ObjectiveTo develop an artificial neural network (ANN) for classification of motor unit action potential (MUAP) duration in real-word, unselected and uncleaned needle electromyography (n-EMG)... Show moreObjectiveTo develop an artificial neural network (ANN) for classification of motor unit action potential (MUAP) duration in real-word, unselected and uncleaned needle electromyography (n-EMG) recordings.MethodsTwo nested ANN models were trained, the first discerning muscle rest, contraction and artifacts in n-EMG recordings from 2674 individual muscles from 326 patients obtained as part of daily care. The second ANN model subsequently used segments labeled as contraction for prediction of prolonged, normal and shortened MUAPs. Model performance was assessed in one internal and two external validation datasets of 184, 30 and 50 muscles, respectively.ResultsThe first model discerned rest, contraction and artifacts with an accuracy of 96%. The second model predicted prolonged, normal and shortened MUAPs with an accuracy of 67%, 83% and 68% in the different validation sets.ConclusionsWe developed a two-step ANN that classifies rest, muscle contraction and artifacts from real-world n-EMG recordings with very high accuracy. MUAP duration classification had moderate accuracy.SignificanceThis is the first study to show that an ANN can classify MUAPs in real-world n-EMG recordings highlighting the potential for AI assisted MUAP classification as a clinical tool. Show less
Kefalas, M.; Koch, M.; Geraedts, V.J.; Wang, H.; Tannemaat, M.; Bäck, T.H.W. 2020
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... Show moreNeedle 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) andmuscle 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-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 automatedapproach. 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 scoreof 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. Show less
Culver, D.A.; Tavee, J.; Dahan, A.; Bajorunas, D.; Jeziorska, M.; Velzen, M. van; ... ; Brines, M. 2016
