2%), outperforming traditional models like KNN (53%), RF (62%), and CNN (76%). This demonstrates the degree to which the CNN-LSTM model can gain insight into temporal and spatial dependencies throughout the EMG signals, which makes it optimal for real-time classification in IONM applications. This implies that deep learning techniques can improve surgical procedures' safety and efficacy.
Recommended Citation
Nabil Elsharkawy, Abdalla and Zayed, Nourhan, "EMG-Based Intraoperative Neuromonitoring Using Advanced Machine Learning Approaches" (2025). Mechanical Engineering. 127.
https://buescholar.bue.edu.eg/mech_eng/127
Included in
Biomechanical Engineering Commons, Biomedical Engineering and Bioengineering Commons, Computer-Aided Engineering and Design Commons, Computer Engineering Commons, Electrical and Computer Engineering Commons
