Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/100126
Title: Computer-based identification of type 2 diabetic subjects with and without neuropathy using dynamic planter pressure and principal component analysis
Authors: Acharya, U. Rajendra
Tong, Jasper W. K.
Subbhuraam, Vinitha Sree
Chua, Chua Kuang
Ha, Tan Peck
Ghista, Dhanjoo N.
Chattopadhyay, Subhagata
Ng, Kwan-Hoong
Suri, Jasjit S.
Issue Date: 2011
Source: Acharya, U. R., Tong, J., Subbhuraam, V. S., Chua, C. K., Ha, T. P., Ghista, D. N., et al. (2011). Computer-Based Identification of Type 2 Diabetic Subjects with and Without Neuropathy Using Dynamic Planter Pressure and Principal Component Analysis. Journal of Medical Systems, 36(4), 2483-2491.
Series/Report no.: Journal of medical systems
Abstract: Diabetes is a chronic disease that is characterized by an increased blood glucose level due to insulin resistance. Type 2 diabetes is common in middle aged and old people. In this work, we present a technique to analyze dynamic foot pressures images and classify them into normal, diabetes type 2 with and without neuropathy classes. Plantar pressure images were obtained using the F-Scan (Tekscan, USA) in-shoe measurement system. We used Principal Component Analysis (PCA) and extracted the eigenvalues from different regions of the foot image. The features extracted from region 1 of the foot pressure image, which were found to be clinically significant, were fed into the Fuzzy classifier (Sugeno model) for automatic classification. Our results show that the proposed method is able to identify the unknown class with an accuracy of 93.7%, sensitivity of 100%, and specificity of 83.3%. Moreover, in this work, we have proposed an integrated index using the eigenvalues to differentiate the normal subjects from diabetes with and without neuropathy subjects using just one number. This index will help the clinicians in easy and objective daily screening, and it can also be used as an adjunct tool to cross check their diagnosis.
URI: https://hdl.handle.net/10356/100126
http://hdl.handle.net/10220/13594
DOI: 10.1007/s10916-011-9715-0
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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