Automated Motor Tic Detection: A Machine Learning Approach.

BACKGROUND: Video-based tic detection and scoring is useful to independently and objectively assess tic frequency and severity in patients with Tourette syndrome. In trained raters, interrater reliability is good. However, video ratings are time-consuming and cumbersome, particularly in large-scale studies. Therefore, we developed two machine learning (ML) algorithms for automatic tic detection. OBJECTIVE: The aim of this study was to evaluate the performances of state-of-the-art ML approaches for automatic video-based tic detection in patients with Tourette syndrome. METHODS: We used 64 videos of n = 35 patients with Tourette syndrome. The data of six subjects (15 videos with ratings) were used as a validation set for hyperparameter optimization. For the binary classification task to distinguish between tic and no-tic segments, we established two different supervised learning approaches. First, we manually extracted features based on landmarks, which served as input for a Random Forest classifier (Random Forest). Second, a fully automated deep learning approach was used, where regions of interest in video snippets were input to a convolutional neural network (deep neural network). RESULTS: Tic detection F1 scores (and accuracy) were 82.0% (88.4%) in the Random Forest and 79.5% (88.5%) in the deep neural network approach. CONCLUSIONS: ML algorithms for automatic tic detection based on video recordings are feasible and reliable and could thus become a valuable assessment tool, for example, for objective tic measurements in clinical trials. ML algorithms might also be useful for the differential diagnosis of tics. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Urge-tic associations in children and adolescents with Tourette syndrome.

Premonitory urges preceding tics are a cardinal feature of Gilles de la Tourette syndrome (GTS), a developmental disorder usually starting during middle childhood. However, the temporal relation between urges and tics has only been investigated in adults. In 25 children and adolescents with GTS (8-18 years), we assess urge-tic associations, including inter-individual differences, correlation to clinical measures, and in comparison to a previously reported sample of adult GTS patients. Group-level analyses confirmed positive associations between urges and tics. However, at the individual level, less than half of participants showed positive associations, a similar proportion did not, and in two participants, the association was reversed. Tic expression and subjective urge levels correlated with corresponding clinical scores and participants with more severe tics during the urge monitor exhibited stronger urge-tic associations. Associations between reported urge levels and instantaneous tic intensity tended to be less pronounced in children and adolescents than in adult GTS patients. The observed heterogeneity of urge-tic associations cast doubt on the notion that tics are directly caused by urges. More severe tics may facilitate anticipation of tics and thereby lead to more pronounced urge-tic associations, consistent with a hypothesis of urges as a byproduct of tics.