Fractal analysis of knee-joint vibroarthrographic signals via power spectral analysis

Rangayyan, Rangaraj M.; Oloumi, Faraz; Wu, Yunfeng; Cai, Suxian

doi:10.1016/j.bspc.2012.05.004

Cited by 82 publications

(56 citation statements)

References 22 publications

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“…Rangayyan and Wu [27] used the Parzen-window method to establish the PDF models of knee joint vibroarthrographic (VAG) signals, and computed the Shannon entropy of the PDFs for the VAG signals with knee joint disorders. The related studies [27,29,30] demonstrated that the performance of classifiers can be greatly improved with the statistical parameters computed from the PDFs and the fractal dimensions of power spectral density. It is positively believed that the nonparametric PDF modeling method has high potential in the statistical analysis of biomedical signals, and the measures of gait and postural parameters for other movement disorders.…”

Section: Discussionmentioning

confidence: 99%

Statistical Analysis of Gait Maturation in Children Using Nonparametric Probability Density Function Modeling

Xiang

Cai

Yang

et al. 2013

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Analysis of gait dynamics in children may help understand the development of neuromuscular control and maturation of locomotor function. This paper applied the nonparametric Parzen-window estimation method to establish the probability density function (PDF) models for the stride interval time series of 50 children (25 boys and 25 girls). Four statistical parameters, in terms of averaged stride interval (ASI), variation of stride interval (VSI), PDF skewness (SK), and PDF kurtosis (KU), were computed with the Parzen-window PDFs to study the maturation of stride interval in children. By analyzing the results of the children in three age groups (aged 3-5 years, 6-8 years, and 10-14 years), we summarize the key findings of the present study as follows. (1) The gait cycle duration, in terms of ASI, increases until 14 years of age. On the other hand, the gait variability, in terms of VSI, decreases rapidly until 8 years of age, and then continues to decrease at a slower rate. (2) The SK values of both the histograms and Parzen-window PDFs for all of the three age groups are positive, which indicates an imbalance in the stride interval distribution within an age group. However, such an imbalance would be meliorated when the children grow up. (3) The KU values of both the histograms and Parzen-window PDFs decrease with the body growth in children, which suggests that the musculoskeletal growth enables the children to modulate a gait cadence with ease. (4) The SK and KU results also demonstrate the superiority of the Parzen-window PDF estimation method to the Gaussian distribution modeling, for the study of gait maturation in children.

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Section: Discussionmentioning

confidence: 99%

Statistical Analysis of Gait Maturation in Children Using Nonparametric Probability Density Function Modeling

Xiang

Cai

Yang

et al. 2013

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“…Park and Sandberg [27], and Hornik et al [14] have justified in theory that any continuous function on a compact interval can be interpolated with arbitrary accuracy by a well-devised RBFN that contains a sufficiently large number of hidden neurons. Although there is not yet a rigorous theoretical framework that specifies the routine how to determine an optimal RBFN structure with the stated approximation properties, the RBFN has been extensively applied in analysis of VAG signal patterns [28,30,32].…”

Section: Radial Basis Function Networkmentioning

confidence: 99%

Feature Computing and Signal Classifications

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Knee Joint Vibroarthrographic Signal Processing and Analysis

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In this chapter, we describe the feature computing and pattern analysis methods for VAG signal classifications. The purpose of feature selection is to study the feature correlations and then exclude the redundant features before pattern classifications. The reduction of feature dimensions may avoid excessive computation expenses, such that the pattern analysis methods based on the most informative features can also achieve favorable classification results. The signal classification methods include Fisher's linear discriminant analysis, radial basis function network, Vapnik and least-squares support vector machines, Bayesian decision rule, and multiple classifier fusion systems. The text also presents the common diagnostic performance techniques such as cross-validation, confusion matrix, and receiver operating characteristic curves. Finally, we review the state-ofthe-art methods for the VAG signal classifications and compare the results reported in recent literature.

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“…The knee joint VAG signals analyzed in the present work were selected from the dataset used in a few previous related studies [10,[12][13][14][15]. The signals were recorded from 75 subjects (47 healthy volunteers and 28 patients with knee joint disorders).…”

Section: Datasetmentioning

confidence: 99%

“…Therefore, we employed the SVM with polynomial kernels to perform the knee joint VAG signal classification task in the present work. The polynomial kernel is expressed as [30]: (14) where d denotes the degree of the polynomial and t represents the intercept. We searched the polynomial kernel parameters in the range from 1 to 10, and chose d = 3 and t = 4, which could make the SVM achieve the best classification performance.…”

Section: Support Vector Machinementioning

confidence: 99%

“…With the statistical features extracted [12][13][14][15], computational algorithms can be utilized to distinguish anomalous patterns associated with pathology [10,[16][17][18]. The aim of the present study is to use the kernel density estimation method with two-dimensional Gaussian kernels to represent the knee joint VAG signals in the bivariate feature space.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Knee Joint Vibration Signals Using Bivariate Feature Distribution Estimation and Maximal Posterior Probability Decision Criterion

Wu¹,

Cai²,

Yang³

et al. 2013

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Analysis of knee joint vibration or vibroarthrographic (VAG) signals using signal processing and machine learning algorithms possesses high potential for the noninvasive detection of articular cartilage degeneration, which may reduce unnecessary exploratory surgery. Feature representation of knee joint VAG signals helps characterize the pathological condition of degenerative articular cartilages in the knee. This paper used the kernel-based probability density estimation method to model the distributions of the VAG signals recorded from healthy subjects and patients with knee joint disorders. The estimated densities of the VAG signals showed explicit distributions of the normal and abnormal signal groups, along with the corresponding contours in the bivariate feature space. The signal classifications were performed by using the Fisher's linear discriminant analysis, support vector machine with polynomial kernels, and the maximal posterior probability decision criterion. The maximal posterior probability decision criterion was able to provide the total classification accuracy of 86.67% and the area (A z ) of 0.9096 under the receiver operating characteristics curve, which were superior to the results obtained by either the Fisher's linear discriminant analysis (accuracy: 81.33%, A z : 0.8564) or the support vector machine with polynomial kernels (accuracy: 81.33%, A z : 0.8533). Such results demonstrated the merits of the bivariate feature distribution estimation and the superiority of the maximal posterior probability decision criterion for analysis of knee joint VAG signals.

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Fractal analysis of knee-joint vibroarthrographic signals via power spectral analysis

Cited by 82 publications

References 22 publications

Statistical Analysis of Gait Maturation in Children Using Nonparametric Probability Density Function Modeling

Statistical Analysis of Gait Maturation in Children Using Nonparametric Probability Density Function Modeling

Feature Computing and Signal Classifications

Classification of Knee Joint Vibration Signals Using Bivariate Feature Distribution Estimation and Maximal Posterior Probability Decision Criterion

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