State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China.
Artif Intell Med. 2020 Jun;106:101877. doi: 10.1016/j.artmed.2020.101877. Epub 2020 May 19.
The clinical assessment after stroke depends on the rating scale, usually lack of quantitative feedback such as biomedical signal captured from stroke patients. This study attempts to develop a unified assessment framework for persons after stroke via surface electromyography (sEMG) bias from bilateral limbs, based on four types of selected movements, namely forward lift arm, lateral lift arm, forearm internal/external rotation, forearm pronation/supination. Eleven healthy subjects and six stroke patients are recruited to participate in the experiment to perform the bilateral-mirrored paradigm with six channels of sEMG signals recorded from each of their arms. The linear discriminant analysis (LDA), random forest algorithm (RF) and support vector machine (SVM) are adopted, trained and used for stroke patients qualitative recognition. The bilateral bias diagnosis algorithm (BBDA) is developed to evaluate the stroke severity quantitatively based on the similarity index (SI) of the sEMG. The results reveal that: (1) the sEMG feature bias of bilateral arms for stroke patients is different from that of healthy people; (2) the RF and SVM demonstrate a better performance with an average recognition accuracy of 0.92 ± 0.12 and 0.93 ± 0.12 than LDA (0.84 ± 0.20) in distinguishing stroke patients from healthy subjects; (3) there is a strong positive correlation between SI and the Fugl-Meyer score (r = 0.93). These research findings indicate that the dominant qualitative assessment after stroke could be complementary by its counterpart quantitative solutions, and stroke rehabilitation could be automated with less involvement of professional therapists.
脑卒中后的临床评估取决于评分量表，通常缺乏从脑卒中患者中捕获的生物医学信号等定量反馈。本研究试图通过从双侧肢体采集的表面肌电图（sEMG）偏置，基于四种选定的运动类型（向前抬起手臂、侧向抬起手臂、前臂内/外旋、前臂旋前/旋后），为脑卒中患者开发一个统一的评估框架。11 名健康受试者和 6 名脑卒中患者被招募参与实验，他们的双侧手臂分别记录 6 通道的 sEMG 信号，完成双侧镜像范式。采用线性判别分析（LDA）、随机森林算法（RF）和支持向量机（SVM）进行训练和使用，用于脑卒中患者的定性识别。基于相似指数（SI），开发双侧偏置诊断算法（BBDA）对脑卒中的严重程度进行定量评估。结果表明：（1）脑卒中患者双侧手臂的 sEMG 特征偏置与健康人不同；（2）RF 和 SVM 的表现优于 LDA（0.84 ± 0.20），平均识别准确率分别为 0.92 ± 0.12 和 0.93 ± 0.12；（3）SI 与 Fugl-Meyer 评分之间存在很强的正相关（r = 0.93）。这些研究结果表明，脑卒中后的主导定性评估可以通过其对应的定量解决方案进行补充，并且脑卒中康复可以实现自动化，减少对专业治疗师的依赖。
