On the Selection of Neural Network Architecture for Supervised Motor Unit Identification from High-Density Surface EMG

Abstract

In the last decade, accurate identification of motor unit (MU) firings received a lot of research interest. Different decomposition methods have been developed, each with its advantages and disadvantages. In this study, we evaluated the capability of three different types of neural networks (NNs), namely dense NN, long short-term memory (LSTM) NN and convolutional NN, to identify MU firings from high-density surface electromyograms (HDsEMG). Each type of NN was evaluated on simulated HDsEMG signals with a known MU firing pattern and high variety of MU characteristics. Compared to dense NN, LSTM and convolutional NN yielded significantly higher precision and significantly lower miss rate of MU identification. LSTM NN demonstrated higher sensitivity to noise than convolutional NN.Clinical Relevance-MU identification from HDsEMG signals offers valuable insight into neurophysiology of motor system but requires relatively high level of expert knowledge. This study assesses the capability of self-learning artificial neural networks to cope with this problem.