Entropy-based feature extraction and classification of vibroarthographic signal using complete ensemble empirical mode decomposition with adaptive noise
Entropy-based feature extraction and classification of vibroarthographic signal using complete ensemble empirical mode decomposition with adaptive noise
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DOI：
10.1049/iet-smt.2017.0284
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发表时间：
2018-05-01
影响因子：
1.4
通讯作者：
Agrawal, Anita
中科院分区：
文献类型：
--
作者：
Nalband, Saif;Prince, Amalin;Agrawal, Anita
膝关节疾病的计算机辅助诊断的非侵入性方法提供了一个有效的工具。本研究的目的是利用非线性信号处理技术分析振动关节造影（VAG）信号。这项研究包括不同的基于熵的特征提取技术，以获得高度可区分的功能。作者提出了一种非线性的方法，称为完整的合奏经验模式分解与自适应白色噪声的VAG信号分解成本征模式函数（IMF）。基于熵的特征，包括近似熵，样本熵，香农熵，Renyi熵，Tsallis熵和排列熵（PeEn）计算从占主导地位的IMF和重建的VAG信号。这些提取的特征作为输入给作为分类器的最小二乘支持向量机。结果表明，PeEn表现更好，相对于其他熵。PeEn的分类准确率为86.61%，马修斯相关系数为0.7082。分析了熵的计算复杂性。结果表明，PeEn算法的计算复杂度为O（N），是一种简单、鲁棒、计算量小的特征提取方法。使用非线性预处理和基于熵的特征对VAG信号进行分析可以提供高度可区分的特征，用于准确检测膝关节疾病。
Non-invasive methods accomplished by a computer aided diagnosis of knee-joint disorders provide an effective tool. The objective of this study is to analyse vibroarthographic (VAG) signals using non-linear signal processing technique. This study includes different entropy-based feature extraction techniques to attain highly distinguishable features. The authors proposed to use a non-linear method known as complete ensemble empirical mode decomposition with adaptive white noise to decompose the VAG signals into intrinsic mode functions (IMFs). Entropy-based features involving approximate entropy, sample entropy, Shannon entropy, Renyi entropy, Tsallis entropy and permutation entropy (PeEn) are computed from dominant IMFs and reconstructed VAG signals. These extracted features are given as input to the least squares support vector machine as a classifier. The results illustrated that PeEn performed better with respect to other entropies. PeEn gives a classification accuracy of 86.61% and Matthews correlation coefficient of 0.7082. The computational complexity of entropies was also analysed. Results inferred that PeEn has a computational complexity of O(N) provided a simple, robust and low computational feature extraction technique. Analysis of VAG signals using non-linear preprocessing and entropy-based features can provide highly distinguishable features for accurate detection of knee-joint disorders.