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  • Conference paper
    Naylor PA, Alcazar J, Boudy J, Grenier Yet al., 1994,

    Enhancement of Handsfree Telecommunications

    , Dallas, Tx, Pages: 142-147-142-147
  • Conference paper
    Hart JE, Naylor PA, Tanrikulu O, 1993,

    Polyphase allpass IIR structures for sub-band acoustic echo cancellation

    , Proc. European Speech Proc. Conf, Pages: 1813-1816
  • Conference paper
    Naylor PA, 1993,

    Enhancement of Hands-free Telecommunications - Activities of the FREETEL Consortium

    , Plestin les Gr�ves, France
  • Conference paper
    Strong AJ, Kirkpatrick PJ, Bucknall RM, Naylor P, Rudman Set al., 1992,

    Measurement of superficial cerebral cortical blood flow by imaging umbelliferone clearance in anaesthetized cats and rabbits

    , ISSN: 0022-3751
  • Journal article
    Strong J, Kirkpatrick PJ, Bucknall RM, Naylor PA, Rudman Set al., 1991,

    Measurement of superficial cerebral cortical blood flow by imaging umbelliferone clearance

    , Journal of the Physiological Society, Vol: 446
  • Conference paper
    Brookes DM, Patrick AN, 1988,

    Speech production modelling with variable glottal reflection coefficient

    , Proc. Int. Conf. on Acoustics, Speech and Sig. Proc, Pages: 671-674
  • Journal article
    Yiallourides C, Naylor PA,

    Time-Frequency Analysis and Parameterisation of Knee Sounds for Non-invasive Detection of Osteoarthritis

    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.

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