Soleus muscle flap as coverage tissue is a possible surgical solution adopted to cover the wounds due to open fractures. Despite this procedure presents many clinical advantages, relatively poor information is available about the loss of functionality of triceps surae of the treated leg. In this study, a group of patients who underwent a soleus muscle flap surgical procedure has been analyzed through the heel rise test (HRT), in order to explore the triceps surae residual functionalities. A frequency band analysis was performed in order to assess whether the residual heads of triceps surae exhibit different characteristics with respect to both the non-treated lower limb and an age-matched control group. Then, an in-depth analysis based on a machine learning approach was proposed for discriminating between groups by generalizing across new unseen subjects. Experimental results showed the reliability of the proposed analyses for discriminating between-group at a specific time epoch and the high interpretability of the proposed machine learning algorithm allowed the temporal localization of the most discriminative frequency bands. Findings of this study highlighted that significant differences can be recognized in the myoelectric spectral characteristics between the treated and contralateral leg in patients who underwent soleus flap surgery. These experimental results may support the clinical decision-making for assessing triceps surae performance and for supporting the choice of treatment in plastic and reconstructive surgery.

比目鱼肌瓣作为覆盖组织是一种可能的手术解决方案，用于覆盖开放性骨折造成的伤口。尽管该手术具有许多临床优势，但关于受治疗腿小腿三头肌功能丧失的信息相对较少。在这项研究中，一组接受比目鱼肌瓣手术的患者通过足跟抬高试验 (HRT) 进行了分析，以探索小腿三头肌的残余功能。进行频带分析以评估小腿三头肌的残余头是否相对于未治疗的下肢和年龄匹配的对照组表现出不同的特征。然后，提出了基于机器学习方法的深入分析，通过概括新的未知主题来区分群体。实验结果表明，所提出的用于在特定时间时期区分组间的分析的可靠性以及所提出的机器学习算法的高可解释性允许对最具辨别力的频段进行时间定位。这项研究的结果强调，在接受比目鱼肌瓣手术的患者中，治疗侧腿和对侧腿之间的肌电频谱特征存在显着差异。这些实验结果可以支持评估小腿三头肌性能的临床决策，以及支持整形和重建手术治疗的选择。实验结果表明，所提出的用于在特定时间时期区分组间的分析的可靠性以及所提出的机器学习算法的高可解释性允许对最具辨别力的频段进行时间定位。这项研究的结果强调，在接受比目鱼肌瓣手术的患者中，治疗侧腿和对侧腿之间的肌电频谱特征存在显着差异。这些实验结果可以支持评估小腿三头肌性能的临床决策，以及支持整形和重建手术治疗的选择。实验结果表明，所提出的用于在特定时间时期区分组间的分析的可靠性以及所提出的机器学习算法的高可解释性允许对最具辨别力的频段进行时间定位。这项研究的结果强调，在接受比目鱼肌瓣手术的患者中，治疗侧腿和对侧腿之间的肌电频谱特征存在显着差异。这些实验结果可以支持评估小腿三头肌性能的临床决策，以及支持整形和重建手术治疗的选择。