Romano Francesco, Formenti Damiano, Cardone Daniela, Russo Emanuele Francesco, Castiglioni Paolo, Merati Giampiero, Merla Arcangelo, Perpetuini David
Department of Engineering and Geology, University G. D'Annunzio of Chieti-Pescara, 65127 Pescara, Italy.
Department of Biotechnology and Life Sciences, University of Insubria, 21100 Varese, Italy.
Entropy (Basel). 2024 Jul 7;26(7):578. doi: 10.3390/e26070578.
A stroke represents a significant medical condition characterized by the sudden interruption of blood flow to the brain, leading to cellular damage or death. The impact of stroke on individuals can vary from mild impairments to severe disability. Treatment for stroke often focuses on gait rehabilitation. Notably, assessing muscle activation and kinematics patterns using electromyography (EMG) and stereophotogrammetry, respectively, during walking can provide information regarding pathological gait conditions. The concurrent measurement of EMG and kinematics can help in understanding disfunction in the contribution of specific muscles to different phases of gait. To this aim, complexity metrics (e.g., sample entropy; approximate entropy; spectral entropy) applied to EMG and kinematics have been demonstrated to be effective in identifying abnormal conditions. Moreover, the conditional entropy between EMG and kinematics can identify the relationship between gait data and muscle activation patterns. This study aims to utilize several machine learning classifiers to distinguish individuals with stroke from healthy controls based on kinematics and EMG complexity measures. The cubic support vector machine applied to EMG metrics delivered the best classification results reaching 99.85% of accuracy. This method could assist clinicians in monitoring the recovery of motor impairments for stroke patients.
中风是一种严重的医学病症，其特征是脑部血液供应突然中断，导致细胞损伤或死亡。中风对个体的影响从轻度损伤到严重残疾不等。中风治疗通常侧重于步态康复。值得注意的是，在行走过程中分别使用肌电图（EMG）和立体摄影测量法评估肌肉激活和运动学模式，可以提供有关病理性步态状况的信息。同时测量EMG和运动学有助于了解特定肌肉在步态不同阶段的贡献中存在的功能障碍。为此，已证明应用于EMG和运动学的复杂性指标（例如样本熵、近似熵、频谱熵）在识别异常状况方面是有效的。此外，EMG和运动学之间的条件熵可以识别步态数据与肌肉激活模式之间的关系。本研究旨在利用几种机器学习分类器，基于运动学和EMG复杂性测量来区分中风患者与健康对照者。应用于EMG指标的立方支持向量机给出了最佳分类结果，准确率达到99.85%。该方法可以帮助临床医生监测中风患者运动功能障碍的恢复情况。
