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

  • Authors:

  • Rajendra Acharya U;Peck Ha Tan;Tavintharan Subramaniam;Toshiyo Tamura;Kuang Chua Chua;Seach Chyr Goh;Choo Min Lim;Shu Yi Goh;Kang Rui Chung;Chelsea Law

  • Affiliations:

  • Electronic and Computer Engineering Division, Ngee Ann Polytechnic, Clementi, Singapore 599489;Electronic and Computer Engineering Division, Ngee Ann Polytechnic, Clementi, Singapore 599489;Department of General Medicine, Diabetic Centre, Alexandra Hospital, Alexandra, Singapore 159964;Department of Medical System Engineering, Chiba University, Chiba, Japan 263-8522;Electronic and Computer Engineering Division, Ngee Ann Polytechnic, Clementi, Singapore 599489;Electronic and Computer Engineering Division, Ngee Ann Polytechnic, Clementi, Singapore 599489;Electronic and Computer Engineering Division, Ngee Ann Polytechnic, Clementi, Singapore 599489;Electronic and Computer Engineering Division, Ngee Ann Polytechnic, Clementi, Singapore 599489;Electronic and Computer Engineering Division, Ngee Ann Polytechnic, Clementi, Singapore 599489;Department of Rehabilitation, Diabetic Centre, Alexandra Hospital, Alexandra, Singapore 159964

  • Venue:
  • Journal of Medical Systems
  • Year:
  • 2008

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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.