PDF(Pubmed)
Abstract：
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.
摘要：
中风代表一种严重的医疗状况，其特征是流向大脑的血液突然中断，导致细胞损伤或死亡。中风对个体的影响可以从轻度损伤到严重残疾。中风的治疗通常集中在步态康复上。值得注意的是,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，分别,在步行过程中可以提供有关病理性步态状况的信息。同时测量EMG和运动学可以帮助理解特定肌肉对步态不同阶段的贡献中的功能障碍。为了这个目标,复杂性度量(例如，样本熵；近似熵；谱熵）应用于EMG和运动学已被证明可有效识别异常情况。此外，肌电图和运动学之间的条件熵可以识别步态数据和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，根据运动学和EMG复杂性度量来区分中风个体与健康对照。应用于EMG度量的立方支持向量机提供了最佳的分类结果，达到了99.85％的准确性。这种方法可以帮助临床医生监测中风患者运动障碍的恢复。
