@inproceedings{b7ba06717f234688901e93fc7f54fba0,
title = "Classification of normal and abnormal knee joint using back-propagation neural network",
abstract = "The objective of this paper is to classify vibroarthrographic (VAG) signals, generated during joint movement, according to the pathological condition using time-frequency analysis and back-propagation neural network. VAG signals were segmented at 0.5 Hz by dynamic time warping (DTW) algorithm and Noise within the time-frequency distribution (TFD) of segmented VAG signals was reduced by singular value decomposition (SVD) algorithm. The features of VAG signals consist of the energy parameter (EP), the energy spread parameter (ESP), the frequency parameter (FP) and the frequency spread parameter (FSP) by Wigner-Ville distribution (WVD) and the magnitude pattern, the mean and the median frequency by fast Fourier transform (FFT). As a result, the average of the classification accuracy was 92.3 ±0.9 \%. The proposed method showed good potential for non-invasive diagnosis of joint disorders.",
keywords = "Articular pathology, Back-propagation neural network, Dynamic time warping, Singular value decomposition, Vibroarthrography",
author = "Kim, \{K. S.\} and Seo, \{J. H.\} and Song, \{C. G.\}",
year = "2008",
language = "English",
isbn = "1601320558",
series = "Proceedings of the 2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008",
pages = "483--488",
booktitle = "Proceedings of the 2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008",
note = "2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008 ; Conference date: 14-07-2008 Through 17-07-2008",
}