Fino Peter C, Mojdehi Ahmad R, Adjerid Khaled, Habibi Mohammad, Lockhart Thurmon E, Ross Shane D
Department of Mechanical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
Department of Biomedical Engineering and Mechanics, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
Ann Biomed Eng. 2016 May;44(5):1636-45. doi: 10.1007/s10439-015-1479-0. Epub 2015 Oct 13.
The health and financial cost of falls has spurred research to differentiate the characteristics of fallers and non-fallers. Postural stability has received much of the attention with recent studies exploring various measures of entropy. This study compared the discriminatory ability of several entropy methods at differentiating two paradigms in the center-of-pressure of elderly individuals: (1) eyes open (EO) vs. eyes closed (EC) and (2) fallers (F) vs. non-fallers (NF). Methods were compared using the area under the curve (AUC) of the receiver-operating characteristic curves developed from logistic regression models. Overall, multiscale entropy (MSE) and composite multiscale entropy (CompMSE) performed the best with AUCs of 0.71 for EO/EC and 0.77 for F/NF. When methods were combined together to maximize the AUC, the entropy classifier had an AUC of for 0.91 the F/NF comparison. These results suggest researchers and clinicians attempting to create clinical tests to identify fallers should consider a combination of every entropy method when creating a classifying test. Additionally, MSE and CompMSE classifiers using polar coordinate data outperformed rectangular coordinate data, encouraging more research into the most appropriate time series for postural stability entropy analysis.
跌倒造成的健康和经济成本促使人们开展研究，以区分跌倒者和未跌倒者的特征。姿势稳定性受到了广泛关注，近期的研究探索了各种熵的测量方法。本研究比较了几种熵方法在区分老年人压力中心的两种范式时的辨别能力：（1）睁眼（EO）与闭眼（EC），以及（2）跌倒者（F）与未跌倒者（NF）。使用逻辑回归模型生成的受试者工作特征曲线的曲线下面积（AUC）对各种方法进行比较。总体而言，多尺度熵（MSE）和复合多尺度熵（CompMSE）表现最佳，EO/EC的AUC为0.71，F/NF的AUC为0.77。当将各种方法组合起来以最大化AUC时，熵分类器在F/NF比较中的AUC为0.91。这些结果表明，试图创建用于识别跌倒者的临床测试的研究人员和临床医生在创建分类测试时应考虑结合使用每种熵方法。此外，使用极坐标数据的MSE和CompMSE分类器优于直角坐标数据，这鼓励对姿势稳定性熵分析最合适的时间序列进行更多研究。
