A Contrastive Graph Convolutional Network for Toe-Tapping Assessment in Parkinson's Disease
IEEE Transactions on Circuits and Systems for Video Technology
; : 1-1, 2022.
Article
in English
| Scopus | ID: covidwho-1992676
ABSTRACT
One of the common motor symptoms of Parkinson’s disease (PD) is bradykinesia. Automated bradykinesia assessment is critically needed for helping neurologists achieve objective clinical diagnosis and hence provide timely and appropriate medical services. This need has become especially urgent after the outbreak of the coronavirus pandemic in late 2019. Currently, the main factor limiting the accurate assessment is the difficulty of mining the fine-grained discriminative motion features. Therefore, we propose a novel contrastive graph convolutional network for automated and objective toe-tapping assessment, which is one of the most important tests of lower-extremity bradykinesia. Specifically, based on joint sequences extracted from videos, a supervised contrastive learning strategy was followed to cluster together the features of each class, thereby enhancing the specificity of the learnt class-specific features. Subsequently, a multi-stream joint sparse learning mechanism was designed to eliminate potentially similar redundant features of joint position and motion, hence strengthening the discriminability of features extracted from different streams. Finally, a spatial-temporal interaction graph convolutional module was developed to explicitly model remote dependencies across time and space, and hence boost the mining of fine-grained motion features. Comprehensive experimental results demonstrate that this method achieved remarkable classification performance on a clinical video dataset, with an accuracy of 70.04% and an acceptable accuracy of 98.70%. These results obviously outperformed other existing sensor- and video-based methods. The proposed video-based scheme provides a reliable and objective tool for automated quantitative toe-tapping assessment, and is expected to be a viable method for remote medical assessment and diagnosis. IEEE
Contrastive learning; Convolutional neural networks; Deep learning; Diseases; Feature extraction; Graph convolutional network; Parkinson’s disease; Support vector machines; Task analysis; Toe tapping; Video-based assessment; Videos; Automation; Classification (of information); Convolution; Diagnosis; Job analysis; Neural networks; Convolutional networks; Convolutional neural network; Features extraction; Support vectors machine; Video
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
IEEE Transactions on Circuits and Systems for Video Technology
Year:
2022
Document Type:
Article
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