Extraction and screening of Knee joint Vibroarthrographic signals using the independent component analysis method

Jien Chen Chen; Pi Cheng Tung; Shih Fong Huang; Shu Wei Wu; Shih Lin Lin

Extraction and screening of Knee joint Vibroarthrographic signals using the independent component analysis method

Jien Chen Chen, Pi Cheng Tung, Shih Fong Huang, Shu Wei Wu, Shih Lin Lin

機械工程學系

研究成果: 雜誌貢獻 › 期刊論文 › 同行評審

3 引文斯高帕斯（Scopus）

摘要

This study presents a novel method for the extraction and screening of knee joint vibroarthrographic (VAC) signals using an independent component analysis (ICA) technique. A time-frequency analysis technique of the extracted vibration signals is proposed to carry out knee joint diagnosis. The performance of the ICA technique is verified experimentally. Statistical pattern classification screening accuracy is 82.5% in VAC. The results confirm that ICA is a feasible approach for the noninvasive diagnosis and monitoring of articular cartilage pathology.

原文	???core.languages.en_GB???
頁（從 - 到）	7501-7518
頁數	18
期刊	International Journal of Innovative Computing, Information and Control
卷	8
發行號	11
出版狀態	已出版 - 2012

引用此

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abstract = "This study presents a novel method for the extraction and screening of knee joint vibroarthrographic (VAC) signals using an independent component analysis (ICA) technique. A time-frequency analysis technique of the extracted vibration signals is proposed to carry out knee joint diagnosis. The performance of the ICA technique is verified experimentally. Statistical pattern classification screening accuracy is 82.5% in VAC. The results confirm that ICA is a feasible approach for the noninvasive diagnosis and monitoring of articular cartilage pathology.",

keywords = "Independent component analysis, Vibroarthrographic",

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TY - JOUR

T1 - Extraction and screening of Knee joint Vibroarthrographic signals using the independent component analysis method

AU - Chen, Jien Chen

AU - Tung, Pi Cheng

AU - Huang, Shih Fong

AU - Wu, Shu Wei

AU - Lin, Shih Lin

PY - 2012

Y1 - 2012

N2 - This study presents a novel method for the extraction and screening of knee joint vibroarthrographic (VAC) signals using an independent component analysis (ICA) technique. A time-frequency analysis technique of the extracted vibration signals is proposed to carry out knee joint diagnosis. The performance of the ICA technique is verified experimentally. Statistical pattern classification screening accuracy is 82.5% in VAC. The results confirm that ICA is a feasible approach for the noninvasive diagnosis and monitoring of articular cartilage pathology.

AB - This study presents a novel method for the extraction and screening of knee joint vibroarthrographic (VAC) signals using an independent component analysis (ICA) technique. A time-frequency analysis technique of the extracted vibration signals is proposed to carry out knee joint diagnosis. The performance of the ICA technique is verified experimentally. Statistical pattern classification screening accuracy is 82.5% in VAC. The results confirm that ICA is a feasible approach for the noninvasive diagnosis and monitoring of articular cartilage pathology.

KW - Independent component analysis

KW - Vibroarthrographic

UR - http://www.scopus.com/inward/record.url?scp=84869770251&partnerID=8YFLogxK

M3 - 期刊論文

AN - SCOPUS:84869770251

SN - 1349-4198

VL - 8

SP - 7501

EP - 7518

JO - International Journal of Innovative Computing, Information and Control

JF - International Journal of Innovative Computing, Information and Control

IS - 11

ER -

Extraction and screening of Knee joint Vibroarthrographic signals using the independent component analysis method

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