Modeling and classification of gait patterns between anterior cruciate ligament deficient and intact knees based on phase space reconstruction, Euclidean distance and neural networks.
Modeling and classification of gait patterns between anterior cruciate ligament deficient and intact knees based on phase space reconstruction, Euclidean distance and neural networks.
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基于相空间重建、欧氏距离和神经网络的前交叉韧带缺陷和完整膝关节步态模式的建模和分类
DOI：
10.1186/s12938-018-0594-1
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发表时间：
2018-11-01
影响因子：
3.9
通讯作者：
Zhang Y
中科院分区：
文献类型：
--
作者：
Wu W;Zeng W;Ma L;Yuan C;Zhang Y
研究背景前交叉韧带在稳定胫骨相对于股骨的平移和旋转中起着重要作用。前交叉韧带损伤会改变膝关节运动学，通常与步态模式的改变有关。本研究的目的是开发一种基于非线性特征和神经网络的新方法来区分前交叉韧带缺陷症(ACL-D)患者和正常对照(ACL-I)膝关节的步态模式。方法首先提取膝关节的旋转和平移参数，并采用相空间重构方法。在重构的相空间中保留了与步态系统动力学相关的属性。为了对ACL-D和ACL-I膝关节步态模式进行分类，采用了三维PSR和欧氏距离计算相结合的方法。这些测量的参数显示出两组之间在步态动力学方面的显著差异，并已被用于形成特征集。然后构建神经网络来识别步态动力学，并利用神经网络作为分类器，根据两组步态动力学的差异来区分ACL-D和ACL-I膝关节步态模式。结果在包含18名前交叉韧带损伤患者和28名健康对照的数据库上进行了实验，以评估所提出方法的有效性。结果表明，与其他最先进的方法相比，该方法能够检测出存在ACL缺陷时的步态变化，且具有较好的性能。该方法为前交叉韧带缺陷症患者与健康受试者之间的自动无创分类提供了一种潜在的候选方法。
BackgroundThe anterior cruciate ligament (ACL) plays an important role in stabilizing translation and rotation of the tibia relative to the femur. ACL injury alters knee kinematics and usually links to the alternation of gait patterns. The aim of this study is to develop a new method to distinguish between gait patterns of patients with anterior cruciate ligament deficient (ACL-D) knees and healthy controls with ACL-intact (ACL-I) knees based on nonlinear features and neural networks. Therefore ACL injury will be automatically and objectively detected.MethodsFirst knee rotation and translation parameters are extracted and phase space reconstruction (PSR) is employed. The properties associated with the gait system dynamics are preserved in the reconstructed phase space. For the purpose of classification of ACL-D and ACL-I knee gait patterns, three-dimensional (3D) PSR together with Euclidean distance computation has been used. These measured parameters show significant difference in gait dynamics between the two groups and have been utilized to form a feature set. Neural networks are then constructed to identify gait dynamics and are utilized as the classifier to distinguish between ACL-D and ACL-I knee gait patterns based on the difference of gait dynamics between the two groups.ResultsExperiments are carried out on a database containing 18 patients with ACL injury and 28 healthy controls to assess the effectiveness of the proposed method. By using the twofold and leave-one-subject-out cross-validation styles, the correct classification rates for ACL-D and ACL-I knees are reported to be 91.3and 95.65, respectively.ConclusionCompared with other state-of-the-art methods, the results demonstrate that gait alterations in the presence of ACL deficiency can be detected with superior performance. The proposed method is a potential candidate for the automatic and non-invasive classification between patients with ACL deficiency and healthy subjects.
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影响因子：
2.4
作者：
Andriacchi, TP;Dyrby, CO
通讯作者：
Dyrby, CO
影响因子：
3.8
作者：
Knoll, Z;Kocsis, L;Kiss, RM
通讯作者：
Kiss, RM
影响因子：
3.8
作者：
Atarod, Mohammad;Frank, Cyril B.;Shrive, Nigel G.
通讯作者：
Shrive, Nigel G.
影响因子：
4.6
作者：
Chen, Hsin-Chen;Wu, Chia-Hsing;Sun, Yung-Nien
通讯作者：
Sun, Yung-Nien
影响因子：
2.1
作者：
Gao, Bo;Cordova, Mitchell L.;Zheng, Naiquan (Nigel)
通讯作者：
Zheng, Naiquan (Nigel)