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

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

Statistical Analysis of Gait Maturation in Children Using Nonparametric Probability Density Function Modeling

Analysis of gait dynamics in children may help understand the development of neuromuscular control and maturation of locomotor function. This paper applied the nonparametric Parzen-window estimation method to establish the probability density function (PDF) models for the stride interval time series of 50 children (25 boys and 25 girls). Four statistical parameters, in terms of averaged stride interval (ASI), variation of stride interval (VSI), PDF skewness (SK), and PDF kurtosis (KU), were computed with the Parzen-window PDFs to study the maturation of stride interval in children. By analyzing the results of the children in three age groups (aged 3–5 years, 6–8 years, and 10–14 years), we summarize the key findings of the present study as follows. (1) The gait cycle duration, in terms of ASI, increases until 14 years of age. On the other hand, the gait variability, in terms of VSI, decreases rapidly until 8 years of age, and then continues to decrease at a slower rate. (2) The SK values of both the histograms and Parzen-window PDFs for all of the three age groups are positive, which indicates an imbalance in the stride interval distribution within an age group. However, such an imbalance would be meliorated when the children grow up. (3) The KU values of both the histograms and Parzen-window PDFs decrease with the body growth in children, which suggests that the musculoskeletal growth enables the children to modulate a gait cadence with ease. (4) The SK and KU results also demonstrate the superiority of the Parzen-window PDF estimation method to the Gaussian distribution modeling, for the study of gait maturation in children.This work was supported by the Fundamental Research Funds for the Central Universities of China (grant no. 2010121061), the Natural Science Foundation of Fujian (grant no. 2011J01371), and the National Natural Science Foundation of China (grant no. 81101115). N. Xiang was supported by Xiamen University Undergraduate Innovation Training Project (grant no. XDDC201210384072). Z. T. Zhong was supported by the Fundamental Research Funds for the Central Universities (grant no. CXB2011023). J. He was supported by the Xiamen University undergraduate student innovative experiment project (grant no. XDDC2011007). The authors acknowledge Hausdorff et al. for providing the data of gait experiments with public access via PhysioNet

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

Detrending Knee Joint Vibration Signals with a Cascade Moving Average Filter

Knee joint vibration signals are very useful for computer-aided analysis of the pathological conditions in the knee. In a vibration arthrometry test, the legs of patients with knee joint disorders may tremble due to the reaction of pain, which causes the baseline wander that may affect the diagnostic decision making in medical study. This paper presents a new type of cascade moving average filter with hierarchical layers to remove the baseline wander in the raw knee joint vibration signals. The first layer of the cascade filter contains two moving averaging operators with the same order. The five tail inputs of the first moving averaging operator are overlapping with the beginning inputs of the successive operator. The piecewise linear trends estimated by the moving average operators in the first layer were smoothed in the final cascade filter output. The simulation results showed that the cascade filter can effectively remove the baseline wander in the raw knee joint vibration signals

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

Review and classification of variability analysis techniques with clinical applications

Analysis of patterns of variation of time-series, termed variability analysis, represents a rapidly evolving discipline with increasing applications in different fields of science. In medicine and in particular critical care, efforts have focussed on evaluating the clinical utility of variability. However, the growth and complexity of techniques applicable to this field have made interpretation and understanding of variability more challenging. Our objective is to provide an updated review of variability analysis techniques suitable for clinical applications. We review more than 70 variability techniques, providing for each technique a brief description of the underlying theory and assumptions, together with a summary of clinical applications. We propose a revised classification for the domains of variability techniques, which include statistical, geometric, energetic, informational, and invariant. We discuss the process of calculation, often necessitating a mathematical transform of the time-series. Our aims are to summarize a broad literature, promote a shared vocabulary that would improve the exchange of ideas, and the analyses of the results between different studies. We conclude with challenges for the evolving science of variability analysis