Behavioral ratings based on clinical observations are still the gold standard for screening, diagnosing, and assessing outcomes in Tourette syndrome. Detecting tic symptoms plays an important role in patient treatment and evaluation; accurate tic identification is the key to clinical diagnosis and evaluation. In this study, we proposed a tic action detection method using face video feature recognition for tic and control groups. Through facial ROI extraction, a 3D convolutional neural network was used to learn video feature representations, and multi-instance learning anomaly detection strategy was integrated to construct the tic action analysis and discrimination framework. We applied this tic recognition framework in our video dataset. The model evaluation results achieved average tic detection accuracy of 91.02%, precision of 77.07% and recall of 78.78%. And the tic score curve with postprocessing provided information of how the patient's twitches change over time. The detection results at the individual level indicated that our method can effectively detect tic actions in videos of Tourette patients without the need for fine labeling, which is significant for the long-term evaluation of patients with Tourette syndrome.
Keywords: 3D-CNN networks; Multi-instance learning; Tic recognition; Tourette syndrome.
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