Tic Detection in Tourette Syndrome Patients Based on Unsupervised Visual Feature Learning
| Autor: | Yufan Guo, Tianshu Zhou, Yu Tian, Jianhua Feng, Hua Ru, Yuting Lou, Junya Wu, Jing-Song Li |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Tic disorder
Medicine (General) Article Subject Tics Computer science Movement Biomedical Engineering Health Informatics Machine learning computer.software_genre Tourette syndrome Multiclass classification 03 medical and health sciences 0302 clinical medicine R5-920 medicine Medical technology Humans Medical diagnosis R855-855.5 030304 developmental biology 0303 health sciences business.industry Deep learning Supervised learning medicine.disease Tic Disorders Surgery Artificial intelligence business Feature learning computer 030217 neurology & neurosurgery Tourette Syndrome Research Article Biotechnology |
| Zdroj: | Journal of Healthcare Engineering, Vol 2021 (2021) Journal of Healthcare Engineering |
| ISSN: | 2040-2309 2040-2295 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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