Enhanced time-frequency analysis of VAG signals by segmentation and denoising algorithm

Author

Kim, Kwang Soon ; Seo, Jae Hyun ; Kang, Jin U. ; Song, C.G.

Author_Institution

Sch. of Electron. & Inf. Eng., Chonbuk Nat. Univ., Jeonju

Volume

44

Issue

20

fYear

2008

Firstpage

1184

Lastpage

1185

Abstract

An enhanced time-frequency analysis of vibroarthrographic (VAG) signals is devised using segmentation by the dynamic time warping and denoising algorithm by the singular value decomposition, and the normal and abnormal VAG signals are classified by a back-propagation neural network. A total of 1408 VAG segments (normal 1031, abnormal 377) were used for evaluating the performance of the devised method and, consequently, the average accuracy was 92.0 + 1.6% (ranging from 89.4 to 95.4). This method could be used as a complementary tool for the non-invasive diagnosis of joint disorders.

Keywords

backpropagation; biomechanics; medical signal processing; neural nets; orthopaedics; patient diagnosis; signal classification; signal denoising; singular value decomposition; time-frequency analysis; VAG signal classification; back-propagation neural network; dynamic time warping; enhanced time-frequency analysis; joint disorders; noninvasive diagnosis; signal denoising algorithm; signal segmentation algorithm; singular value decomposition; vibroarthrographic signals;

fLanguage

English

Journal_Title

Electronics Letters

Publisher

iet

ISSN

0013-5194

Type

jour

DOI

10.1049/el:20081758

Filename

4635004