Entropy-Based Surface Electromyogram Feature Extraction for Knee Osteoarthritis Classification

Xin Chen; Jun Chen; Jie Liang; Yurong Li; Carol Ann Courtney; Yuan Yang

doi:10.1109/ACCESS.2019.2950665

Entropy-Based Surface Electromyogram Feature Extraction for Knee Osteoarthritis Classification

Xin Chen, Jun Chen, Jie Liang^*, Yurong Li, Carol Ann Courtney, Yuan Yang

^*Corresponding author for this work

Physical Therapy and Human Movement Sciences

Research output: Contribution to journal › Article › peer-review

15 Scopus citations

Abstract

Knee osteoarthritis (KOA) is one of the major causes of lower limb disability. This study aims to develop a computer-based approach to discriminate KOA individuals from controls by using entropy-based features, and therefore to provide an auxiliary, quantitative tool for KOA diagnosis. The surface EMG (sEMG) data were collected from the vastus lateralis, vastus medialis, biceps femoris, and semitendinosus when KOA participants and controls were walking barefoot on ground at a self-paced speed. We employed and compared three different entropy measures, including 1) approximate entropy, 2) sample entropy, 3) fuzzy entropy, for extracting KOA-related features from the sEMG signals for classification. The differences between the KOA group and healthy controls are primarily shown in the fuzzy entropy features extracted from the vastus medialis and biceps femoris muscle pair. Among all tested measures, the fuzzy entropy yielded the best performance in distinguishing KOA patients from controls, with 92% of accuracy, 91.43% of sensitivity and 93.33% of specificity. The results indicate that the fuzzy entropy method is applicable for extracting KOA-related features from sEMG, which can be developed as a sensitive metric for computer-assist diagnosis of knee osteoarthritis.

Original language	English (US)
Article number	8888169
Pages (from-to)	164144-164151
Number of pages	8
Journal	IEEE Access
Volume	7
DOIs	https://doi.org/10.1109/ACCESS.2019.2950665
State	Published - 2019

Funding

Corresponding authors: Jie Liang ([email protected]), Yurong Li ([email protected]), and Yuan Yang ([email protected]) This work was supported in part by the Fujian Province Nature Science Foundation of China under Grant 2019J01544, in part by the National Nature Science Foundation of China under Grant 61773124, in part by the International Science and Technology Cooperation Project of Fujian Province of China under Grant 2019I0003, in part by the Fuzhou Key Specialty Construction Project of China under Grant 201710272, and in part by the U.K.-China Industry Academia Partnership Programme\276.

Keywords

EMG
Knee osteoarthritis
classification
computer-assist diagnosis
entropy

ASJC Scopus subject areas

General Computer Science
General Materials Science
General Engineering

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10.1109/ACCESS.2019.2950665

Cite this

@article{fdc06e7e36964a92bdc2ab870ea136d8,

title = "Entropy-Based Surface Electromyogram Feature Extraction for Knee Osteoarthritis Classification",

abstract = "Knee osteoarthritis (KOA) is one of the major causes of lower limb disability. This study aims to develop a computer-based approach to discriminate KOA individuals from controls by using entropy-based features, and therefore to provide an auxiliary, quantitative tool for KOA diagnosis. The surface EMG (sEMG) data were collected from the vastus lateralis, vastus medialis, biceps femoris, and semitendinosus when KOA participants and controls were walking barefoot on ground at a self-paced speed. We employed and compared three different entropy measures, including 1) approximate entropy, 2) sample entropy, 3) fuzzy entropy, for extracting KOA-related features from the sEMG signals for classification. The differences between the KOA group and healthy controls are primarily shown in the fuzzy entropy features extracted from the vastus medialis and biceps femoris muscle pair. Among all tested measures, the fuzzy entropy yielded the best performance in distinguishing KOA patients from controls, with 92% of accuracy, 91.43% of sensitivity and 93.33% of specificity. The results indicate that the fuzzy entropy method is applicable for extracting KOA-related features from sEMG, which can be developed as a sensitive metric for computer-assist diagnosis of knee osteoarthritis.",

keywords = "EMG, Knee osteoarthritis, classification, computer-assist diagnosis, entropy",

author = "Xin Chen and Jun Chen and Jie Liang and Yurong Li and Courtney, {Carol Ann} and Yuan Yang",

note = "Funding Information: Corresponding authors: Jie Liang ([email protected]), Yurong Li ([email protected]), and Yuan Yang ([email protected]) This work was supported in part by the Fujian Province Nature Science Foundation of China under Grant 2019J01544, in part by the National Nature Science Foundation of China under Grant 61773124, in part by the International Science and Technology Cooperation Project of Fujian Province of China under Grant 2019I0003, in part by the Fuzhou Key Specialty Construction Project of China under Grant 201710272, and in part by the U.K.-China Industry Academia Partnership Programme\276. Publisher Copyright: {\textcopyright} 2013 IEEE.",

year = "2019",

doi = "10.1109/ACCESS.2019.2950665",

language = "English (US)",

volume = "7",

pages = "164144--164151",

journal = "IEEE Access",

issn = "2169-3536",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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TY - JOUR

T1 - Entropy-Based Surface Electromyogram Feature Extraction for Knee Osteoarthritis Classification

AU - Chen, Xin

AU - Chen, Jun

AU - Liang, Jie

AU - Li, Yurong

AU - Courtney, Carol Ann

AU - Yang, Yuan

N1 - Funding Information: Corresponding authors: Jie Liang ([email protected]), Yurong Li ([email protected]), and Yuan Yang ([email protected]) This work was supported in part by the Fujian Province Nature Science Foundation of China under Grant 2019J01544, in part by the National Nature Science Foundation of China under Grant 61773124, in part by the International Science and Technology Cooperation Project of Fujian Province of China under Grant 2019I0003, in part by the Fuzhou Key Specialty Construction Project of China under Grant 201710272, and in part by the U.K.-China Industry Academia Partnership Programme\276. Publisher Copyright: © 2013 IEEE.

PY - 2019

Y1 - 2019

N2 - Knee osteoarthritis (KOA) is one of the major causes of lower limb disability. This study aims to develop a computer-based approach to discriminate KOA individuals from controls by using entropy-based features, and therefore to provide an auxiliary, quantitative tool for KOA diagnosis. The surface EMG (sEMG) data were collected from the vastus lateralis, vastus medialis, biceps femoris, and semitendinosus when KOA participants and controls were walking barefoot on ground at a self-paced speed. We employed and compared three different entropy measures, including 1) approximate entropy, 2) sample entropy, 3) fuzzy entropy, for extracting KOA-related features from the sEMG signals for classification. The differences between the KOA group and healthy controls are primarily shown in the fuzzy entropy features extracted from the vastus medialis and biceps femoris muscle pair. Among all tested measures, the fuzzy entropy yielded the best performance in distinguishing KOA patients from controls, with 92% of accuracy, 91.43% of sensitivity and 93.33% of specificity. The results indicate that the fuzzy entropy method is applicable for extracting KOA-related features from sEMG, which can be developed as a sensitive metric for computer-assist diagnosis of knee osteoarthritis.

AB - Knee osteoarthritis (KOA) is one of the major causes of lower limb disability. This study aims to develop a computer-based approach to discriminate KOA individuals from controls by using entropy-based features, and therefore to provide an auxiliary, quantitative tool for KOA diagnosis. The surface EMG (sEMG) data were collected from the vastus lateralis, vastus medialis, biceps femoris, and semitendinosus when KOA participants and controls were walking barefoot on ground at a self-paced speed. We employed and compared three different entropy measures, including 1) approximate entropy, 2) sample entropy, 3) fuzzy entropy, for extracting KOA-related features from the sEMG signals for classification. The differences between the KOA group and healthy controls are primarily shown in the fuzzy entropy features extracted from the vastus medialis and biceps femoris muscle pair. Among all tested measures, the fuzzy entropy yielded the best performance in distinguishing KOA patients from controls, with 92% of accuracy, 91.43% of sensitivity and 93.33% of specificity. The results indicate that the fuzzy entropy method is applicable for extracting KOA-related features from sEMG, which can be developed as a sensitive metric for computer-assist diagnosis of knee osteoarthritis.

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KW - Knee osteoarthritis

KW - classification

KW - computer-assist diagnosis

KW - entropy

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ER -

Entropy-Based Surface Electromyogram Feature Extraction for Knee Osteoarthritis Classification

Abstract

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Keywords

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