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Introduction: To guide the neurologist and neurophysiologist with interpretation and implementation of clinical neurophysiological examinations, we aim to provide a systematic review on evidence of electrophysiological features used to differentiate between hyperkinetic movement disorders., Methods: A PRISMA systematic search and QUADAS quality evaluation has been performed in PubMed to identify diagnostic test accuracy studies comparing electromyography and accelerometer features. We included papers focusing on tremor, dystonia, myoclonus, chorea, tics and ataxia and their functional variant. The features were grouped as 1) basic features (e.g., amplitude, frequency), 2) the influence of tasks on basic features (e.g., entrainment, distraction), 3) advanced analyses of multiple signals, 4) and diagnostic tools combining features., Results: Thirty-eight cross-sectional articles were included discussing tremor (n = 28), myoclonus (n = 5), dystonia (n = 5) and tics (n = 1). Fifteen were rated as 'high quality'. In tremor, the basic and task-related features showed great overlap between clinical tremor syndromes, apart from rubral and enhanced physiological tremor. Advanced signal analyses were best suited for essential, parkinsonian and functional tremor, and cortical, non-cortical and functional jerks. Combinations of electrodiagnostic features could identify essential, enhanced physiological and functional tremor., Conclusion: Studies into the diagnostic accuracy of electrophysiological examinations to differentiate between hyperkinetic movement disorders have predominantly been focused on clinical tremor syndromes. No single feature can differentiate between them all; however, a combination of analyses might improve diagnostic accuracy., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background and Purpose: Functional movement disorders (FMDs) pose a diagnostic challenge for clinicians. Over the years several associated features have been shown to be suggestive for FMDs. Which features mentioned in the literature are discriminative between FMDs and non-FMDs were examined in a large cohort. In addition, a preliminary prediction model distinguishing these disorders was developed based on differentiating features., Method: Medical records of all consecutive patients who visited our hyperkinetic outpatient clinic from 2012 to 2019 were retrospectively reviewed and 12 associated features in FMDs versus non-FMDs were compared. An independent t test for age of onset and Pearson chi-squared analyses for all categorical variables were performed. Multivariate logistic regression analysis was performed to develop a preliminary predictive model for FMDs., Results: A total of 874 patients were eligible for inclusion, of whom 320 had an FMD and 554 a non-FMD. Differentiating features between these groups were age of onset, sex, psychiatric history, family history, more than one motor phenotype, pain, fatigue, abrupt onset, waxing and waning over long term, and fluctuations during the day. Based on these a preliminary predictive model was computed with a discriminative value of 91%., Discussion: Ten associated features are shown to be not only suggestive but also discriminative between hyperkinetic FMDs and non-FMDs. Clinicians can use these features to identify patients suspected for FMDs and can subsequently alert them to test for positive symptoms at examination. Although a first preliminary model has good predictive accuracy, further validation should be performed prospectively in a multi-center study., (© 2020 The Authors. European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.)