Imperial College London
Alternate

Patrick A. Naylor

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Speech & Acoustic Signal Processing
 
 
 
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Contact

 

+44 (0)20 7594 6235p.naylor Website

 
 
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Location

 

803Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Yiallourides:2021:10.1109/TBME.2020.3024285,
author = {Yiallourides, C and Naylor, PA},
doi = {10.1109/TBME.2020.3024285},
journal = {IEEE Transactions on Biomedical Engineering},
pages = {1250--1261},
title = {Time-frequency analysis and parameterisation of knee sounds fornon-invasive setection of osteoarthritis},
url = {http://dx.doi.org/10.1109/TBME.2020.3024285},
volume = {68},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Objective: In this work the potential of non-invasive detection of kneeosteoarthritis is investigated using the sounds generated by the knee jointduring walking. Methods: The information contained in the time-frequency domainof these signals and its compressed representations is exploited and theirdiscriminant properties are studied. Their efficacy for the task of normal vsabnormal signal classification is evaluated using a comprehensive experimentalframework. Based on this, the impact of the feature extraction parameters onthe classification performance is investigated using Classification andRegression Trees (CART), Linear Discriminant Analysis (LDA) and Support VectorMachine (SVM) classifiers. Results: It is shown that classification issuccessful with an area under the Receiver Operating Characteristic (ROC) curveof 0.92. Conclusion: The analysis indicates improvements in classificationperformance when using non-uniform frequency scaling and identifies specificfrequency bands that contain discriminative features. Significance: Contrary toother studies that focus on sit-to-stand movements and knee flexion/extension,this study used knee sounds obtained during walking. The analysis of suchsignals leads to non-invasive detection of knee osteoarthritis with highaccuracy and could potentially extend the range of available tools for theassessment of the disease as a more practical and cost effective method withoutrequiring clinical setups.
AU  - Yiallourides,C
AU  - Naylor,PA
DO  - 10.1109/TBME.2020.3024285
EP  - 1261
PY  - 2021///
SN  - 0018-9294
SP  - 1250
TI  - Time-frequency analysis and parameterisation of knee sounds fornon-invasive setection of osteoarthritis
T2  - IEEE Transactions on Biomedical Engineering
UR  - http://dx.doi.org/10.1109/TBME.2020.3024285
UR  - http://arxiv.org/abs/2004.12745v1
UR  - https://ieeexplore.ieee.org/document/9198114
UR  - http://hdl.handle.net/10044/1/89014
VL  - 68
ER  -
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