School of Physics and Mechanical & Electrical Engineering, Longyan University, Longyan 364012, PR China.
Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI 02881, USA.
Neural Netw. 2019 Mar;111:64-76. doi: 10.1016/j.neunet.2018.12.012. Epub 2019 Jan 6.
Parkinson's disease (PD) is a common neurodegenerative disorder that affects human's quality of life, especially leading to locomotor deficits such as postural instability and gait disturbances. Gait signal is one of the best features to characterize and detect movement disorders caused by a malfunction in parts of the brain and nervous system of the patients with PD. Various classification approaches using spatiotemporal gait variables have been presented earlier to classify Parkinson's gait. In this study we propose a novel method for gait pattern classification between patients with PD and healthy controls, based upon phase space reconstruction (PSR), empirical mode decomposition (EMD) and neural networks. First, vertical ground reaction forces (GRFs) at specific positions of human feet are captured and then phase space is reconstructed. The properties associated with the gait system dynamics are preserved in the reconstructed phase space. Three-dimensional (3D) PSR together with Euclidean distance (ED) has been used. These measured parameters demonstrate significant difference in gait dynamics between the two groups and have been utilized to form a reference variable set. Second, reference variables are decomposed into Intrinsic Mode Functions (IMFs) using EMD, and the third IMFs are extracted and served as gait features. Third, neural networks are then used as the classifier to distinguish between patients with PD and healthy controls based on the difference of gait dynamics preserved in the gait features between the two groups. Finally, experiments are carried out on 93 PD patients and 73 healthy subjects to assess the effectiveness of the proposed method. By using 2-fold, 10-fold and leave-one-out cross-validation styles, the correct classification rates are reported to be 91.46%, 96.99% and 98.80%, respectively. Compared with other state-of-the-art methods, the results demonstrate superior performance and the proposed method can serve as a potential candidate for the automatic and non-invasive classification between patients with PD and healthy subjects.
帕金森病（PD）是一种常见的神经退行性疾病，它会影响人类的生活质量，尤其是导致姿势不稳和步态障碍等运动缺陷。步态信号是一种最好的特征，可以用来描述和检测由于大脑和神经系统部分功能障碍而导致的运动障碍。之前已经提出了各种使用时空步态变量的分类方法来对帕金森步态进行分类。在这项研究中，我们提出了一种基于相空间重构（PSR）、经验模态分解（EMD）和神经网络的 PD 患者与健康对照组之间步态模式分类的新方法。首先，我们采集人脚特定位置的垂直地面反力（GRF），然后进行相空间重构。在重构的相空间中保留了与步态系统动力学相关的特性。我们使用了三维（3D）PSR 和欧几里得距离（ED）。这些测量参数在两组之间的步态动力学中表现出显著差异，并被用作参考变量集。其次，使用 EMD 将参考变量分解为固有模态函数（IMF），提取第三 IMF 并作为步态特征。然后，神经网络被用作分类器，根据两组之间步态特征中保留的步态动力学差异，将 PD 患者和健康对照组区分开来。最后，我们对 93 名 PD 患者和 73 名健康受试者进行了实验，以评估所提出方法的有效性。通过使用 2 折、10 折和留一法交叉验证，报告的正确分类率分别为 91.46%、96.99%和 98.80%。与其他最先进的方法相比，结果表明该方法具有优越的性能，可作为 PD 患者和健康受试者自动、非侵入式分类的潜在候选方法。
