Application of EEMD-DFA algorithms and ann classification for detection of knee osteoarthritis using vibroarthrography

• Autorzy: Machrowska Anna , Karpiński Robert , Maciejewski Marcin , Jonak Józef , Krakowski Przemysław

Identyfikatory

DOI

10.35784/acs-2024-18

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

knee joint; cartilage; artificial neural networks; EEMD; DFA; ANOVA; vibroartrography

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2024
• Tom: Vol. 20, no. 2
• Strony: 90--108
• Opis fizyczny: Bibliogr. 57 poz., fig., tab.

Twórcy

autor

Machrowska Anna

• Lublin University of Technology, Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Poland

autor

Karpiński Robert

• Lublin University of Technology, Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Poland

• https://orcid.org/0000-0003-4063-8503

autor

Maciejewski Marcin

• Lublin University of Technology, Faculty of Electrical Engineering and Computer Science, Department of Electronics and Information Technology, Poland

• https://orcid.org/0000-0001-9116-5481

autor

Jonak Józef

• Lublin University of Technology, Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Poland

• https://orcid.org/0000-0003-4658-4569

autor

Krakowski Przemysław

• Medical University of Lublin, Chair and Department of Traumatology and Emergency Medicine, Poland

• https://orcid.org/0000-0001-7137-7145

Bibliografia

• [1] Aranchayanont, T., Songsiri, J., & Srungboonmee, K. (2016). Spectral analysis on vibroarthrographic signal of total knee arthroplasty. 2016 IEEE Region 10 Conference (TENCON) (pp. 1747–1751). IEEE. https://doi.org/10.1109/TENCON.2016.7848318
• [2] Bączkowicz, D., & Majorczyk, E. (2014). Joint motion quality in vibroacoustic signal analysis for patients with patellofemoral joint disorders. BMC Musculoskeletal Disorders, 15(1), 426. https://doi.org/10.1186/1471-2474-15-426
• [3] Balajee, A., Murugan, R., & Venkatesh, K. (2023). Security-enhanced machine learning model for diagnosis of knee joint disorders using vibroarthrographic signals. Soft Computing, 27, 7543–7553. https://doi.org/10.1007/s00500-023-07934-2
• [4] Balajee, A., & Venkatesan, R. (2021). Machine learning based identification and classification of disorders in human knee joint – computational approach. Soft Computing, 25, 13001–13013. https://doi.org/10.1007/s00500-021-06134-0
• [5] Cai, S., Yang, S., Zheng, F., Lu, M., Wu, Y., & Krishnan, S. (2013). Knee joint vibration signal analysis with matching pursuit decomposition and dynamic weighted classifier fusion. Computational and Mathematical Methods in Medicine, 2013(1), 04267. https://doi.org/10.1155/2013/904267
• [6] Chen, Z., Ivanov, P. Ch., Hu, K., & Stanley, H. E. (2002). Effect of nonstationarities on detrended fluctuation analysis. Physical Review E, 65, 041107. https://doi.org/10.1103/PhysRevE.65.041107
• [7] Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 21, 6. https://doi.org/10.1186/s12864-019-6413-7
• [8] Dąbrowski, Z., & Dziurdź, J. (2016). Simultaneous Analysis of noise and vibration of machines in vibroacoustic diagnostics. Archives of Acoustics, 41(4), 783–789. https://doi.org/10.1515/aoa-2016-0075
• [9] Delvecchio, S., Bonfiglio, P., & Pompoli, F. (2018). Vibro-acoustic condition monitoring of Internal Combustion Engines: A critical review of existing techniques. Mechanical Systems and Signal Processing, 99, 661–683. https://doi.org/10.1016/j.ymssp.2017.06.033
• [10] Falkowicz, K., & Kulisz, M. (2024). Prediction of buckling behaviour of composite plate element using Artificial Neural Networks. Advances in Science and Technology Research Journal, 18(1), 231–243. https://doi.org/10.12913/22998624/177399
• [11] Glyn-Jones, S., Palmer, A. J. R., Agricola, R., Price, A. J., Vincent, T. L., Weinans, H., & Carr, A. J. (2015). Osteoarthritis. The Lancet, 386(9991), 376–387. https://doi.org/10.1016/S0140-6736(14)60802-3
• [12] Gong, R., Ohtsu, H., Hase, K., & Ota, S. (2021). Vibroarthrographic signals for the low-cost and computationally efficient classification of aging and healthy knees. Biomedical Signal Processing and Control, 70, 103003. https://doi.org/10.1016/j.bspc.2021.103003
• [13] Goossens, Q., Locsin, M., Gharehbaghi, S., Brito, P., Moise, E., Ponder, L. A., Inan, O. T., & Prahalad, S. (2023). Knee acoustic emissions as a noninvasive biomarker of articular health in patients with juvenile idiopathic arthritis: A clinical validation in an extended study population. Pediatric Rheumatology, 21, 59. https://doi.org/10.1186/s12969-023-00842-7
• [14] Hu, K., Ivanov, P. C., Chen, Z., Carpena, P., & Stanley, H. E. (2001). Effect of trends on detrended fluctuation analysis. Physical Review E, 64, 011114. https://doi.org/10.1103/PhysRevE.64.011114
• [15] Hunter, D. J., & Bierma-Zeinstra, S. (2019). Osteoarthritis. The Lancet, 393(10182), 1745–1759. https://doi.org/10.1016/S0140-6736(19)30417-9
• [16] Jedliński, Ł., Caban, J., Krzywonos, L., Wierzbicki, S., & Brumerčík, F. (2015). Application of vibration signal in the diagnosis of IC engine valve clearance. Journal of Vibroengineering, 17(1), 175–187.
• [17] Jedliński, Ł., Syta, A., Gajewski, J., & Jonak, J. (2022). Nonlinear analysis of cylindrical gear dynamics under varying tooth breakage. Measurement, 190, 110721. https://doi.org/10.1016/j.measurement.2022.110721
• [18] Karpiński, R. (2022). Knee joint osteoarthritis diagnosis based on selected acoustic signal discriminants using machine learning. Applied Computer Science, 18(2), 71–85. https://doi.org/10.35784/acs-2022-14
• [19] Karpiński, R., Krakowski, P., Jonak, J., Machrowska, A., & Maciejewski, M. (2023). Comparison of selected classification methods based on machine learning as a diagnostic tool for knee joint cartilage damage based on generated vibroacoustic processes. Applied Computer Science, 19(4), 136–150. https://doi.org/10.35784/acs-2023-40
• [20] Karpiński, R., Krakowski, P., Jonak, J., Machrowska, A., Maciejewski, M., & Nogalski, A. (2021a). Analysis of differences in vibroacoustic signals between healthy and osteoarthritic knees using EMD algorithm and statistical analysis. Journal of Physics: Conference Series, 2130, 012010. https://doi.org/10.1088/1742-6596/2130/1/012010
• [21] Karpiński, R., Krakowski, P., Jonak, J., Machrowska, A., Maciejewski, M., & Nogalski, A. (2021b). Estimation of differences in selected indices of vibroacoustic signals between healthy and osteoarthritic patellofemoral joints as a potential non-invasive diagnostic tool. Journal of Physics: Conference Series, 2130, 012009. https://doi.org/10.1088/1742-6596/2130/1/012009
• [22] Karpiński, R., Krakowski, P., Jonak, J., Machrowska, A., Maciejewski, M., & Nogalski, A. (2022a). Diagnostics of articular cartilage damage based on generated acoustic signals using ANN—Part I: Femoral-tibial joint. Sensors, 22(6), 2176. https://doi.org/10.3390/s22062176
• [23] Karpiński, R., Krakowski, P., Jonak, J., Machrowska, A., Maciejewski, M., & Nogalski, A. (2022b). Diagnostics of articular cartilage damage based on generated acoustic signals using ANN—Part II: Patellofemoral joint. Sensors, 22(10), 3765. https://doi.org/10.3390/s22103765
• [24] Karpiński, R., Machrowska, A., & Maciejewski, M. (2019). Application of acoustic signal processing methods in detecting differences between open and closed kinematic chain movement for the knee joint. Applied Computer Science, 15(1), 36–48. https://doi.org/10.23743/acs-2019-03
• [25] Kim, K. S., Seo, J. H., Kang, J. U., & Song, C. G. (2009). An enhanced algorithm for knee joint sound classification using feature extraction based on time-frequency analysis. Computer Methods and Programs in Biomedicine, 94(2), 198–206. https://doi.org/10.1016/j.cmpb.2008.12.012
• [26] Krakowski, P., Karpiński, R., Jojczuk, M., Nogalska, A., & Jonak, J. (2021). Knee MRI underestimates the grade of cartilage lesions. Applied Sciences, 11(4), 1552. https://doi.org/10.3390/app11041552
• [27] Krakowski, P., Karpiński, R., Jonak, J., & Maciejewski, R. (2021a). Evaluation of diagnostic accuracy of physical examination and MRI for ligament and meniscus injuries. Journal of Physics: Conference Series, 1736, 012027. https://doi.org/10.1088/1742-6596/1736/1/012027
• [28] Krakowski, P., Karpiński, R., Maciejewski, R., & Jonak, J. (2021b). Evaluation of the diagnostic accuracy of MRI in detection of knee cartilage lesions using Receiver Operating Characteristic curves. Journal of Physics: Conference Series, 1736, 012028. https://doi.org/10.1088/1742-6596/1736/1/012028
• [29] Krakowski, P., Karpiński, R., Maciejewski, R., Jonak, J., & Jurkiewicz, A. (2020). Short-term effects of arthroscopic microfracturation of knee chondral defects in Osteoarthritis. Applied Sciences, 10(23), 8312. https://doi.org/10.3390/app10238312
• [30] Kręcisz, K., & Bączkowicz, D. (2018). Analysis and multiclass classification of pathological knee joints using vibroarthrographic signals. Computer Methods and Programs in Biomedicine, 154, 37–44. https://doi.org/10.1016/j.cmpb.2017.10.027
• [31] Kręcisz, K., Bączkowicz, D., & Kawala-Sterniuk, A. (2022). Using nonlinear vibroartrographic parameters for Age-Related changes assessment in knee arthrokinematics. Sensors, 22(15), 5549. https://doi.org/10.3390/s22155549
• [32] Krishnan, S., Rangayyan, R. M., Bell, G. D., & Frank, C. B. (2000). Adaptive time-frequency analysis of knee joint vibroarthrographic signals for noninvasive screening of articular cartilage pathology. IEEE Transactions on Biomedical Engineering, 47(6), 773-783. https://doi.org/10.1109/10.844228
• [33] Krishnan, S., Rangayyan, R. M., Bell, G. D., Frank, C. B., & Ladly, K. O. (1997). Adaptive filtering, modelling and classification of knee joint vibroarthrographic signals for non-invasive diagnosis of articular cartilage pathology. Medical & Biological Engineering & Computing, 35, 677–684. https://doi.org/10.1007/BF02510977
• [34] Lin, W.-C., Lee, T.-F., Lin, S.-Y., Wu, L.-F., Wang, H.-Y., Chang, L., Wu, J.-M., Jiang, J.-C., Tuan, C.-C., Horng, M.-F., Shieh, C.-S., & Chao, P.-J. (2014). Non-invasive knee osteoarthritis diagnosis via vibroarthrographic signal analysis. Journal of Information Hiding and Multimedia Signal Processing 5(3), 497–507.
• [35] Liu, Y., Dai, Y., Zhou, Y., Lang, X., Liu, Y., Zheng, Q., Zhang, Y., Jiang, X., Zhang, L., & Tang, J. (2019). An efficient and robust muscle artifact removal method for few-channel EEG. IEEE Access, 7, 176036–176050. https://doi.org/10.1109/ACCESS.2019.2957401
• [36] Loeser, R. F., Goldring, S. R., Scanzello, C. R., & Goldring, M. B. (2012). Osteoarthritis: A disease of the joint as an organ. Arthritis & Rheumatology, 64(6), 1697–1707. https://doi.org/10.1002/art.34453
• [37] Łysiak, A., Froń, A., Bączkowicz, D., & Szmajda, M. (2020). Vibroarthrographic signal spectral features in 5-class knee joint classification. Sensors, 20(17), 5015. https://doi.org/10.3390/s20175015
• [38] Machrowska, A., Karpiński, R., Jonak, J., Szabelski, J., & Krakowski, P. (2020). Numerical prediction of the component-ratio-dependent compressive strength of bone cement. Applied Computer Science, 16(3), 88–101. https://doi.org/10.23743/acs-2020-24
• [39] Machrowska, A., Szabelski, J., Karpiński, R., Krakowski, P., Jonak, J., & Jonak, K. (2020a). Use of deep learning networks and statistical modeling to predict changes in mechanical parameters of contaminated bone cements. Materials, 13(23), 5419. https://doi.org/10.3390/ma13235419
• [40] Madeleine, P., Andersen, R. E., Larsen, J. B., Arendt-Nielsen, L., & Samani, A. (2020). Wireless multichannel vibroarthrographic recordings for the assessment of knee osteoarthritis during three activities of daily living. Clinical Biomechanics, 72, 16–23. https://doi.org/10.1016/j.clinbiomech.2019.11.015
• [41] Maussavi, Z. M. K., Rangayyan, R. M., Bell, G. D., Frank, C. B., & Ladly, K. O. (1996). Screening of vibroarthrographic signals via adaptive segmentation and linear prediction modeling. IEEE Transactions on Biomedical Engineering, 43(1),. https://doi.org/10.1109/10.477697
• [42] Mu, T., Nandi, A. K., & Rangayyan, R. M. (2008). Screening of knee-joint vibroarthrographic signals using the strict 2-surface proximal classifier and genetic algorithm. Computers in Biology and Medicine, 38(10), 1103–1111. https://doi.org/10.1016/j.compbiomed.2008.08.009
• [43] Nalband, S., Prince, A., & Agrawal, A. (2018). Entropy‐based feature extraction and classification of vibroarthographic signal using complete ensemble empirical mode decomposition with adaptive noise. IET Science, Measurement & Technology, 12(3), 350–359. https://doi.org/10.1049/iet-smt.2017.0284
• [44] Nalband, S., Sreekrishna, R. R., & Prince, A. A. (2016). Analysis of knee joint vibration signals using ensemble empirical mode decomposition. Procedia Computer Science, 89, 820–827. https://doi.org/10.1016/j.procs.2016.06.067
• [45] Nalband, S., Valliappan, C. A., Prince, R. G. A. A., & Agrawal, A. (2017). Feature extraction and classification of knee joint disorders using Hilbert Huang transform. 2017 14th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) (pp. 266–269). IEEE. https://doi.org/10.1109/ECTICon.2017.8096224
• [46] Ota, L., Uchitomi, H., Suzuki, K., Hove, M. J., Orimo, S., & Miyake, Y. (2011). Relationship between fractal property of gait cycle and severity of Parkinson’s disease. 2011 IEEE/SICE International Symposium on System Integration (SII) (pp. 236–239). IEEE. https://doi.org/10.1109/SII.2011.6147452
• [47] Patankar, S., Durge, G., Joshi, A., Jaid, A., Kalambe, K., & Dhale, H. (2023). VAG signal classification using time domain statistical features and machine learning. 2023 3rd International Conference on Mobile Networks and Wireless Communications (ICMNWC), 1–6. IEEE. https://doi.org/10.1109/ICMNWC60182.2023.10435757
• [48] Rangayyan, R. M., Oloumi, F., Wu, Y., & Cai, S. (2013). Fractal analysis of knee-joint vibroarthrographic signals via power spectral analysis. Biomedical Signal Processing and Control, 8(1), 23–29. https://doi.org/10.1016/j.bspc.2012.05.004
• [49] Rangayyan, R. M., & Wu, Y. (2009). Analysis of vibroarthrographic signals with features related to signal variability and radial-basis functions. Annals of Biomedical Engineering, 37, 156–163. https://doi.org/10.1007/s10439-008-9601-1
• [50] Rehman, N., & Mandic, D. P. (2010). Multivariate empirical mode decomposition. Royal Society, 466(2117), 1291–1302. https://doi.org/10.1098/rspa.2009.0502
• [51] Semiz, B., Hersek, S., Whittingslow, D. C., Ponder, L. A., Prahalad, S., & Inan, O. T. (2018). Using knee acoustical emissions for sensing joint health in patients with juvenile idiopathic arthritis: A pilot study. IEEE Sensors Journal, 18(22), 9128–9136. https://doi.org/10.1109/JSEN.2018.2869990
• [52] Shidore, M. M., Athreya, S. S., Deshpande, S., & Jalnekar, R. (2021). Screening of knee-joint vibroarthrographic signals using time and spectral domain features. Biomedical Signal Processing and Control, 68, 102808. https://doi.org/10.1016/j.bspc.2021.102808
• [53] Szabelski, J., Karpiński, R., & Machrowska, A. (2022). Application of an Artificial Neural Network in the modelling of heat curing effects on the strength of adhesive joints at elevated temperature with imprecise adhesive mix ratios. Materials, 15(3), 721. https://doi.org/10.3390/ma15030721
• [54] Wang, Y., Zheng, T., Song, J., & Gao, W. (2021). A novel automatic Knee Osteoarthritis detection method based on vibroarthrographic signals. Biomedical Signal Processing and Control, 68, 102796. https://doi.org/10.1016/j.bspc.2021.102796
• [55] Wu, Y. (2015). Knee Joint Vibroarthrographic Signal Processing and Analysis. Springer Berlin Heidelberg.
• [56] Wu, Y., Yang, S., Zheng, F., Cai, S., Lu, M., & Wu, M. (2014). Removal of artifacts in knee joint vibroarthrographic signals using ensemble empirical mode decomposition and detrended fluctuation analysis. Physiological Measurement, 35, 429. https://doi.org/10.1088/0967-3334/35/3/429
• [57] Yang, S., Cai, S., Zheng, F., Wu, Y., Liu, K., Wu, M., Zou, Q., & Chen, J. (2014). Representation of fluctuation features in pathological knee joint vibroarthrographic signals using kernel density modeling method. Medical Engineering & Physics, 36(10), 1305–1311. https://doi.org/10.1016/j.medengphy.2014.07.008