Classification of needle‐EMG resting potentials by machine learning

ABSTRACT Introduction : The diagnostic importance of audio signal characteristics in needle electromyography (EMG) is well established. Given the recent advent of audio‐sound identification by artificial intelligence, we hypothesized that the extraction of characteristic resting EMG signals and application of machine learning algorithms could help classify various EMG discharges. Methods : Data files of 6 classes of resting EMG signals were divided into 2‐s segments. Extraction of characteristic features (384 and 4,367 features each) was used to classify the 6 types of discharges using machine learning algorithms. Results : Across 841 audio files, the best overall accuracy of 90.4% was observed for the smaller feature set. Among the feature classes, mel‐frequency cepstral coefficients (MFCC)‐related features were useful in correct classification. Conclusions : We showed that needle EMG resting signals were satisfactorily classifiable by the combination of feature extraction and machine learning, and this can be applied to clinical settings. Muscle Nerve 59 :224–228, 2019