Motor unit number estimation based on convolutional neural network

Objective. The compound muscle action potential (CMAP) scan contains a muscle’s detailed stimulus-activation information and thereby can be used for motor unit number estimation (MUNE). Due to the challenges in accurately obtaining the motor unit numbers from experimental CMAP scans, most existing MUNE methods rely on data fitting, which is time-consuming and requires manual operations. This study explored the feasibility of a neural network-based MUNE approach and proposes an end-to-end model for rapid estimation. Approach. We developed NNEstimation, a novel supervised learning framework based on a convolutional neural network (CNN), to estimate motor unit numbers from both synthetic and experimental CMAP scans. A probabilistic model with varied parameters was used to generate CMAP scans with diverse characteristics for neural network training. NNEstimation trained on synthetic data was directly tested on both synthetic and experimental data. Main Results. Evaluations on synthetic CMAP scans demonstrate that NNEstimation achieves lower estimation error and shorter execution time than the conventional data fitting method, MScanFit. The accuracy of NNEstimation is influenced by motor unit numbers of CMAP scans and remains unaffected by noise levels, recorded amplitudes, or motor unit activation thresholds. Moreover, NNEstimation’s estimates on experimental data are highly consistent with those of MScanFit. Significance. Although trained solely on synthetic CMAP scans, NNEstimation achieves estimation results comparable to those of the traditional algorithm on experimental data while significantly reducing execution time, demonstrating its potential for practical MUNE applications.