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Application of DCT-derived parameters for early detection of polyneuropathy in diabetic patients

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Diabetic foot is one of the most severe complications of diabetes. Early diagnosis of this syndrome can ensure proper medical care and adequate treatment. Various image analysis methods can be used to speed up the diagnosis process, and automated diagnosis can be applied as a screening technique to reduce its cost. Introducing auxiliary diagnostic parameters may help to detect polyneuropathy or neuropathy, both of which often precede the appearance of diabetic foot syndrome. The present paper describes a study performed on a group of diabetic patients by analyzing plantar pressure distribution images. As part of this study, 2D discrete cosine transform (DCT) is computed for the forefoot and rearfoot regions of each diabetic subject in a group of 37 patients. Three new DCT-based parameters are introduced to help to detect polyneuropathy or at least indicate that the patient may have polyneuropathy without a time-consuming examination. The results indicate a certain relationship between these parameters and the presence of polyneuropathy. This information could be used in further diagnosis to prevent foot ulcers from developing in patients with diabetes.
Rocznik
Strony
179--186
Opis fizyczny
Bibliogr. 26 poz., rys., wykr., zdj.
Twórcy
autor
  • Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland
autor
  • Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland
Bibliografia
  • 1. Singh N, Armstrong DG, Lipsky BA. Preventing foot ulcers inpatients with diabetes. J Am Med Assoc 2005;293:217–28.
  • 2. Clarke A. Avoiding foot complications in diabetes: footcomplicationsin diabetes are serious and costly. CME2010;28:181–5.
  • 3. Weigelt JA, editor. MRSA. 2nd ed. New York: Informa Healthcare,2010.
  • 4. Motley TA, Gilligan AM, Lange DL, Waycaster CR, Dickerson Jr JE.Cost-effectiveness of clostridial collagenase ointment on woundclosure in patients with diabetic foot ulcers: economic analysisof results from a multicenter, randomized, open-label trial. J FootAnkle Res 2015;8:7.
  • 5. Delbridge L, Ctercteko G, Fowler C, Reeve TS, Le Quesne LP. Theaetiology of diabetic neuropathic ulceration of the foot. Br J Surg 1985;72:1–6.
  • 6. Caselli A, Pham H, Giurini JM, Armstrong DG, Veves A. The forefoot-to-rearfoot plantar pressure ratio is increased in severe diabetic neuropathy and can predict foot ulceration. Diabetes Care 2002;25:1066–71.
  • 7. Boulton AJ. Pressure and the diabetic foot: clinical science and offloading techniques. Am J Surg 2004;187:17S–24S.
  • 8. Boulton AJ, Vinik AI, Arezzo JC, Bril V, Feldman EL, Freeman R, et al. Diabetic neuropathies: a statement by the American Diabetes Association. Diabetes Care 2005;28:956–62.
  • 9. Alexiadou K, Doupis J. Management of diabetic foot ulcers. Diabetes Ther 2012;3:4.
  • 10. Prabhu KG, Patil KM, Srinivasan S. Diabetic feet at risk: a new method of analysis of walking foot pressure images at different levels of neuropathy for early detection of plantar ulcers. Med BiolEngComput 2001;39:288–93.
  • 11. Charanya G, Patil KM, Thomas JV, Narayanamurthy VB, Parivalavan R, Visvanath K. Standing foot pressure image analysis for variations in foot sole soft tissue properties and levels of diabetic neuropathy. ITBM-RBM 2004;25:23–33.
  • 12. Acharya UR, Tan PH, Subramaniam T, Tamura T, Chua KC, Goh SC, et al. Automated identification of diabetic type 2 subjects with and without neuropathy using wavelet transform on pedobarograph. J Med Syst 2007;32:21–9.
  • 13. Periyasamy R, Mishra A, Anand S, Ammini AC. Preliminary investigation of foot pressure distribution variation in men and women adults while standing. Foot 2011;21:142–8.
  • 14. Siddiqui HR, Spruce M, Alty SR, Dudley S. Automated peripheral neuropathy assessment using optical imaging and foot anthropometry. IEEE Trans Biomed Eng 2015;62:1911–7.
  • 15. Keijsers NL, Stolwijk NM, Pataky TC. Linear dependence of peak, mean, and pressure-time integral values in plantar pressure images. GaitPosture 2010;31:140–2.
  • 16. Razjouyan J, Khayat O, Siahi M, Mansouri AA. A hybrid soft-computing method for image analysis of digital plantar scanners. J Med Signals Sensors 2013;3:15–21.
  • 17. Grabara M. Influence of football training on alignment of the lower limbs and shaping of the feet. Hum Move 2008;9:46–50.
  • 18. Shah SR, Patil KM. Processing of foot pressure images and display of an advanced clinical parameter PR in diabetic neuropathy. In: Proceedings of the 2nd International IEEE EMBS Conference on Neural Engineering, Arlington, VA, 2005.
  • 19. Puri M, Patil KM, Balasubramanian V, Narayanamurthy VB. Texture analysis of foot sole soft tissue images in diabetic neuropathy using wavelet transform. Med BiolEngComput 2005;43:756–63.
  • 20. Prabhu KG, Patil KM, Srinivasan S. A new method of analysis of standing foot pressure images for detection of the plantar ulcers in early-stage diabetic neuropathy. Front Med BiolEng 2000;11:31–43.
  • 21. Golec J, Gołaszewska K, Kamińska M, Szczygieł E, Golec P, Tomaszewski P. Evaluation of disorder of balance and posture in the osteoarthrosis and osteoporosis. OstryDyżur 2015;8:170–4.
  • 22. Chen S, An T, Hao L. Discrete cosine transform image compression based on genetic algorithm. In: International Conference on Information Engineering and Computer Science, Wuhan, 2009:1–3.
  • 23. Downey AB. Think DSP: digital signal processing in Python. Needham, MA: Green Tea Press, 2014.
  • 24. Makhoul J. A fast cosine transform in one and two dimensions. IEEE Trans Acoust Speech Signal Proc 1980;28:27–34.
  • 25. Mueller MJ, Zou D, Lott DJ. “Pressure gradient” as an indicator of plantar skin injury. Diabetes Care 2005;28:2908–12.
  • 26. Mueller MJ, Zou D, Lott DJ. Pressure gradient and subsurface shear stress on the neuropathic forefoot. ClinBiomech 2008;23:342–8.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b383c549-d4da-4c28-a075-b15481c075a5
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