An Easy-to-Use Assessment System for Spasticity Severity Quantification in Post-Stroke Rehabilitation

被引:3
|
作者
Wang, Chen [1 ]
Peng, Liang [1 ]
Hou, Zeng-Guang [1 ,2 ,3 ]
Zhang, Pu [4 ]
Fang, Peng [5 ]
机构
[1] Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
[2] Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techno, Beijing 100190, Peoples R China
[3] Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
[4] China Rehabil Res Ctr, Beijing Boai Hosp, Beijing 100068, Peoples R China
[5] Chinese Acad Sci, Shenzhen Inst Adv Technol, CAS Key Lab Human Machine Intelligence Synergy Sys, Shenzhen 518055, Peoples R China
关键词
Muscles; Biomechanics; Kinematics; Immune system; Electrical resistance measurement; Elbow; Torque; Clinical measurement; machine learning; post-stroke spasticity; wearable technology; STRETCH REFLEX THRESHOLD; LIMB MOTOR FUNCTION; MODIFIED ASHWORTH SCALE; MODIFIED TARDIEU SCALE; STROKE; PATHOPHYSIOLOGY; RELIABILITY; LIMITATIONS; ACTIVATION; HYPERTONIA;
D O I
10.1109/TCDS.2023.3304352
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Spasticity is a motor disorder integrated in the upper motor neuron syndrome resulting from central nerve diseases such as stroke. The multifactorial nature of spasticity manifestations leads to the inter-rater and intrarater 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 support for spasticity diagnosis, disease monitoring, and treatment adjustment.
引用
收藏
页码:828 / 839
页数:12
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