股直肌肌电图信号聚类:脑瘫患者蹲踞步态的数据驱动管理

Mehrdad Davoudi, Firooz Salami, Robert Reisig, Dimitrios Patikas, Sebastian Wolf
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引用次数: 0

摘要

本研究旨在探讨股直肌(RF)肌电图(EMG)聚类分析如何帮助更好地解读脑瘫(CP)患者的步态分析。CP 患者的回顾性步态数据被分为两组:初始检查(E1,881 名患者)和后续检查(E2,377 名患者)。研究人员收集了射频的包络格式肌电图数据。使用 PCA 和 PSO-K-means 组合算法,确定了主要的聚类。进一步将患者分为蹲踞、跳跃、回旋、僵硬和轻微步态,以便进行详细分析。这些聚类(标签)的特点是在挥杆中段(L1)有明显的肌电活动峰值,在站立(L2)时有长时间的肌电活动,在加载反应(L3)时有肌电活动峰值。值得注意的是,在 E1 和 E2 阶段,L2 分别占所有下蹲患者的 76% 和 92%。比较 L2-E1 和 L2-E2 中的蹲踞步态患者,出现了两个亚组:持续蹲踞的患者(G1)和在 E2 时有所改善的患者(G2)。在 20-45% 的步态中,G1 患者的射频最低活动量明显高于 G2 患者(p= 0.025)。蹲踞步态改善的机会更大,这可能与站立阶段射频活动较低有关。利用我们的研究结果,我们有可能建立一种方法来改善治疗 CP 患者的临床决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rectus Femoris Electromyography Signal Clustering: Data-Driven Management of Crouch Gait in Patients with Cerebral Palsy
This study aimed to investigate how electromyography (EMG) cluster analysis of the rectus femoris (RF) could help to better interpret gait analysis in patients with cerebral palsy (CP). The retrospective gait data of CP patients were categorized into two groups: initial examination (E1, 881 patients) and subsequent examination (E2, 377 patients). Envelope-formatted EMG data of RF were collected. Using PCA and a combined PSO-K-means algorithm, main clusters were identified. Patients were further classified into crouch, jump, recurvatum, stiff and mild gait for detailed analysis. The clusters (labels) were characterized by a significant peak EMG activity during mid-swing (L1), prolonged EMG activity during stance (L2), and a peak EMG activity during loading response (L3). Notably, L2 contained 76% and 92% of all crouch patients at E1 and E2, respectively. Comparing patients with a crouch gait pattern in L2-E1 and L2-E2, two subgroups emerged: patients with persistent crouch (G1) and patients showing improvement at E2 (G2). The minimum activity of RF during 20-45% of the gait was significantly higher (p= 0.025) in G1 than in G2. A greater chance of improvement from crouch gait might be associated with lower RF activity during the stance phase. Using our findings, we could potentially establish an approach to improve clinical decision-making regarding treatment of patients with CP.
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