Time-frequency analysis and parameterisation of knee sounds for
non-invasive setection of osteoarthritis
non-invasive setection of osteoarthritis
File(s)
Accepted version
Author(s)
Yiallourides, Costas
Naylor, Patrick A
Type
Journal Article
Abstract
Objective: In this work the potential of non-invasive detection of knee
osteoarthritis is investigated using the sounds generated by the knee joint
during walking. Methods: The information contained in the time-frequency domain
of these signals and its compressed representations is exploited and their
discriminant properties are studied. Their efficacy for the task of normal vs
abnormal signal classification is evaluated using a comprehensive experimental
framework. Based on this, the impact of the feature extraction parameters on
the classification performance is investigated using Classification and
Regression Trees (CART), Linear Discriminant Analysis (LDA) and Support Vector
Machine (SVM) classifiers. Results: It is shown that classification is
successful with an area under the Receiver Operating Characteristic (ROC) curve
of 0.92. Conclusion: The analysis indicates improvements in classification
performance when using non-uniform frequency scaling and identifies specific
frequency bands that contain discriminative features. Significance: Contrary to
other studies that focus on sit-to-stand movements and knee flexion/extension,
this study used knee sounds obtained during walking. The analysis of such
signals leads to non-invasive detection of knee osteoarthritis with high
accuracy and could potentially extend the range of available tools for the
assessment of the disease as a more practical and cost effective method without
requiring clinical setups.
osteoarthritis is investigated using the sounds generated by the knee joint
during walking. Methods: The information contained in the time-frequency domain
of these signals and its compressed representations is exploited and their
discriminant properties are studied. Their efficacy for the task of normal vs
abnormal signal classification is evaluated using a comprehensive experimental
framework. Based on this, the impact of the feature extraction parameters on
the classification performance is investigated using Classification and
Regression Trees (CART), Linear Discriminant Analysis (LDA) and Support Vector
Machine (SVM) classifiers. Results: It is shown that classification is
successful with an area under the Receiver Operating Characteristic (ROC) curve
of 0.92. Conclusion: The analysis indicates improvements in classification
performance when using non-uniform frequency scaling and identifies specific
frequency bands that contain discriminative features. Significance: Contrary to
other studies that focus on sit-to-stand movements and knee flexion/extension,
this study used knee sounds obtained during walking. The analysis of such
signals leads to non-invasive detection of knee osteoarthritis with high
accuracy and could potentially extend the range of available tools for the
assessment of the disease as a more practical and cost effective method without
requiring clinical setups.
Date Issued
2021-04-01
Date Acceptance
2020-09-03
Citation
IEEE Transactions on Biomedical Engineering, 2021, 68 (4), pp.1250-1261
ISSN
0018-9294
Publisher
IEEE
Start Page
1250
End Page
1261
Journal / Book Title
IEEE Transactions on Biomedical Engineering
Volume
68
Issue
4
Copyright Statement
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
Sponsor
Wellcome Trust
Identifier
http://arxiv.org/abs/2004.12745v1
Grant Number
097816/Z/11/B
Subjects
eess.AS
eess.AS
cs.SD
Notes
Submitted to IEEE Transactions on Biomedical Engineering
Publication Status
Published
Date Publish Online
2020-09-15