{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T16:12:09Z","timestamp":1772727129576,"version":"3.50.1"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T00:00:00Z","timestamp":1744329600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T00:00:00Z","timestamp":1744329600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Circuits Syst Signal Process"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Signal processing techniques play a critical role in addressing real-world applications across domains such as sensor analysis, defence, and clinical and biomedical fields. Within healthcare, computer-aided diagnostic (CAD) systems have become pivotal in supporting medical professionals with the interpretation of data and images, especially in medical imaging and radiological diagnostics. For diagnosing joint disorders, both time-domain and frequency-domain analyses are employed to examine complex, non-stationary, and nonlinear signals. To process Vibroarthrographic signals in this context, an initial step involves applying the Hilbert-Huang Transform, which comprises two stages: Empirical Mode Decomposition (EMD) for computing intrinsic mode functions (IMFs), followed by the Hilbert transform for further signal analysis. In our proposed approach, we utilized Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Time-Varying Frequency Empirical Mode Decomposition (TVF-EMD) to compute IMFs, as well as Variation Mode Decomposition to calculate mode signals. Subsequent feature extraction incorporates both time and frequency characteristics, focusing on metrics such as pixel intensity, mean, and standard deviation. These features then serve as inputs to machine learning models for classification tasks, distinguishing between healthy and non-healthy signal samples. In our model, we employed a Least Squares Support Vector Machine (LS-SVM) and a Support Vector Machine with Recursive Feature Elimination (SVM-RFE) to enhance classification accuracy. This sequence of signal processing and machine learning steps demonstrates a structured and effective approach for CAD-based diagnosis in joint disorder assessments.<\/jats:p>","DOI":"10.1007\/s00034-025-03096-8","type":"journal-article","created":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T16:27:29Z","timestamp":1744388849000},"page":"512-534","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Novel Ensemble Empirical Decomposition and Time\u2013Frequency Analysis Approach for Vibroarthrographic Signal Processing"],"prefix":"10.1007","volume":"45","author":[{"given":"Surbhi Bhatia","family":"Khan","sequence":"first","affiliation":[]},{"given":"A.","family":"Balajee","sequence":"additional","affiliation":[]},{"given":"S. Sheik Mohideen","family":"Shah","sequence":"additional","affiliation":[]},{"given":"T. R.","family":"Mahesh","sequence":"additional","affiliation":[]},{"given":"Mohammad","family":"Alojail","sequence":"additional","affiliation":[]},{"given":"Indrajeet","family":"Gupta","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,11]]},"reference":[{"key":"3096_CR1","doi-asserted-by":"publisher","first-page":"45029","DOI":"10.1109\/ACCESS.2022.3170108","volume":"10","author":"\u00d8 Bjelland","year":"2022","unstructured":"\u00d8. Bjelland, B. Rasheed, H.G. Schaathun, M.D. Pedersen, M. Steinert, A.I. Hellevik, R.T. Bye, Toward a digital twin for arthroscopic knee surgery: a systematic review. IEEE Access 10, 45029\u201345052 (2022)","journal-title":"IEEE Access"},{"issue":"2","key":"3096_CR2","doi-asserted-by":"publisher","first-page":"1196","DOI":"10.1109\/TMECH.2021.3084311","volume":"27","author":"MH Chang","year":"2021","unstructured":"M.H. Chang, D.H. Kim, S.H. Kim, Y. Lee, S. Cho, H.S. Park, K.J. Cho, Anthropomorphic prosthetic hand inspired by efficient swing mechanics for sports activities. IEEE\/ASME Trans. Mechatron. 27(2), 1196\u20131207 (2021)","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"3096_CR3","first-page":"1","volume":"20","author":"R Chen","year":"2023","unstructured":"R. Chen, Y. Jiang, H. Ni, Optimal imaging time interval selection method for space target via time\u2013frequency analysis with spaceborne ISAR. IEEE Geosci. Remote Sens. Lett. Sens. Lett. 20, 1\u20135 (2023)","journal-title":"IEEE Geosci. Remote Sens. Lett. Sens. Lett."},{"issue":"5","key":"3096_CR4","doi-asserted-by":"publisher","first-page":"537","DOI":"10.3390\/jpm14050537","volume":"14","author":"A-C Cocea","year":"2024","unstructured":"A.-C. Cocea, C.I. Stoica, Interactions and trends of interleukins, PAI-1, CRP, and TNF-\u03b1 in inflammatory responses during the perioperative period of joint arthroplasty: implications for pain management\u2014a narrative review. J. Pers. Med. 14(5), 537 (2024)","journal-title":"J. Pers. Med."},{"key":"3096_CR5","doi-asserted-by":"publisher","first-page":"4376","DOI":"10.1109\/TNSRE.2023.3328936","volume":"31","author":"P-A Deleu","year":"2023","unstructured":"P.-A. Deleu, A. Naaim, B.D. Bevernage, L. Ch\u00e8ze, R. Dumas, I. Birch, J.-L. Besse, T. Leemrijse, Changes in relative work of the lower extremity and distal foot joints after total ankle replacement: an exploratory study. IEEE Trans. Neural Syst. Rehabil. Eng. 31, 4376\u20134381 (2023). https:\/\/doi.org\/10.1109\/TNSRE.2023.3328936","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"3096_CR6","doi-asserted-by":"publisher","first-page":"135680","DOI":"10.1016\/j.jclepro.2022.135680","volume":"385","author":"M Guermoui","year":"2023","unstructured":"M. Guermoui, K. Gairaa, K. Ferkous, S. de Domingos, O. Santos Jr., T. Arrif, A. Belaid, Potential assessment of the TVF-EMD algorithm in forecasting hourly global solar radiation: review and case studies. J. Clean. Prod. 385, 135680 (2023)","journal-title":"J. Clean. Prod."},{"key":"3096_CR7","doi-asserted-by":"publisher","first-page":"100684","DOI":"10.1109\/ACCESS.2019.2930543","volume":"7","author":"H-P Huang","year":"2019","unstructured":"H.-P. Huang, S.-Y. Wei, H.-H. Chao, C.F. Hsu, L. Hsu, S. Chi, An investigation study on mode mixing separation in empirical mode decomposition. IEEE Access 7, 100684\u2013100691 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2930543","journal-title":"IEEE Access"},{"key":"3096_CR8","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.mri.2019.09.007","volume":"65","author":"S Jerban","year":"2020","unstructured":"S. Jerban, E.Y. Chang, Du. Jiang, Magnetic resonance imaging (MRI) studies of knee joint under mechanical loading. Magn. Reson. Imaging 65, 27\u201336 (2020)","journal-title":"Magn. Reson. Imaging"},{"key":"3096_CR9","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/JTEHM.2023.3246919","volume":"11","author":"Y Khalifa","year":"2023","unstructured":"Y. Khalifa, A.S. Mahoney, E. Lucatorto, J.L. Coyle, E. Sejdi\u0107, Non-Invasive sensor-based estimation of anterior-posterior upper esophageal sphincter opening maximal distension. IEEE J. Transl. Eng. Health Med. 11, 182\u2013190 (2023)","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"3096_CR10","doi-asserted-by":"publisher","DOI":"10.1186\/s12944-024-02073-5","author":"H Li","year":"2024","unstructured":"H. Li, Y. Cui, J. Wang, W. Zhang, Y. Chen, J. Zhao, Identification and validation of biomarkers related to lipid metabolism in osteoarthritis based on machine learning algorithms. Lipids Health Dis. (2024). https:\/\/doi.org\/10.1186\/s12944-024-02073-5","journal-title":"Lipids Health Dis."},{"key":"3096_CR11","doi-asserted-by":"publisher","first-page":"109727","DOI":"10.1016\/j.ymssp.2022.109727","volume":"184","author":"F Liu","year":"2023","unstructured":"F. Liu, X. Zhao, Z. Zhu, Z. Zhai, Y. Liu, Dual-microphone active noise cancellation paved with doppler assimilation for TADS. Mech. Syst. Signal Process. 184, 109727 (2023). https:\/\/doi.org\/10.1016\/j.ymssp.2022.109727","journal-title":"Mech. Syst. Signal Process."},{"key":"3096_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2016\/2841249","volume":"2016","author":"Z Li","year":"2016","unstructured":"Z. Li, B. Shi, Research of fault diagnosis based on sensitive intrinsic mode function selection of EEMD and adaptive stochastic resonance. Shock. Vib. 2016, 1\u201312 (2016). https:\/\/doi.org\/10.1155\/2016\/2841249","journal-title":"Shock. Vib."},{"key":"3096_CR13","doi-asserted-by":"publisher","first-page":"110208","DOI":"10.1016\/j.ymssp.2023.110208","volume":"191","author":"L Miaofen","year":"2023","unstructured":"L. Miaofen, L. Youmin, W. Tianyang, C. Fulei, P. Zhike, Adaptive synchronous demodulation transform with application to analyzing multicomponent signals for machinery fault diagnostics. Mech. Syst. Signal Process. 191, 110208 (2023). https:\/\/doi.org\/10.1016\/j.ymssp.2023.110208","journal-title":"Mech. Syst. Signal Process."},{"issue":"5","key":"3096_CR14","doi-asserted-by":"publisher","first-page":"1271","DOI":"10.3390\/s20051271","volume":"20","author":"AA Mousavi","year":"2020","unstructured":"A.A. Mousavi, C. Zhang, S.F. Masri, G. Gholipour, Structural damage localization and quantification based on a CEEMDAN Hilbert transform neural network approach: a model steel truss bridge case study. Sensors 20(5), 1271 (2020)","journal-title":"Sensors"},{"issue":"9","key":"3096_CR15","doi-asserted-by":"publisher","first-page":"1958","DOI":"10.3390\/electronics12091958","volume":"12","author":"M-G Murariu","year":"2023","unstructured":"M.-G. Murariu, F.-R. Doroban\u021bu, D. T\u0103rniceriu, A novel automated empirical mode decomposition (EMD) based method and spectral feature extraction for epilepsy EEG signals classification. Electronics 12(9), 1958 (2023). https:\/\/doi.org\/10.3390\/electronics12091958","journal-title":"Electronics"},{"key":"3096_CR16","doi-asserted-by":"crossref","unstructured":"R. Murugan, A. Balajee, L. Senbagamalar, S.M. Ganie, 11 machine learning and signal processing methodologies to diagnose human knee joint disorders. Artif. Intell. Knowl. Process. Improv. Decis. Mak. Predict. 119 (2023)","DOI":"10.1201\/9781003328414-11"},{"key":"3096_CR17","doi-asserted-by":"crossref","unstructured":"G. Rajalakshmi, C. Vinothkumar, A. Anne Frank Joe, T. Thaj Mary Delsy, Vibroarthographic signal analysis of bone disorders using arduino and piezoelectric sensors. In: 2019 International Conference on Communication and Signal Processing (ICCSP), pp. 0082\u20130086. IEEE (2019)","DOI":"10.1109\/ICCSP.2019.8698064"},{"key":"3096_CR18","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1007\/s11517-007-0278-7","volume":"46","author":"RM Rangayyan","year":"2008","unstructured":"R.M. Rangayyan, Y.F. Wu, Screening of knee-joint vibroarthrographic signals using statistical parameters and radial basis functions. Med. Biol. Eng. Comput. 46, 223 (2008)","journal-title":"Med. Biol. Eng. Comput."},{"issue":"6\u20137","key":"3096_CR19","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1080\/15325008.2022.2136297","volume":"50","author":"R Satheesh","year":"2022","unstructured":"R. Satheesh, S. Rajan, Assessment of mode shape in power system using TVF-EMD and spectral analysis. Electr. Power Componen. Syst. 50(6\u20137), 349\u2013358 (2022)","journal-title":"Electr. Power Componen. Syst."},{"key":"3096_CR20","doi-asserted-by":"crossref","unstructured":"M.S. Begum, A.V.M.B. Aruna, A. Balajee, R. Murugan, An artificial intelligent methodology to classify knee joint disorder using machine learning and image processing techniques. Cogn. Anal. Reinf. Learn. Theor. Tech. Appl. 167\u2013187 (2024)","DOI":"10.1002\/9781394214068.ch9"},{"key":"3096_CR21","doi-asserted-by":"crossref","unstructured":"S. Sridevi, B. Indira, S. S. Dutta, S. Sandeep, A. Sreenivasan, Quantum enhanced support vector machine with instantaneous quantum polynomial encoding for improved cyclone classification. In: 2023 6th International Conference on Recent Trends in Advance Computing (ICRTAC), pp. 748\u2013752. IEEE (2023)","DOI":"10.1109\/ICRTAC59277.2023.10480791"},{"key":"3096_CR22","doi-asserted-by":"publisher","first-page":"126963","DOI":"10.1016\/j.neucom.2023.126963","volume":"564","author":"Y Sun","year":"2024","unstructured":"Y. Sun, Z. Peng, J. Hu, B.K. Ghosh, Event-triggered critic learning impedance control of lower limb exoskeleton robots in interactive environments. Neurocomputing 564, 126963 (2024). https:\/\/doi.org\/10.1016\/j.neucom.2023.126963","journal-title":"Neurocomputing"},{"key":"3096_CR23","doi-asserted-by":"publisher","first-page":"115571","DOI":"10.1016\/j.jsv.2020.115571","volume":"485","author":"G Tu","year":"2020","unstructured":"G. Tu, X. Dong, S. Chen, B. Zhao, Hu. Lan, Z. Peng, Iterative nonlinear chirp mode decomposition: a Hilbert-Huang transform-like method in capturing intra-wave modulations of nonlinear responses. J. Sound Vib. 485, 115571 (2020)","journal-title":"J. Sound Vib."},{"key":"3096_CR24","first-page":"1","volume":"72","author":"P Tu","year":"2023","unstructured":"P. Tu, J. Li, H. Wang, Y. Li, W. Xiang, A novel lower-limb coordination assessment scheme using multi-scale nonlinear coupling characteristics with sEMG. IEEE Trans. Instrum. Meas. 72, 1\u201311 (2023)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"3096_CR25","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1016\/j.matcom.2023.04.034","volume":"212","author":"M Umar","year":"2023","unstructured":"M. Umar, Z. Sabir, M.A.Z. Raja, H.M. Baskonus, M.R. Ali, N.A. Shah, Heuristic computing with sequential quadratic programming for solving a nonlinear hepatitis B virus model. Math. Comput. Simul 212, 234\u2013248 (2023). https:\/\/doi.org\/10.1016\/j.matcom.2023.04.034","journal-title":"Math. Comput. Simul"},{"key":"3096_CR26","doi-asserted-by":"publisher","first-page":"106443","DOI":"10.1016\/j.ymssp.2019.106443","volume":"135","author":"J Wang","year":"2020","unstructured":"J. Wang, Du. Guifu, Z. Zhu, C. Shen, Q. He, Fault diagnosis of rotating machines based on the EMD manifold. Mech. Syst. Signal Process. 135, 106443 (2020)","journal-title":"Mech. Syst. Signal Process."},{"key":"3096_CR27","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.ymssp.2016.09.032","volume":"86","author":"Y Wang","year":"2017","unstructured":"Y. Wang, F. Liu, Z. Jiang, S. He, Q. Mo, Complex variational mode decomposition for signal processing applications. Mech. Syst. Signal Process. 86, 75\u201385 (2017)","journal-title":"Mech. Syst. Signal Process."},{"key":"3096_CR28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-44284-5","volume-title":"Knee joint vibroarthrographic signal processing and analysis","author":"Y Wu","year":"2015","unstructured":"Y. Wu, Knee joint vibroarthrographic signal processing and analysis (Springer, Berlin\/Heidelberg, Germany, 2015)"},{"key":"3096_CR29","doi-asserted-by":"publisher","first-page":"1406","DOI":"10.1007\/s00167-017-4780-7","volume":"26","author":"C Yang","year":"2018","unstructured":"C. Yang, Y. Tashiro, A. Lynch, Fu. Freddie, W. Anderst, Kinematics and arthrokinematics in the chronic ACL-deficient knee are altered even in the absence of instability symptoms. Knee Surg. Sports Traumatol. Arthrosc. 26, 1406\u20131413 (2018)","journal-title":"Knee Surg. Sports Traumatol. Arthrosc."},{"issue":"6","key":"3096_CR30","doi-asserted-by":"publisher","first-page":"296","DOI":"10.3390\/a16060296","volume":"16","author":"Yu Miaomiao","year":"2023","unstructured":"Yu. Miaomiao, H. Yuan, K. Li, L. Deng, Noise cancellation method based on TVF-EMD with bayesian parameter optimization. Algorithms 16(6), 296 (2023). https:\/\/doi.org\/10.3390\/a16060296","journal-title":"Algorithms"},{"key":"3096_CR31","doi-asserted-by":"publisher","first-page":"4666","DOI":"10.1109\/TSP.2024.3437450","volume":"72","author":"H Zhang","year":"2024","unstructured":"H. Zhang, Lu. Wenhuan, J. Wei, X. Huang, X. Yang, Lu. Xugang, Efficient singular spectrum mode ensemble for extracting wide-band components in overlapping spectral environments. IEEE Trans. Signal Process. 72, 4666\u20134681 (2024). https:\/\/doi.org\/10.1109\/TSP.2024.3437450","journal-title":"IEEE Trans. Signal Process."},{"issue":"2","key":"3096_CR32","doi-asserted-by":"publisher","first-page":"435","DOI":"10.3390\/jmse11020435","volume":"11","author":"L Zhao","year":"2023","unstructured":"L. Zhao, Z. Li, J. Zhang, B. Teng, An integrated complete ensemble empirical mode decomposition with adaptive noise to optimize LSTM for significant wave height forecasting. J. Mar. Sci. Eng. 11(2), 435 (2023)","journal-title":"J. Mar. Sci. Eng."},{"issue":"2","key":"3096_CR33","doi-asserted-by":"publisher","first-page":"291","DOI":"10.3390\/math11020291","volume":"11","author":"Y Zhao","year":"2023","unstructured":"Y. Zhao, M. Zhang, Q. Ni, X. Wang, Adaptive nonparametric density estimation with b-spline bases. Mathematics 11(2), 291 (2023)","journal-title":"Mathematics"},{"key":"3096_CR34","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1016\/j.isatra.2020.06.011","volume":"106","author":"J Zheng","year":"2020","unstructured":"J. Zheng, H. Pan, Mean-optimized mode decomposition: an improved EMD approach for non-stationary signal processing. ISA Trans. 106, 392\u2013401 (2020)","journal-title":"ISA Trans."}],"container-title":["Circuits, Systems, and Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00034-025-03096-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00034-025-03096-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00034-025-03096-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T01:26:32Z","timestamp":1769995592000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00034-025-03096-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,11]]},"references-count":34,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["3096"],"URL":"https:\/\/doi.org\/10.1007\/s00034-025-03096-8","relation":{},"ISSN":["0278-081X","1531-5878"],"issn-type":[{"value":"0278-081X","type":"print"},{"value":"1531-5878","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,11]]},"assertion":[{"value":"27 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 March 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 April 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 August 2025","order":6,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":7,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Acknowledgment section has been updated.","order":8,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}