Maranesi Elvira, Barbarossa Federico, Biscetti Leonardo, Benadduci Marco, Casoni Elisa, Barboni Ilaria, Lattanzio Fabrizia, Fantechi Lorenzo, Fornarelli Daniela, Paci Enrico, Mecozzi Sara, Sallei Manuela, Giannoni Mirko, Pelliccioni Giuseppe, Riccardi Giovanni Renato, Di Donna Valentina, Bevilacqua Roberta
Scientific Direction, IRCCS INRCA, Ancona, Italy.
Unit of Neurology, IRCCS INRCA, Ancona, Italy.
Front Aging. 2025 May 22;6:1562355. doi: 10.3389/fragi.2025.1562355. eCollection 2025.
Stroke is a significant global public health challenge, ranking as the second leading cause of death after heart disease. One of the most debilitating consequences for stroke survivors is the restriction of mobility and walking, which greatly impacts their quality of life. The scientific literature extensively details the characteristics of post-stroke gait, which differs markedly from physiological walking in terms of speed, symmetry, balance control, and biomechanical parameters. This study aims to analyze the gait parameters of stroke survivors, considering the type of stroke and the affected cerebral regions, with the goal of identifying specific gait biomarkers to facilitate the design of personalized and effective rehabilitation programs.
The research focuses on 45 post-stroke patients who experienced either hemorrhagic or ischemic strokes, categorizing them based on the location of brain damage (cortical-subcortical, corona radiata, and basal ganglia). Gait analysis was conducted using the GaitRite system, measuring 39 spatio-temporal parameters.
Statistical tests revealed no significant differences, but Principal Component Analysis identified a dominant structure. Machine learning (ML) algorithms-Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbors (KNN)-were employed for classification, with RF demonstrating superior performance in accuracy, precision, recall (all exceeding 85%), and F1 score compared to SVM and KNN. Results indicated ML models could identify stroke types based on gait variables when traditional tests could not. Notably, RF outperformed others, suggesting its efficacy in handling complex and nonlinear data relationships.
The clinical implication emphasized a connection between gait parameters and cerebral lesion location, notably linking basal ganglia lesions to prolonged double support time. This underscores the basal ganglia's role in motor control, sensory processing, and postural control, highlighting the importance of sensory input in post-stroke rehabilitation.
中风是一项重大的全球公共卫生挑战，是仅次于心脏病的第二大死因。中风幸存者最使人衰弱的后果之一是行动和行走受限，这对他们的生活质量有很大影响。科学文献广泛详述了中风后步态的特征，其在速度、对称性、平衡控制和生物力学参数方面与生理行走有显著差异。本研究旨在分析中风幸存者的步态参数，考虑中风类型和受影响的脑区，目标是识别特定的步态生物标志物，以促进个性化且有效的康复方案的设计。
该研究聚焦于45名经历过出血性或缺血性中风的中风后患者，根据脑损伤位置（皮质 - 皮质下、放射冠和基底神经节）对他们进行分类。使用GaitRite系统进行步态分析，测量39个时空参数。
统计检验未显示出显著差异，但主成分分析确定了一个主导结构。使用机器学习（ML）算法——随机森林（RF）、支持向量机（SVM）和k近邻（KNN）进行分类，与SVM和KNN相比，RF在准确率、精确率、召回率（均超过85%）和F1分数方面表现出卓越性能。结果表明，当传统测试无法做到时，ML模型可以根据步态变量识别中风类型。值得注意的是，RF优于其他模型，表明其在处理复杂和非线性数据关系方面的有效性。
临床意义强调了步态参数与脑损伤位置之间的联系，尤其将基底神经节损伤与延长的双支撑时间联系起来。这突出了基底神经节在运动控制、感觉处理和姿势控制中的作用，强调了感觉输入在中风后康复中的重要性。
