Automated Machine Learning for the Classification of Normal and Abnormal Electromyography Data

<p>Needle electromyography (EMG) is a common technique used in clinical neurophysiology to record the electrical activity of muscles at different levels of activation. It can be used to diagnose various neurological/muscular disorders, as the EMG signals of patients with both nerve diseases (neuropathies) and<br>muscle diseases (myopathies) differ from the signal in healthy controls. A major drawback of this examination is that it relies on visual inspection and as such, it is highly subjective and prone to errors. Based on EMG time series of 65 individuals (40 with ALS/IBM and 25 healthy), we aim to develop an automated machine-learning pipeline for the classification of EMG recordings of muscles in either disease or healthy (muscle-<br>level). The automated pipeline consists of feature extraction, feature selection, modelling algorithm, and optimization, in which the most significant features are automatically selected from the feature space and the hyperparameters of the model are optimized by a Bayesian technique as part of the automated<br>approach. Aside from the muscle-level approach, we also explore a patient-level approach, which uses the output of the muscle-level automated pipeline in a post-processing manner to classify patients in being either disease or healthy, based on their muscle recordings. The resulting two approaches yield an AUC score<br>of 81.7% (muscle-level) and 81.5% (patient-level), indicating that such approaches can assist clinicians in diagnosing if a patient has a neuropathy/myopathy or is healthy.<br></p>