An Easy-to-Use Assessment System for Spasticity Severity Quantification in Post-Stroke Rehabilitation
Chen Wang1; Liang Peng1; Zeng-Guang Hou1; Pu Zhang2; Peng Fang3
发表期刊IEEE Transactions on Cognitive and Developmental Systems
2023
页码1 - 1
文章类型Early Access
摘要

Spasticity is a motor disorder integrated in the upper motor neuron syndrome resulting from central nerve diseases such as stroke. The multi-factorial nature of spasticity manifestations leads to the inter-rater and intra-rater reliability of clinical assessment, hence, the objective severity quantification of the spastic hypertonia has attracted significant attention in the context of post-stroke rehabilitation. Here, we developed a novel assessment system to reliably identify the exaggerated muscle tone and quantitatively estimate the symptom severity in patients with upper-limb spasticity. Twenty subjects with post-stroke spasticity (53.0 ± 13.9 years old) and ten age-matched healthy subjects performed the passive stretch movements under the single-task and dual-task protocols, while wearing an exoskeletal measurement device developed by us. A preliminary identification layer was designed to discriminate the pathological electrophysiological outputs of the upper extremity muscles by using the long short-term memory (LSTM) networks. In the next layer, the severity quantification models can be triggered in parallel, aiming at evaluating the neural and non-neural level pathologies underlying the spastic resistance manually percepted by clinicians, where the muscle activation/co-activation features, kinematic departure and biomechanical characteristics were considered to improve the clinical relevance. Based on these single-level decisions, the third layer was constructed as an integrated model to yield a more comprehensive quantification of the symptom severity. The experimental validation of the proposed system demonstrated good reliability in discriminating the spastic hypertonia from the normal muscle tone, as well as strong agreement of the quantitative severity estimations with the commonly accepted clinical scales for the neural level (R=0.79, P=2.79e-5), non-neural level (R=0.75, P=1.62e-4) and integrated level (R=0.86, P=9.86e-7). In conclusion, the proposed assessment system holds great promise to provide clinicians with an easy-to-use tool as suitable supports for spasticity diagnosis, disease monitoring and treatment adjustment.

学科门类工学::控制科学与工程
DOI10.1109/TCDS.2023.3304352
收录类别SCI
语种英语
七大方向——子方向分类机器学习
国重实验室规划方向分类多模态协同认知
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被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/57194
专题多模态人工智能系统全国重点实验室
作者单位1.the State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences
2.China Rehabilitation Research Center
3.Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
第一作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Chen Wang,Liang Peng,Zeng-Guang Hou,et al. An Easy-to-Use Assessment System for Spasticity Severity Quantification in Post-Stroke Rehabilitation[J]. IEEE Transactions on Cognitive and Developmental Systems,2023:1 - 1.
APA Chen Wang,Liang Peng,Zeng-Guang Hou,Pu Zhang,&Peng Fang.(2023).An Easy-to-Use Assessment System for Spasticity Severity Quantification in Post-Stroke Rehabilitation.IEEE Transactions on Cognitive and Developmental Systems,1 - 1.
MLA Chen Wang,et al."An Easy-to-Use Assessment System for Spasticity Severity Quantification in Post-Stroke Rehabilitation".IEEE Transactions on Cognitive and Developmental Systems (2023):1 - 1.
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