Feature selection and classification methodology for the detection of knee-joint disorders

@article{Nalband2016FeatureSA,
  title={Feature selection and classification methodology for the detection of knee-joint disorders},
  author={Saif Nalband and Aditya Sundar and A. Amalin Prince and Anita Agarwal},
  journal={Computer methods and programs in biomedicine},
  year={2016},
  volume={127},
  pages={94-104}
}
Vibroarthographic (VAG) signals emitted from the knee joint disorder provides an early diagnostic tool. The nonstationary and nonlinear nature of VAG signal makes an important aspect for feature extraction. In this work, we investigate VAG signals by proposing a wavelet based decomposition. The VAG signals are decomposed into sub-band signals of different frequencies. Nonlinear features such as recurrence quantification analysis (RQA), approximate entropy (ApEn) and sample entropy (SampEn) are… CONTINUE READING