Safi Khaled, Aly Wael Hosny Fouad, AlAkkoumi Mouhammad, Kanj Hassan, Ghedira Mouna, Hutin Emilie
Computer Science Department, Strasbourg University, 67081 Strasbourg, France.
College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait.
Bioengineering (Basel). 2022 Jun 28;9(7):283. doi: 10.3390/bioengineering9070283.
There has recently been increasing interest in postural stability aimed at gaining a better understanding of the human postural system. This system controls human balance in quiet standing and during locomotion. Parkinson's disease (PD) is the most common degenerative movement disorder that affects human stability and causes falls and injuries. This paper proposes a novel methodology to differentiate between healthy individuals and those with PD through the empirical mode decomposition (EMD) method. EMD enables the breaking down of a complex signal into several elementary signals called intrinsic mode functions (IMFs). Three temporal parameters and three spectral parameters are extracted from each stabilometric signal as well as from its IMFs. Next, the best five features are selected using the feature selection method. The classification task is carried out using four known machine-learning methods, KNN, decision tree, Random Forest and SVM classifiers, over 10-fold cross validation. The used dataset consists of 28 healthy subjects (14 young adults and 14 old adults) and 32 PD patients (12 young adults and 20 old adults). The SVM method has a performance of 92% and the Dempster-Sahfer formalism method has an accuracy of 96.51%.
最近，人们对姿势稳定性的兴趣日益浓厚，旨在更好地了解人体姿势系统。该系统在安静站立和运动过程中控制人体平衡。帕金森病（PD）是最常见的退行性运动障碍，会影响人体稳定性并导致跌倒和受伤。本文提出了一种通过经验模态分解（EMD）方法区分健康个体和帕金森病患者的新方法。EMD能够将复杂信号分解为几个称为固有模态函数（IMF）的基本信号。从每个稳定测量信号及其IMF中提取三个时间参数和三个频谱参数。接下来，使用特征选择方法选择最佳的五个特征。分类任务使用四种已知的机器学习方法（KNN、决策树、随机森林和支持向量机分类器）进行10折交叉验证。使用的数据集包括28名健康受试者（14名年轻人和14名老年人）和32名帕金森病患者（12名年轻人和20名老年人）。支持向量机方法的性能为92%，登普斯特-谢弗形式主义方法的准确率为96.51%。
