APPLICATIONS IN VIBROARTHROGRAPHY: ASSESSMENTS OF INSTABILITY IN TOTAL HIP ARTHROPLASTY, CAM-POST ENGAGEMENT IN TOTAL KNEE ARTHROPLASTY, AND VISCOSUPPLEMENTATION IN OSTEOARTHRITIC KNEES

Measurement of joint sounds and vibrations for non-invasive orthopaedic diagnostic purposes has slowly advanced since the 1960s. Most work has been focused in the development of methods for screening of abnormal knees. To date the technique has not gained clinical traction as is it fraught with various obstacles and skepticism. This doctoral thesis is neither an argument in favor of nor against the clinical use of vibroarthrography for musculoskeletal diagnostics in humans, but rather an exploration of its potential in cases of orthopaedic interest. These areas include 1) instability in total hip arthroplasty, 2) cam-post engagement in posterior stabilized total knee arthroplasty, and 3) viscosupplementation in osteoarthritic knees. It was expected that each of these unique cases would be characterized by dynamic phenomena that could be measured in the form of surface vibrations at the skin.Methods previously presented in various vibroarthrography research were adopted, modified, and expounded upon to best suit the needs of each experiment. In a mechanical hip simulator, it was found that vibroarthrography could be effectively used to distinguish the difference between 1 mm and 2 mm of hip separation. In posterior stabilized total knee arthroplasty subjects, it was found that multiple vibroarthrographic features may be used to approximate the occurrence of cam-post engagement, and that vibrations measured at the joint surface may be correlated to cam-post engagement velocity. In osteoarthritic knees, the relationship between clinical evidence, viscosupplementation, and vibroarthrography varied on a case by case basis.To the knowledge of the author, all three of these experiments are the first of their kind. Ultimately, the methods and results presented within provide new foundations for vibroarthrography that may be used to further explore the clinical potential of this noninvasive diagnostic

Screening of knee-joint vibroarthrographic signals using statistical parameters and radial basis functions

Externally detected vibroarthrographic (VAG) signals bear diagnostic information related to the roughness, softening, breakdown, or the state of lubrication of the articular cartilage surfaces of the knee joint. Analysis of VAG signals could provide quantitative indices for noninvasive diagnosis of articular cartilage breakdown and staging of osteoarthritis. We propose the use of statistical parameters of VAG signals, including the form factor involving the variance of the signal and its derivatives, skewness, kurtosis, and entropy, to classify VAG signals as normal or abnormal. With a database of 89 VAG signals, screening efficiency of up to 0.82 was achieved, in terms of the area under the receiver operating characteristics curve, using a neural network classifier based on radial basis functions

Knee Joint Vibration Signal Analysis with Matching Pursuit Decomposition and Dynamic Weighted Classifier Fusion

Analysis of knee joint vibration (VAG) signals can provide quantitative indices for detection of knee joint pathology at an early stage. In addition to the statistical features developed in the related previous studies, we extracted two separable features, that is, the number of atoms derived from the wavelet matching pursuit decomposition and the number of significant signal turns detected with the fixed threshold in the time domain. To perform a better classification over the data set of 89 VAG signals, we applied a novel classifier fusion system based on the dynamic weighted fusion (DWF) method to ameliorate the classification performance. For comparison, a single leastsquares support vector machine (LS-SVM) and the Bagging ensemble were used for the classification task as well. The results in terms of overall accuracy in percentage and area under the receiver operating characteristic curve obtained with the DWF-based classifier fusion method reached 88.76% and 0.9515, respectively, which demonstrated the effectiveness and superiority of the DWF method with two distinct features for the VAG signal analysis

In Vivo Mechanics of Cam-Post Engagement in Fixed and Mobile Bearing TKA and Vibroarthrography of the Knee Joint

The objective of this dissertation was to determine the mechanics of the cam-post mechanism for subjects implanted with a Rotating Platform (RP) PS TKA, Fixed Bearing (FB) PS TKA or FB Bi-Cruciate Stabilized (BCS) TKA. Additionally, a secondary goal of this dissertation was to investigate the feasibility of vibroarthrography in correlating in-vivo vibrations with features exhibited in native, arthritic and implanted knees. In-vivo, 3D kinematics were determined for subjects implanted with nine knees with a RP-PS TKA, five knees with a FB-PS TKA, and 10 knees with a FB-BCS TKA, while performing a deep knee bend. Distance between the cam-post surfaces was monitored throughout flexion and the predicted contact map was calculated. A forward dynamic model was constructed for 3 test cases to determine the variation in the nature of contact forces at the cam-post interaction. Lastly, a different set of patients was monitored using vibroarthrography to determine differences in vibration between native, arthritic and implanted knees. Posterior cam-post engagement occurred at 34° for FB-BCS, 93o for FB-PS and at 97° for RP-PS TKA. In FB-BCS and FB-PS knees, the contact initially occurred on the medial aspect of the tibial post and then moved centrally and superiorly with increasing flexion. For RP-PS TKA, it was located centrally on the post at all times. Force analysis determined that the forces at the cam-post interaction were 1.6*body-weight, 2.0*body-weight, and 1.3*body-weight for the RP-PS, FB-BCS and FB-PS TKA. Sound analysis revealed that there were distinct differences between native and arthritic knees which could be differentiated using a pattern classifier with 97.5% accuracy. Additionally, vibrations from implanted knees were successfully correlated to occurrences such as lift-off and cam-post engagement. This study suggests that mobility of the polyethylene plays a significant role in ensuring proper cam-post interaction in RP-PS TKA. The polyethylene insert rotates axially in accord with the rotating femur, maintaining central cam-post contact. This phenomenon was not observed in the FB-BCS and FB-PS TKAs

Discovery of acoustic emission based biomarker for quantitative assessment of knee joint ageing and degeneration

Based on the study of 34 healthy and 19 osteoarthritic knees in three different age groups (early, middle and late adulthood), this thesis reports the discovery of the potential of knee acoustic emission (AE) as a biomarker for quantitative assessment of joint ageing and degeneration. Signal processing and statistical analysis were conducted on the joint angle signals acquired using electronic goniometers attached to the lateral side of the legs during repeated sit- stand-sit movements. A four-phase movement model derived from joint angle measurement is proposed for statistical analysis, and it consists of the ascending- acceleration and ascending-deceleration phases in the sit-to- stand movement, followed by the descending-acceleration and descending-deceleration phases in the stand-to-sit movement. Through the quantitative assessment of joint angle signals based on the four-phase model established, statistical differences of different knee conditions related to age and degeneration were discovered based on cycle-by- cycle variations and movement symmetry. For AE burst signals acquired from piezo-electric sensors attached to the knee joints during repeated sit-stand-sit movements, the statistical analysis started from the quantity of AE events in the proposed four movement phases and extended to waveform features extracted from AE signals. While the quantity of AE events was found to follow certain statistical trends related to age and degeneration in each movement phase, detail statistical analysis of AE waveform features yielded the peak amplitude value and average signal level of each AE burst as two most significant features. An image based knee AE feature profile is presented based on 2D colour histograms formed by the peak amplitude value and average signal level in four movement phases. It provides not only a visual trend related to knee age and degeneration, but also enables visual assessment of th

Dynamics, Electromyography and Vibroarthrography as Non-Invasive Diagnostic Tools: Investigation of the Patellofemoral Joint

The knee joint plays an essential role in the human musculoskeletal system. It has evolved to withstand extreme loading conditions, while providing almost frictionless joint movement. However, its performance may be disrupted by disease, anatomical deformities, soft tissue imbalance or injury. Knee disorders are often puzzling, and accurate diagnosis may be challenging. Current evaluation approach is usually limited to a detailed interview with the patient, careful physical examination and radiographic imaging. The X-ray screening may reveal bone degeneration, but does not carry sufficient information of the soft tissue conditions. More advanced imaging tools such as MRI or CT are available, but expensive, time consuming and can be used only under static conditions. Moreover, due to limited resolution the radiographic techniques cannot reveal early stage arthritis. The arthroscopy is often the only reliable option, however due to its semi-invasive nature, it cannot be considered as a practical diagnostic tool. Therefore, the motivation for this work was to combine three scientific methods to provide a comprehensive, non-invasive evaluation tool bringing insight into the in vivo, dynamic conditions of the knee joint and articular cartilage degeneration. Electromyography and inverse dynamics were employed to independently determine the forces present in several muscles spanning the knee joint. Though both methods have certain limitations, the current work demonstrates how the use of these two methods concurrently enhances the biomechanical analysis of the knee joint conditions, especially the performance of the extensor mechanism. The kinetic analysis was performed for 12 TKA, 4 healthy individuals in advanced age and 4 young subjects. Several differences in the knee biomechanics were found between the three groups, identifying age-related and post-operative decrease in the extensor mechanism efficiency, explaining the increased effort of performing everyday activities experienced by the elderly and TKA subjects. The concept of using accelerometers to assess the cartilage degeneration has been proven based on a group of 23 subjects with non-symptomatic knees and 52 patients suffering from knee arthritis. Very high success (96.2%) of pattern classification obtained in this work clearly demonstrates that vibroarthrography is a promising, non-invasive and low-cost technique offering screening capabilities

Screening of knee-joint vibroarthrographic signals using probability density functions estimated with Parzen windows

Pathological conditions of knee joints have been observed to cause changes in the characteristics of vibroarthrographic (VAG) signals. Several studies have proposed many parameters for the analysis and classification of VAG signals; however, no statistical modeling methods have been explored to analyze the distinctions in the probability density functions (PDFs) between normal and abnormal VAG signals. In the present work, models of PDFs were derived using the Parzen-window approach to represent the statistical characteristics of normal and abnormal VAG signals. The Kullback-Leibler distance was computed between the PDF of the signal to be classified and the PDF models for normal and abnormal VAG signals. Additional statistical measures, including the mean, standard deviation, coefficient of variation, skewness, kurtosis, and entropy, were also derived from the PDFs obtained. An overall classification accuracy of 77.53%, sensitivity of 71.05%, and specificity of 82.35% were obtained with a database of 89 VAG signals using a neural network with radial basis functions with the leave-one-out procedure for cross validation. The screening efficiency was derived to be 0.8322, in terms of the area under the receiver operating characteristics curve. (C) 2009 Elsevier Ltd. All rights reserved

CLASSIFICATION OF KNEE-JOINT VIBROARTHROGRAPHIC SIGNALS USING TIME-DOMAIN AND TIME-FREQUENCY DOMAIN FEATURES AND LEAST-SQUARES SUPPORT VECTOR MACHINE

Analysis of knee-joint vibration sounds, also known as vibroarthrographic (VAG) signals, could lead to a noninvasive clinical tool for early detection of knee-joint pathology. In this paper, we employed the wavelet matching pursuit (MP) decomposition and signal variability for time-frequency domain and time-domain analysis of VAG signals. The number of wavelet MP atoms and the number of significant turns detected with the fixed threshold from signal variability analysis were extracted as prominent features for the classification over the data set of 89 VAG signals. Compared with the Fisher linear discriminant analysis, the nonlinear least-squares support vector machine (LS-SVM) is able to achieve higher overall accuracy of 73.03%, and the area of 0.7307 under the receiver operating characteristic curve

Machine Fault Classification Based on Local Discriminant Bases and Locality Preserving Projections

Machine fault classification is an important task for intelligent identification of the health patterns for a mechanical system being monitored. Effective feature extraction of vibration data is very critical to reliable classification of machine faults with different types and severities. In this paper, a new method is proposed to acquire the sensitive features through a combination of local discriminant bases (LDB) and locality preserving projections (LPP). In the method, the LDB is employed to select the optimal wavelet packet (WP) nodes that exhibit high discrimination from a redundant WP library of wavelet packet transform (WPT). Considering that the obtained discriminatory features on these selected nodes characterize the class pattern in different sensitivity, the LPP is then applied to address mining inherent class pattern feature embedded in the raw features. The proposed feature extraction method combines the merits of LDB and LPP and extracts the inherent pattern structure embedded in the discriminatory feature values of samples in different classes. Therefore, the proposed feature not only considers the discriminatory features themselves but also considers the dynamic sensitive class pattern structure. The effectiveness of the proposed feature is verified by case studies on vibration data-based classification of bearing fault types and severities