Screening of knee-joint vibroarthrographic signals using statistical parameters and radial basis functions

Rangayyan, Rangaraj M.; Wu, Yunfeng

doi:10.1007/s11517-007-0278-7

Cited by 99 publications

(75 citation statements)

References 43 publications

(80 reference statements)

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“…The SK and KU are two parameters that usually measure the asymmetry and "peakedness" characteristics of a PDF [24,25]. The SK and KU can be calculated from the PDF moments as…”

Section: Statistical Parametersmentioning

confidence: 99%

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

Xiang

Cai

Yang

et al. 2013

Entropy

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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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“…The SK and KU are two parameters that usually measure the asymmetry and "peakedness" characteristics of a PDF [24,25]. The SK and KU can be calculated from the PDF moments as…”

Section: Statistical Parametersmentioning

confidence: 99%

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

Xiang

Cai

Yang

et al. 2013

Entropy

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“…The p value of the FF feature estimated by the Kruskal-Wallis test was lower than 0.01 [12], which implied that the distributions for the normal and abnormal VAG signals were also significantly different.…”

Section: Features Descriptionmentioning

confidence: 84%

“…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%

“…The Kruskal-Wallis test results showed that the VMS values of the normal and abnormal VAG signals reached the level of significant difference (p < 0.01) [13]. On the other hand, the form factor (FF) value was computed for each VAG signal to measure the shape complexity [12]. The FF is computed as the ratio of mobility of the first derivative of the signal M s ′ to the mobility of the signal itself M s [21]:…”

Section: Features Descriptionmentioning

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

Entropy

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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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KOA Pathology Screening Using Multi-channel VAG Signal Fusion Method

Yang

2022

Communications in Computer and Information Science

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Screening of knee-joint vibroarthrographic signals using statistical parameters and radial basis functions

Cited by 99 publications

References 43 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

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

KOA Pathology Screening Using Multi-channel VAG Signal Fusion Method

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