An enhanced algorithm for knee joint sound classification using feature extraction based on time-frequency analysis

  • Authors:

  • Keo Sik Kim;Jeong Hwan Seo;Jin U. Kang;Chul Gyu Song

  • Affiliations:

  • Division of Electronics and Information Engineering, Chonbuk National University, 664-14 Deokjin-dong, Jeonju, Jeonbuk 561-756, South Korea;School of Medicine, Dept. of Rehabilitation Medicine, Chonbuk National University, 42 Wonjam-5gil, Jeonju, Jeonbuk 561-712, South Korea;Department of Electrical and Computer Engineering, Johns Hopkins University, 3400 N. Charles St., Baltimore, MD 21218, USA;Division of Electronics and Information Engineering, Chonbuk National University, 664-14 Deokjin-dong, Jeonju, Jeonbuk 561-756, South Korea and Department of Electrical and Computer Engineering, J ...

  • Venue:
  • Computer Methods and Programs in Biomedicine
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

Vibroarthrographic (VAG) signals, generated by human knee movement, are non-stationary and multi-component in nature and their time-frequency distribution (TFD) provides a powerful means to analyze such signals. The objective of this paper is to improve the classification accuracy of the features, obtained from the TFD of normal and abnormal VAG signals, using segmentation by the dynamic time warping (DTW) and denoising algorithm by the singular value decomposition (SVD). VAG and knee angle signals, recorded simultaneously during one flexion and one extension of the knee, were segmented and normalized at 0.5Hz by the DTW method. Also, the noise within the TFD of the segmented VAG signals was reduced by the SVD algorithm, and a back-propagation neural network (BPNN) was used to classify the normal and abnormal VAG signals. The characteristic parameters of VAG signals consist of the energy, energy spread, frequency and frequency spread parameter extracted by the TFD. A total of 1408 segments (normal 1031, abnormal 377) were used for training and evaluating the BPNN. As a result, the average classification accuracy was 91.4 (standard deviation +/-1.7) %. The proposed method showed good potential for the non-invasive diagnosis and monitoring of joint disorders such as osteoarthritis.