[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128086-en":3,"doc-seo-128086-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128086,5909887254083,"Miles","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","A Novel Ensemble Empirical Decomposition and Time-Frequency Analysis Approach for Vibroarthrographic Signal Processing","Signal processing techniques support real-world applications across sensor analysis, defense, and clinical and biomedical domains. In healthcare, computer-aided diagnostic (CAD) systems assist medical professionals by interpreting signals and images, especially in medical imaging and radiological diagnostics. For joint disorder assessment, both time- and frequency-domain analyses handle nonstationary and nonlinear signals. The approach applies Hilbert-Huang Transform with ensemble decomposition (TVF-EMD, EMD variants, VMD) to extract time–frequency features for healthy versus non-healthy classification using LS-SVM and SVM-RFE.","A Novel Ensemble Empirical Decomposition and Time–Frequency Analysis Approach for Vibroarthrographic Signal Processing  \nSurbhi Bhatia Khan1,2,3 · A. Balajee4 · S. Sheik Mohideen Shah5 ·  \nT. R. Mahesh4 · Mohammad Alojail6 · Indrajeet Gupta7  \nReceived: 27 August 2024 / Revised: 4 March 2025 / Accepted: 5 March 2025 © The Author(s) 2025  \nAbstract  \nSignal 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, nonstationary, 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.  \nKeywords Vibroarthrography · Joint disorder · Empirical decomposition · Signal processing · Machine learning  \nExtended author information available on the last page of the article  \n1 Introduction  \nOne of the critical disorder analyses in the medical field trending nowadays is the diagnosis ofjoint disorders, which are very complex duetothe structure ofthe different human joints, namely, shoulder joints, hip joints, elbow joints, and knee joints. At present, there are two different methods of diagnosing in practice. One is the invasive method, and the second one is the non-invasive method. An invasive method is similar to arthroscopy, as it is not only expensive but not suitable for regular diagnostics [9] . The other disadvantage of this method is that it is entirely prone to infection [1] . An alternative diagnostic method is a non-invasive method; similar methods include computer tomography (CT), magnetic resonance imaging (MRI), X-rays, etc.  \nVibroarthrography (VAG) method is one of the non-invasive methods. This follows the natural phenomena of performing analysis of high-frequency vibroacoustic radiation, which is obtained from the relative movement of articular surfaces ofthe synovial joint (diarthrosis) [20] . In physical conditions, articulate the outside is covered by hyaline ossein that is smooth and slimy, which detects optimal arthro-kinematic movement quality. Osteoarthritis is again and again observed by using the Patello Femoral Joint (PFJ) [8] . A portion of the knee joint is complex and can be explained by its specified biomechanical surroundings and massive involvement in day-to-day activity. VAG signals onward Computer Aided Diagnostic could contribute those attributes for diagnosing knee joint disorders. VAG signals work on the basis of acoustic sounds or the other vibrations sound emitted from the mid of the patella at the time of active movemen","cbCaiv2USvxvAonB","https://ap.wps.com/l/cbCaiv2USvxvAonB","pdf",2960320,3,1,23,"English","en",105,"# Introduction\n## Non-invasive versus invasive joint diagnosis\n## Vibroarthrography signals and properties\n## Motivation for multiclass CAD-based classification\n# Proposed approach (signal decomposition and classification)\n## Hilbert-Huang Transform and ensemble decomposition\n## Time–frequency feature extraction\n## Machine learning classification models","[{\"question\":\"Why are time-domain and frequency-domain analyses used for joint disorder signals?\",\"answer\":\"They help analyze complex, nonstationary, and nonlinear signals. Using both domains improves characterization of vibroarthrographic patterns.\"},{\"question\":\"Which decomposition and transform steps are included in the proposed vibroarthrographic signal processing approach?\",\"answer\":\"The method uses Hilbert-Huang Transform, including Empirical Mode Decomposition (and ensemble variants such as TVF-EMD and CEEMDAN with adaptive noise), followed by Hilbert transform analysis. It also employs Variation Mode Decomposition to obtain mode signals.\"},{\"question\":\"How is the classification between healthy and non-healthy samples performed?\",\"answer\":\"Time and frequency characteristics are extracted as features (e.g., pixel intensity, mean, standard deviation) and fed into machine learning models. The paper uses LS-SVM and SVM with Recursive Feature Elimination to improve accuracy.\"}]","A Novel Ensemble Empirical Decomposition and Time-Frequency Analysis Approach for Vibroarthrographic Signal Processing | PDF",1785944731,58,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"a-novel-ensemble-empirical-decomposition-and-time-frequency-analysis-approach-for-vibroarthrographic-signal-processing","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/a-novel-ensemble-empirical-decomposition-and-time-frequency-analysis-approach-for-vibroarthrographic-signal-processing/128086/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why are time-domain and frequency-domain analyses used for joint disorder signals?","Question",{"text":76,"@type":77},"They help analyze complex, nonstationary, and nonlinear signals. Using both domains improves characterization of vibroarthrographic patterns.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which decomposition and transform steps are included in the proposed vibroarthrographic signal processing approach?",{"text":81,"@type":77},"The method uses Hilbert-Huang Transform, including Empirical Mode Decomposition (and ensemble variants such as TVF-EMD and CEEMDAN with adaptive noise), followed by Hilbert transform analysis. It also employs Variation Mode Decomposition to obtain mode signals.",{"name":83,"@type":74,"acceptedAnswer":84},"How is the classification between healthy and non-healthy samples performed?",{"text":85,"@type":77},"Time and frequency characteristics are extracted as features (e.g., pixel intensity, mean, standard deviation) and fed into machine learning models. 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