摘要
目的 通过最大Lyapunov指数(the largest Lyapunov exponents,LLE)计算慢性前庭综合征(chronic vestibular syndrome,CVS)患者运动的非线性特征,并通过机器学习算法验证分类模型的有效性。方法 使用三维运动捕捉系统捕捉受试者的关节运动轨迹,通过LLE判断混沌态,计算混沌轨迹的特征作为输入,采用ID3决策树、Adaboost、C45决策树、贝叶斯分类、朴素贝叶斯、支持向量机7种分类器进行分类。结果 共有16个关节点的17组轨迹处在混沌态,实验组运动轨迹的平均能量、增强波长、峰度表现出显著性差异(P<0.05),ID3决策树分类器表现出了最优性能,预测精度、召回率、F_(1)分数均为100%。结论 混沌特征可能包含了CVS患者更多的个性差异,能够提高机器学习算法识别的准确性。研究结果可为CVS患者的早期识别和运动康复提供参考。
Objective To calculate the nonlinear features of motion in patients with chronic vestibular syndrome(CVS) using the largest Lyapunov exponent(LLE),and to verify the classification model's validity through machine learning algorithms.Methods A three-dimensional(3D) motion capture system was used to capture the joint motion trajectories of the subjects,which were determined using the LLE.The features of the chaotic trajectories were calculated as the input,and seven classifiers,namely the ID3 decision tree,Adaboost,C45 decision tree,Bayesian classification,Naive Bayes,and support vector machine,were used for classification.Results A total of 17 sets of trajectories from 16 joints were in the chaotic state,and the average energy,enhanced wavelength,and kurtosis of the motion trajectories in the experimental group showed significant differences(P<0.05).The ID3 decision tree classifier showed optimal performance with 100% prediction accuracy,recall,and F_1-score.Conclusions Chaotic features may contain high personality differences in patients with CVS and can improve the accuracy of machine learning algorithms for recognition.These findings provide a reference for early identification and motor rehabilitation of patients with CVS.
作者
海子睿
吕子阳
马英楠
高星
HAI Zirui;LÜZiyang;MA Yingnan;GAO Xing(School of Science,Beijing University of Posts and Telecommunications,Beijing 100876,China;Institute of Smart Ageing,Beijing Academy of Science and Technology,Beijing 100035,China)
出处
《医用生物力学》
CAS
CSCD
北大核心
2024年第1期106-110,共5页
Journal of Medical Biomechanics
基金
国家重点研发计划项目(2018YFC2001400)。
