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

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

COMPARISON OF SELECTED CLASSIFICATION METHODS BASED ON MACHINE LEARNING AS A DIAGNOSTIC TOOL FOR KNEE JOINT CARTILAGE DAMAGE BASED ON GENERATED VIBROACOUSTIC PROCESSES

Osteoarthritis is one of the most common cause of disability among elderly. It can affect every joint in human body, however, it is most prevalent in hip, knee, and hand joints. Early diagnosis of cartilage lesions is essential for fast and accurate treatment, which can prolong joint function. Available diagnostic methods include conventional X-ray, ultrasound and magnetic resonance imaging. However, those diagnostic modalities are not suitable for screening purposes. Vibroarthrography is proposed in literature as a screening method for cartilage lesions. However, exact method of signal acquisition as well as classification method is still not well established in literature. In this study, 84 patients were assessed, of whom 40 were in the control group and 44 in the study group. Cartilage status in the study group was evaluated during surgical treatment. Multilayer perceptron - MLP, radial basis function - RBF, support vector method - SVM and naive classifier – NBC were introduced in this study as classification protocols. Highest accuracy (0.893) was found when MLP was introduced, also RBF classification showed high sensitivity (0.822) and specificity (0.821). On the other hand, NBC showed lowest diagnostic accuracy reaching 0.702. In conclusion vibroarthrography presents a promising diagnostic modality for cartilage evaluation in clinical setting with the use of MLP and RBF classification methods

KNEE JOINT OSTEOARTHRITIS DIAGNOSIS BASED ON SELECTED ACOUSTIC SIGNAL DISCRIMINANTS USING MACHINE LEARNING

This paper presents the results of a preliminary study on simplified diagnosis of osteoarthritis of the knee joint based on generated vibroacoustic processes. The analysis was based on acoustic signals recorded in a group of 50 people, half of whom were healthy, and the other half - people with previously confirmed degenerative changes. Selected discriminants of the signals were determined and statistical analysis was performed to allow selection of optimal discriminants used at a later stage as input to the classifier. The best results of classification using artificial neural networks (ANN) of RBF (Radial Basis Function) and MLP (Multilevel Perceptron) types are presented. For the problem involving the classification of cases into one of two groups HC (Healthy Control) and OA (Osteoarthritis) an accuracy of 0.9 was obtained, with a sensitivity of 0.885 and a specificity of 0.917. It is shown that vibroacoustic diagnostics has great potential in the non-invasive assessment of damage to joint structures of the knee

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

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

Gait Verification using Knee Acceleration Signals

A novel gait recognition method for biometric applications is proposed. The approach has the following distinct features. First, gait patterns are determined via knee acceleration signals, circumventing difficulties associated with conventional vision-based gait recognition methods. Second, an automatic procedure to extract gait features from acceleration signals is developed that employs a multiple-template classification method. Consequently, the proposed approach can adjust the sensitivity and specificity of the gait recognition system with great flexibility. Experimental results from 35 subjects demonstrate the potential of the approach for successful recognition. By setting sensitivity to be 0.95 and 0.90, the resulting specificity ranges from 1 to 0.783 and 1.00 to 0.945, respectively

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

Removal of artifacts in knee joint vibroarthrographic signals using ensemble empirical mode decomposition and detrended fluctuation analysis

National Natural Science Foundation of China [81101115, 31200769]; Natural Science Foundation of Fujian Province of China [2011J01371]; Fundamental Research Funds for the Central Universities of China [2010121061]; Program for New Century Excellent Talents in Fujian Province UniversityHigh-resolution knee joint vibroarthrographic (VAG) signals can help physicians accurately evaluate the pathological condition of a degenerative knee joint, in order to prevent unnecessary exploratory surgery. Artifact cancellation is vital to preserve the quality of VAG signals prior to further computer-aided analysis. This paper describes a novel method that effectively utilizes ensemble empirical mode decomposition (EEMD) and detrended fluctuation analysis (DFA) algorithms for the removal of baseline wander and white noise in VAG signal processing. The EEMD method first successively decomposes the raw VAG signal into a set of intrinsic mode functions (IMFs) with fast and low oscillations, until the monotonic baseline wander remains in the last residue. Then, the DFA algorithm is applied to compute the fractal scaling index parameter for each IMF, in order to identify the anti-correlation and the long-range correlation components. Next, the DFA algorithm can be used to identify the anti-correlated and the long-range correlated IMFs, which assists in reconstructing the artifact-reduced VAG signals. Our experimental results showed that the combination of EEMD and DFA algorithms was able to provide averaged signal-to-noise ratio (SNR) values of 20.52 dB (standard deviation: 1.14 dB) and 20.87 dB (standard deviation: 1.89 dB) for 45 normal signals in healthy subjects and 20 pathological signals in symptomatic patients, respectively. The combination of EEMD and DFA algorithms can ameliorate the quality of VAG signals with great SNR improvements over the raw signal, and the results were also superior to those achieved by wavelet matching pursuit decomposition and time-delay neural filter