APPLICATION OF EEMD-DFA ALGORITHMS AND ANN CLASSIFICATION FOR DETECTION OF KNEE OSTEOARTHRITIS USING VIBROARTHROGRAPHY
Anna MACHROWSKA
Lublin University of Technology, Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics (Poland)
https://orcid.org/0000-0003-3289-2421
Robert KARPIŃSKI
r.karpinski@pollub.plDepartment of Machine Design and Mechatronics, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, (Poland)
https://orcid.org/0000-0003-4063-8503
Marcin MACIEJEWSKI
Department of Electronics and Information Technology, Faculty of Electrical Engineering and Computer Science, Lublin University of Technology (Poland)
https://orcid.org/0000-0001-9116-5481
Józef JONAK
Lublin University of Technology, Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics (Poland)
https://orcid.org/0000-0003-4658-4569
Przemysław KRAKOWSKI
Orthopaedic and Sports Traumatology Department, Carolina Medical Center (Poland)
https://orcid.org/0000-0001-7137-7145
Abstract
Osteoarthritis is one of the leading causes of disability around the globe. Up to this date there is no definite cure for cartilage lesions. Only fast and accurate diagnosis enables prolonging joint survivor time. Available diagnostic methods have disadvantages such as high price, radiation, need for experienced radiologists or low availability in some regions. The present study evaluates the use of vibroarthorgraphy as a method of cartilage lesion detection. 47 patients with diagnosed cartilage lesions, and 51 healthy control group patients have been enrolled in this study. The cartilage in the study group was evaluated intraoperatively by experienced orthopaedic surgeon. Signal acquisition was performed in open and closed kinematic chain based on 10 knee joint movements from 0-90 degrees. By using EEMD-DFA algorithms, reducing classifier inputs using ANOVA and then classifying using artificial neural networks (ANN), a classification accuracy of almost 93% was achieved. A sensitivity of 0.93 and a specificity of 0.93 with an AUC of 0.942 were obtained for the multilayer perceptron network. These results allow to apply this testing protocol in a clinical setting in the future.
Keywords:
Knee joint, cartilage, Artificial neural networks, EEMD, DFA, ANOVA, vibroartrographyReferences
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Authors
Anna MACHROWSKALublin University of Technology, Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics Poland
https://orcid.org/0000-0003-3289-2421
Authors
Robert KARPIŃSKIr.karpinski@pollub.pl
Department of Machine Design and Mechatronics, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland
https://orcid.org/0000-0003-4063-8503
Authors
Marcin MACIEJEWSKIDepartment of Electronics and Information Technology, Faculty of Electrical Engineering and Computer Science, Lublin University of Technology Poland
https://orcid.org/0000-0001-9116-5481
Authors
Józef JONAKLublin University of Technology, Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics Poland
https://orcid.org/0000-0003-4658-4569
Authors
Przemysław KRAKOWSKIOrthopaedic and Sports Traumatology Department, Carolina Medical Center Poland
https://orcid.org/0000-0001-7137-7145
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