Deep learning for waveform identification of resting needle electromyography signals

•Resting EMG discharges were classified by Mel-spectrogram conversion and deep-learning algorithms.•Data augmentation and use of pre-trained weights (transfer learning) increased the accuracy.•Waveform identification of clinical EMG testing might be possible by deep-learning algorithms. Given the recent advent in machine learning and artificial intelligence on medical data analysis, we hypothesized that the deep learning algorithm can classify resting needle electromyography (n-EMG) discharges. Six clinically observed resting n-EMG signals were used as a dataset. The data were converted to Mel-spectrogram. Data augmentation was then applied to the training data. Deep learning algorithms were applied to assess the accuracies of correct classification, with or without the use of pre-trained weights for deep-learning networks. While the original data yielded the accuracy up to 0.86 on the test dataset, data-augmentation up to 200,000 training images showed significant increase in the accuracy to 1.0. The use of pre-trained weights (fine tuning) showed greater accuracy than “training from scratch”. Resting n-EMG signals were successfully classified by deep-learning algorithm, especially with the use of data augmentation and transfer learning techniques. Computer-aided signal identification of clinical n-EMG testing might be possible by deep-learning algorithms.