Exploring the effects of wavelet types and windowing on EMG-based IONM through deep learning architectures
Document Type
Article
Publication Date
3-1-2026
Abstract
Intraoperative neuromonitoring (IONM) plays a critical role in preserving nerve function during high-risk surgeries through real-time monitoring of electromyographic (EMG) activity. Routine EMG analysis, in real-time, is complex and prone to variability. This work presents an end-to-end deep learning-based framework for accurate EMG signal classification of the nerve status using the discrete wavelet transform (DWT) mathematical technique. Four state-of-the-art deep learning architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), a CNN-LSTM ensemble, and a Transformer model, were tested with various Daubechies wavelet families (db1–db6) and window sizes (50–500 samples). The Transformer model performed superiorly in classification, achieving an outstanding accuracy of 98.13 %, an F1-score of 98.14 %, and a recall of 97.50 % using db1 and a 400-sample window. The results summed up that the use of wavelet-based time-frequency decomposition has a significant influence on enhancing classification performance, especially when utilized with deep learning models.
Recommended Citation
Mohamed Hassan Zayed, Nourhan and Nabil Elsharkawy, Abdalla, "Exploring the effects of wavelet types and windowing on EMG-based IONM through deep learning architectures" (2026). Mechanical Engineering. 205.
https://buescholar.bue.edu.eg/mech_eng/205