ArticleJournal of healthcare engineering2021
Tic Detection in Tourette Syndrome Patients Based on Unsupervised Visual Feature Learning.
Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 23 citations in OpenAlex.
- Deep Learning for Automated Tic Detection and Prediction in Tourette Syndrome Using Electromyography and Video.Research square · 2026Article
- Digital health and Tourette Syndrome: new technological frontiers in diagnosis and management.Frontiers in psychiatry · 2026Review
- Article
- Video-based evaluation system for tic action in Tourette syndrome: modeling, detection, and evaluation.Health information science and systems · 2023Article
- Light-efficient channel attention in convolutional neural networks for tic recognition in the children with tic disorders.Frontiers in computational neuroscience · 2022Article
- Tourette syndrome research highlights from 2021.F1000Research · 2022Review
- Literature Review on the Applications of Machine Learning and Blockchain Technology in Smart Healthcare Industry: A Bibliometric Analysis.Journal of healthcare engineering · 2021Review
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A clinical diagnosis of tic disorder involves several complex processes, among which observation and evaluation of patient behavior usually require considerable time and effective cooperation between the doctor and the patient. The existing assessment scale has been simplified into qualitative and quantitative assessments of movements and sound twitches over a certain period, but it must still be completed manually. Therefore, we attempt to find an automatic method for detecting tic movement to assist in diagnosis and evaluation. Based on real clinical data, we propose a deep learning architecture that combines both unsupervised and supervised learning methods and learns features from videos for tic motion detection. The model is trained using leave-one-subject-out cross-validation for both binary and multiclass classification tasks. For these tasks, the model reaches average recognition precisions of 86.33% and 86.26% and recalls of 77.07% and 78.78%, respectively. The visualization of features learned from the unsupervised stage indicates the distinguishability of the two types of tics and the nontic. Further evaluation results suggest its potential clinical application for auxiliary diagnoses and evaluations of treatment effects.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.