Development and validation of a deep neural network for automated electromyographic pattern classification

This study compared the performance of five artificial neural networks in evaluating surface electromyography (sEMG) signal quality. AlexNet demonstrated the highest accuracy (99.55%) with negligible false classifications, indicating that sEMG quality evaluation can be automated via an image recognition-based deep neural network without compromising human-like classification accuracy.