Representation of fluctuation features in pathological knee joint vibroarthrographic signals using kernel density modeling method

Yang, Shanshan; Cai, Suxian; Zheng, Fang; Wu, Yunfeng; Liu, Kaizhi; Wu, Meihong; Zou, Quan; Chen, Jian

doi:10.1016/j.medengphy.2014.07.008

Cited by 43 publications

(22 citation statements)

References 29 publications

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“…34,35 In addition, wearable motion sensors have also been used to assess joint stability tests such as assessment of ACL insufficiency by measuring rotational rate and peak accelerations with an IMU applied in the Pivot Shift test. 36 Furthermore, IMUs have been applied to measure shoulder joint function before and after orthopaedic interventions.…”

Section: Physical Functionmentioning

confidence: 99%

Evaluating physical function and activity in the elderly patient using wearable motion sensors

Grimm

Bolink

2016

EFORT Open Reviews

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Wearable sensors, in particular inertial measurement units (IMUs) allow the objective, valid, discriminative and responsive assessment of physical function during functional tests such as gait, stair climbing or sit-to-stand.Applied to various body segments, precise capture of time-to-task achievement, spatiotemporal gait and kinematic parameters of demanding tests or specific to an affected limb are the most used measures.In activity monitoring (AM), accelerometry has mainly been used to derive energy expenditure or general health related parameters such as total step counts.In orthopaedics and the elderly, counting specific events such as stairs or high intensity activities were clinimetrically most powerful; as were qualitative parameters at the ‘micro-level’ of activity such as step frequency or sit-stand duration.Low cost and ease of use allow routine clinical application but with many options for sensors, algorithms, test and parameter definitions, choice and comparability remain difficult, calling for consensus or standardisation.Cite this article: Grimm B, Bolink S. Evaluating physical function and activity in the elderly patient using wearable motion sensors. EFORT Open Rev 2016;1:112–120. DOI: 10.1302/2058-5241.1.160022.

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Section: Physical Functionmentioning

confidence: 99%

Evaluating physical function and activity in the elderly patient using wearable motion sensors

Grimm

Bolink

2016

EFORT Open Reviews

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“…The multiple classifier fusion system input with the combination of FF, S, K, H, TC, and VMS features may provide a slight improvement of area value 0.9484 under the ROC curve [41]. Yang et al [46] extracted the features of fractal scaling index and averaged envelope amplitude to describe the subtle fluctuations in VAG signals. The classification results of the Bayesian decision rule based on these two fluctuation features (accuracy: 88 %, A z : 0.957) [46] were better than the same classifier with the input features of FF and VMS (accuracy: 86.67 %, A z : 0.9096) [45].…”

Section: Vag Signal Classification Results Comparisonmentioning

confidence: 99%

“…Yang et al [46] extracted the features of fractal scaling index and averaged envelope amplitude to describe the subtle fluctuations in VAG signals. The classification results of the Bayesian decision rule based on these two fluctuation features (accuracy: 88 %, A z : 0.957) [46] were better than the same classifier with the input features of FF and VMS (accuracy: 86.67 %, A z : 0.9096) [45]. To improve the classification performance, Cai et al [2] proposed the dynamic weighted classifier fusion system to distinguish VAG signals.…”

Section: Vag Signal Classification Results Comparisonmentioning

confidence: 99%

Feature Computing and Signal Classifications

2014

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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“…Precision and recall can also be used as evaluation indicators in tests of pattern recognition models. Precision is expressed as the ratio of the correctly predicted values for the entire positive data set and recall reflects the number correctly judged as positive examples in the positive example test set [36]. The above three indicators are expressed in

\begin{matrix} T17 \\ A C C = \frac{T P + T N}{T P + F P + T N + F N}, \\ p r e c i s i o n = \frac{T P}{T P + F P}, \\ r e c a l l = \frac{T P}{T P + F N} . \end{matrix}

…”

Section: Mirna Identification With Bp Neural Networkmentioning

confidence: 99%

BP Neural Network Could Help Improve Pre-miRNA Identification in Various Species

Jiang

Zhang

Xuan

et al. 2016

BioMed Research International

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MicroRNAs (miRNAs) are a set of short (21–24 nt) noncoding RNAs that play significant regulatory roles in cells. In the past few years, research on miRNA-related problems has become a hot field of bioinformatics because of miRNAs' essential biological function. miRNA-related bioinformatics analysis is beneficial in several aspects, including the functions of miRNAs and other genes, the regulatory network between miRNAs and their target mRNAs, and even biological evolution. Distinguishing miRNA precursors from other hairpin-like sequences is important and is an essential procedure in detecting novel microRNAs. In this study, we employed backpropagation (BP) neural network together with 98-dimensional novel features for microRNA precursor identification. Results show that the precision and recall of our method are 95.53% and 96.67%, respectively. Results further demonstrate that the total prediction accuracy of our method is nearly 13.17% greater than the state-of-the-art microRNA precursor prediction software tools.

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Representation of fluctuation features in pathological knee joint vibroarthrographic signals using kernel density modeling method

Cited by 43 publications

References 29 publications

Evaluating physical function and activity in the elderly patient using wearable motion sensors

Evaluating physical function and activity in the elderly patient using wearable motion sensors

Feature Computing and Signal Classifications

BP Neural Network Could Help Improve Pre-miRNA Identification in Various Species

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