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

• Published in: Journal of medical systems Vol. 32; no. 1; pp. 21 - 29
• Main Authors: Acharya U, Rajendra, Tan, Peck Ha, Subramaniam, Tavintharan, Tamura, Toshiyo, Chua, Kuang Chua, Goh, Seach Chyr Ernest, Lim, Choo Min, Goh, Shu Yi Diana, Chung, Kang Rui Conrad, Law, Chelsea
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.02.2008; Springer Nature B.V
• Subjects: Adolescent; Adult; Aged; Aged, 80 and over; Algorithms; Automation; Diabetes; Diabetes Mellitus, Type 2 - diagnosis; Diabetic Foot; Diabetic Neuropathies; Female; Health Informatics; Health Sciences; Humans; Image Interpretation, Computer-Assisted; Male; Medicine; Medicine & Public Health; Middle Aged; Neural Networks (Computer); Normal Distribution; Original Paper; Pressure; Statistics for Life Sciences; Wavelet transforms; Diabetes Type 2; Plantar pressure; Neuropathy; Artificial neural network; Gaussian mixture model
• ISSN: 0148-5598; 1573-689X
• DOI: 10.1007/s10916-007-9103-y

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.