Exploring the effects of wavelet types and windowing on EMG-based IONM through deep learning architectures

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.