Deep neural networks to classify motor unit action potential signals acquired using needle electromyography - Tatlidil, Isil; Ekinci, Murat; Boz, Cavit (Journal of Medical Science, 2025)
Aim. To classify motor unit action potential patterns using a deep learning technique with high accuracy.Material and methods. A dataset was compiled from three main groups of motor unit action potential patterns, including myopathy, neuropathy, and normal, as assessed by a clinical neurophysiologist during routine clinical assessments. After preprocessing the raw signals in the dataset, a total of 3,152 signal segments from 96 muscles of 26 individuals were divided into training and test sets. Deep learning network models were developed in Python using the Keras API in Jupyter Notebook.Results. Among the deep learning models, a hybrid deep neural network model with a one-dimensional convolution layer as an input layer and four layers of gate recurrent units (1DCNN-GRU) achieved the highest accuracy rates. Ten-fold cross-validation resulted in a mean accuracy rate of 98.13 ± 1.05%.Conclusions. Both conventional machine learning models and deep learning models could classify needle EMG patterns that belonged to three neuromuscular disorder groups with high accuracy. However, more clinical studies with larger datasets are needed for validation. In contrast to conventional machine learning techniques, deep learning models could receive signals as input data and automatically extract the required features. Therefore, they could facilitate the real-time implementation of the pattern recognition tasks in the future.
Keywords: deep learning; convolutional neural network; gate recurrent unit; needle electromyography; neuromuscular disorders