Knee Joint Pathology Screening Using Time-Domain Multidimensional Fusion Feature and Random Forest
Author:
Affiliation:
1. Northwestern Polytechnical University,School of Automation,Xi’an,China
2. The 705 Research Institute, CSIC,Xi’an,China
3. Xijing Orthopaedics Hospital of Fourth Military Medical University,Xi’an,China
Funder
Research and Development
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10054511/10054645/10055251.pdf?arnumber=10055251
Reference29 articles.
1. Analysis of knee-joint vibroarthographic signals using bandwidth-duration localized three-channel filter bank
2. Classification of Knee Joint Vibration Signals Using Bivariate Feature Distribution Estimation and Maximal Posterior Probability Decision Criterion
3. Knee osteoarthritis detection based on the combination of empirical mode decomposition and wavelet analysis
4. Analysis of Vibroarthrographic Signals with Features Related to Signal Variability and Radial-Basis Functions
5. Screening of knee-joint vibroarthrographic signals using probability density functions estimated with Parzen windows
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1. Optimized feature selection for enhanced accuracy in knee osteoarthritis detection and severity classification with machine learning;Biomedical Signal Processing and Control;2024-11
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