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Automated Identification of Diabetic Type 2 Subjects with and without Neuropathy Using Wavelet Transform on Pedobarograph

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Abstract

Diabetes is a disorder of metabolism—the way our bodies use digested food for growth and energy. The most common form of diabetes is Type 2 diabetes. Abnormal plantar pressures are considered to play a major role in the pathologies of neuropathic ulcers in the diabetic foot. The purpose of this study was to examine the plantar pressure distribution in normal, diabetic Type 2 with and without neuropathy subjects. Foot scans were obtained using the F-scan (Tekscan USA) pressure measurement system. Various discrete wavelet coefficients were evaluated from the foot images. These extracted parameters were extracted using the discrete wavelet transform (DWT) and presented to the Gaussian mixture model (GMM) and a four-layer feed forward neural network for classification. We demonstrated a sensitivity of 100% and a specificity of more than 85% for the classifiers.

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Authors and Affiliations

  1. Electronic and Computer Engineering Division, Ngee Ann Polytechnic, 535 Clementi Rd, Clementi, Singapore, 599489

    Rajendra Acharya U, Peck Ha Tan, Kuang Chua Chua, Seach Chyr Ernest Goh, Choo Min Lim, Shu Yi Diana Goh & Kang Rui Conrad Chung

  2. Department of General Medicine, Diabetic Centre, Alexandra Hospital, 378 Alexandra Road, Alexandra, Singapore, 159964

    Tavintharan Subramaniam

  3. Department of Medical System Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba-shi, Chiba, 263-8522, Japan

    Toshiyo Tamura

  4. Department of Rehabilitation, Diabetic Centre, Alexandra Hospital, 378 Alexandra Road, Alexandra, Singapore, 159964

    Chelsea Law

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  1. Rajendra Acharya U
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  2. Peck Ha Tan
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Correspondence to Rajendra Acharya U.

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Acharya U, R., Tan, P.H., Subramaniam, T. et al. Automated Identification of Diabetic Type 2 Subjects with and without Neuropathy Using Wavelet Transform on Pedobarograph. J Med Syst 32, 21–29 (2008). https://doi.org/10.1007/s10916-007-9103-y

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  • DOI: https://doi.org/10.1007/s10916-007-9103-y

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