使用FEG-Pro进行短记录帕金森步态分析中的横断面可分性与纵向响应对比：北欧步行与适应性身体活动
Cross-Sectional Separability versus Longitudinal Response in Short-Record Parkinsonian Gait Analysis Using FEG-Pro: Nordic Walking and Adapted Physical Activity
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中文总结 AI 辅助
本研究用FEG-Pro分析帕金森患者的短步态记录，发现NW与APA干预无稳健纵向响应，横断面可分性不等同于干预效应，需纵向验证的生物标志物才可靠。
中文摘要 AI 辅助
【研究背景】机器学习对康复队列的分离并不能固有地确立干预措施的差异化响应。本研究采用 Forecast-Error Growth Profiling（FEG-Pro，预测误差增长轮廓）方法，区分帕金森病患者队列的横断面可分性，以及在接受北欧步行（Nordic Walking，NW）和适应性身体活动（Adapted Physical Activity，APA）后的受试者层面纵向变化。【方法】分析了24名参与者的公开短步态记录（NW组14人，APA组10人），将基线及12周随访时的下肢信号转换为FEG-Pro和预测误差分布熵描述符；采用经基线调整的敏感性分析及错误发现率（False-Discovery-Rate，FDR）校正，比较两组的个体变化评分（Δ=T1-T0）；此外，采用完全嵌套的机器学习管线评估多维变化向量是否可识别干预措施。【结果】自我选择的队列在基线时已存在临床差异；在提取的1098个特征中，经FDR校正和基线调整后，无任何特征表现出组间纵向变化的稳健差异；且基于多维变化向量的嵌套分类未表现出优于随机水平的性能（平均马修斯相关系数MCC=-0.178±0.228）；相比之下，探索性横断面模型在基线时达到峰值MCC值0.604，干预后达到0.554，成功分离队列。【结论】该队列未提供NW或APA具有模态特异性纵向响应的稳健证据；研究表明，自我选择队列的横断面可分性不得被解读为干预效应，真正的康复生物标志物需经过受试者层面的纵向验证、基线调整及防泄露评估。
英文摘要
\textbf{Objective:} Machine-learning separation of rehabilitation cohorts does not inherently establish a differential intervention response. This study used Forecast-Error Growth Profiling (FEG-Pro) to distinguish cross-sectional cohort separability from subject-level longitudinal change following Nordic Walking (NW) and Adapted Physical Activity (APA) in Parkinson's disease. \textbf{Methods:} Publicly available short gait records from 24 participants (NW=14, APA=10) were analyzed. Lower-limb signals at baseline and 12 weeks were transformed into FEG-Pro and Forecast-Error Distribution Entropy descriptors. We compared individual change scores ($Δ=T1-T0$) between groups using baseline-adjusted sensitivity analyses and false-discovery-rate (FDR) correction. Additionally, a fully nested machine-learning pipeline evaluated whether multidimensional change vectors could identify the intervention. \textbf{Results:} The self-selected cohorts already differed clinically at baseline. Among 1,098 extracted features, none exhibited robust between-group differences in longitudinal change after FDR correction or baseline adjustment. Furthermore, nested classification based on multidimensional change vectors failed to perform above chance (mean MCC = $-0.178 \pm 0.228$). In contrast, exploratory cross-sectional models separated the cohorts with peak MCC values of 0.604 at baseline and 0.554 post-intervention. \textbf{Conclusion:} This cohort did not provide robust evidence of modality-specific longitudinal responses to NW or APA. These findings demonstrate that cross-sectional separability of self-selected cohorts must not be interpreted as an intervention effect; true rehabilitation biomarkers require subject-level longitudinal validation, baseline adjustment, and leakage-safe evaluation.
发表机构
- School of Mechanical and Power Engineering, Zhengzhou University（郑州大学机械与动力工程学院）
- Institute of Physics and Technology, Petrozavodsk State University（彼得罗扎沃茨克国立大学物理与技术研究所）
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