Stroke patients often suffer from spasticity. Before treatment of spasticity, there are often practical demands for objective and quantitative assessment of muscle spasticity. However, the common quantitative spasticity assessment method, the tonic stretch reflex threshold (TSRT), is time-consuming and complicated to implement due to the requirement of multiple passive stretches. To evaluate spasticity conveniently, a novel spasticity evaluation method based on surface electromyogram (sEMG) signals and adaptive neuro fuzzy inference system (i.e., the sEMG-ANFIS method) was presented in this paper. Eleven stroke patients with spasticity and four healthy subjects were recruited to participate in the experiment. During the experiment, the Modified Ashworth scale (MAS) scores of each subject was obtained and sEMG signals from four elbow flexors or extensors were collected from several times (4–5) repetitions of passive stretching. Four time-domain features (root mean square, the zero-cross rate, the wavelength and a 4th-order autoregressive model coefficient) and one frequency-domain feature (the mean power frequency) were extracted from the collected sEMG signals to reflect the spasticity information. Using the ANFIS classifier, excellent regression performance was achieved [mean accuracy = 0.96, mean root-mean-square error (RMSE) = 0.13], outperforming the classical TSRT method (accuracy = 0.88, RMSE = 0.28). The results showed that the sEMG-ANFIS method not only has higher accuracy but also is convenient to implement by requiring fewer repetitions (4–5) of passive stretches. The sEMG-ANFIS method can help stroke patients develop proper rehabilitation training programs and can potentially be used to provide therapeutic feedback for some new spasticity interventions, such as shockwave therapy and repetitive transcranial magnetic stimulation.

中文翻译：

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基于表面肌电信号和自适应神经模糊推理系统的痉挛定量评估新方法

中风患者经常患有痉挛。在治疗痉挛之前，通常需要对肌肉痉挛进行客观和定量的评估。然而，常见的定量痉挛评估方法，强直牵张反射阈值 (TSRT)，由于需要多次被动牵伸，实施起来既耗时又复杂。为了方便评估痉挛状态，本文提出了一种基于表面肌电信号和自适应神经模糊推理系统的痉挛状态评估方法（即sEMG-ANFIS方法）。招募了 11 名患有痉挛的中风患者和 4 名健康受试者参与该实验。在实验过程中，获得每个受试者的改良 Ashworth 量表 (MAS) 分数，并从多次 (4-5) 次重复被动拉伸中收集来自四个肘屈肌或伸肌的 sEMG 信号。从收集到的sEMG信号中提取了四个时域特征（均方根、过零率、波长和四阶自回归模型系数）和一个频域特征（平均功率频率）以反映痉挛状态信息。使用 ANFIS 分类器，实现了出色的回归性能 [平均准确度 = 0.96，平均均方根误差 (RMSE) = 0.13]，优于经典 TSRT 方法（准确度 = 0.88，RMSE = 0.28）。结果表明，sEMG-ANFIS 方法不仅具有更高的准确性，而且由于需要较少重复（4-5）次被动拉伸而易于实施。