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Detection of knee osteoarthritis based on recurrence quantification analysis, fuzzy entropy and shallow classifiers

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Abstract

Knee osteoarthritis (OA) is the most common joint disorder which results in mobility impairment and altered gait patterns. For the purpose of developing an automatic and highly accurate diagnosis system for knee OA, this study investigated the classification capability of different dynamical features extracted from gait kinematic signals when evaluating their impact on different classification models. A general feature extraction framework was proposed and various dynamical features, such as recurrence rate, determinism and entropy from the recurrence quantification analysis (RQA), Fuzzy entropy and statistical analysis, were included. Different shallow classifiers, including support vector machine (SVM) classifier, K-nearest neighbor (KNN), naive Bayes (NB) classifier, decision tree (DT) and ensemble learning based Adaboost (ELA) classifier, derived for discriminant analysis of multiple dynamical gait features were evaluated on the classification accuracy for a comparative study. The effectiveness of this strategy was verified using a dataset of tibiofemoral joint angle and translation waveforms from 26 patients with knee OA and 26 age-matched asymptomatic healthy controls (HCs). When evaluated with two-fold and leave-one-subject-out cross-validation methods, the highest classification accuracy for discriminating between groups of patients with knee OA and HCs was reported to be \(92.31\%\) and \(100\%\), respectively, by using the SVM classifier. Compared with other state-of-the-art methods, the results demonstrate superior performance and support the validity of the proposed method.

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Data Availability

The datasets used and/or analyzed during in current study are available from the corresponding author on reasonable requests.

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Funding

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61773194, 31700880) and by the Natural Science Foundation of Fujian Province (Grant No. 2023J01966).

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Authors and Affiliations

  1. School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan, 364012, People’s Republic of China

    Wei Zeng

  2. Department of Orthopedics, Guangdong Provincial People’s Hospital, Guangzhou, 510080, People’s Republic of China

    Limin Ma & Yu Zhang

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  1. Wei Zeng
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Correspondence to Wei Zeng or Limin Ma.

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Ethics approval for this study was obtained from the institutional ethics committees of Guangzhou General Hospital of Guangzhou Military Command. All patients and asymptomatic subjects were included based on their consent forms.

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Zeng, W., Ma, L. & Zhang, Y. Detection of knee osteoarthritis based on recurrence quantification analysis, fuzzy entropy and shallow classifiers. Multimed Tools Appl 83, 11977–11998 (2024). https://doi.org/10.1007/s11042-023-15772-5

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