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

A method of feature fusion and dimension reduction for knee joint pathology screening and separability evaluation criteria

BACKGROUND AND OBJECTIVE

Knee-joint vibroarthrographic (VAG) signal is an effective method for performing a non-invasive knee osteoarthritis (KOA) diagnosis, VAG signal analysis plays a crucial role in achieving the early pathological screening of the knee joint. In order to improve the accuracy of knee pathology screening and to investigate the method suitable for embedded in wearable diagnostic device for knee joint, this paper proposes a knee pathology screening method. Aiming to fill the gap of lacking suitable and unified evaluation indexes for single feature and fusion feature, this paper proposes feature separability evaluation criteria.

METHODS

In this paper, we propose a knee joint pathology screening method based on feature fusion and dimension reduction combined with random forest classifier, as well as, the evaluation criteria of feature separability. As for pathological screening method, this paper proposes the idea of multi-dimensional feature fusion, using principal component analysis (PCA) to reduce the redundant part of fusion feature (F-F) to obtain deep fusion feature (D-F-F) with more separability. Meanwhile, this paper proposes the maximal information coefficient (MIC) and correlation matrix collinearity (CMC) feature evaluation criteria, these not only can be used as new feature quantitative metrics, but also illustrate that the divisibility of the deep fusion feature is more potent than that before feature dimension reduction.

RESULTS

The experimental results show that the method in this paper has good performance in pathology classification on random forest classifier with 96% accuracy, especially the accuracy of SVM and K-NN are also improved after feature dimension reduction.

CONCLUSION

The results indicate that this classification research has high screening efficiency for KOA diagnosis and could provide a feasible method for computer-assisted non-invasive diagnosis of KOA. And we provide a novel way for separability evaluation of VAG signal features.

Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN—Part II: Patellofemoral Joint

Cartilage loss due to osteoarthritis (OA) in the patellofemoral joint provokes pain, stiffness, and restriction of joint motion, which strongly reduces quality of life. Early diagnosis is essential for prolonging painless joint function. Vibroarthrography (VAG) has been proposed in the literature as a safe, noninvasive, and reproducible tool for cartilage evaluation. Until now, however, there have been no strict protocols for VAG acquisition especially in regard to differences between the patellofemoral and tibiofemoral joints. The purpose of this study was to evaluate the proposed examination and acquisition protocol for the patellofemoral joint, as well as to determine the optimal examination protocol to obtain the best diagnostic results. Thirty-four patients scheduled for knee surgery due to cartilage lesions were enrolled in the study and compared with 33 healthy individuals in the control group. VAG acquisition was performed prior to surgery, and cartilage status was evaluated during the surgery as a reference point. Both closed (CKC) and open (OKC) kinetic chains were assessed during VAG. The selection of the optimal signal measures was performed using a neighborhood component analysis (NCA) algorithm. The classification was performed using multilayer perceptron (MLP) and radial basis function (RBF) neural networks. The classification using artificial neural networks was performed for three variants: I. open kinetic chain, II. closed kinetic chain, and III. open and closed kinetic chain. The highest diagnostic accuracy was obtained for variants I and II for the RBF 9-35-2 and MLP 10-16-2 networks, respectively, achieving a classification accuracy of 98.53, a sensitivity of 0.958, and a specificity of 1. For variant III, a diagnostic accuracy of 97.79 was obtained with a sensitivity and specificity of 0.978 for MLP 8-3-2. This indicates a possible simplification of the examination protocol to single kinetic chain analyses.

Monitoring deterioration of knee osteoarthritis using vibration arthrography in daily activities

BACKGROUND AND OBJECTIVE

Pathological recognition of knee joint using vibration arthrography (VAG) is increasingly becoming prevailed, due to the non-invasive and non-radiative benefits. However, knee joint health monitoring using VAG signals is a difficult problem, since VAG signals are contaminated by strong motion artifacts (MA) caused by knee movements during daily activities, such as squatting. So far few works have investigated this problem. Existing studies mainly focused on clinical diagnosis of knee disorders for 2-class (normal/abnormal) classification using VAG signals, which are less contaminated by MA in the scene when subjects perform knee extension and flexion movements in seated position. The purpose of this study is to propose a framework to monitor knee joint health during daily activities.

METHODS

In this paper, a general framework is designed to monitor knee joint health, which consists of VAG enhancement, feature extraction and fusion, and classification. VAG enhancement aims to remove MA and irrelevant components of knee joint pathologies in raw VAG signals. Distinctive features from enhanced VAG signals are obtained in feature extraction and fusion. Classification can not only distinguish whether the knee joint is normal or abnormal, but also distinguish the grade of deterioration of knee osteoarthritis.

RESULTS

813 VAG signals from VAG-OA dataset, which is currently the largest VAG dataset, have been collected from medical cases in Xijing Hospital of the Fourth Military Medical University during daily activities. Experimental results on VAG-OA dataset showed that the accuracy of 2-class (normal/abnormal) classification was 95.9% with sensitivity 98.1% and specificity 93.3%. For 5-class classification based on deterioration grades of osteoarthritis (OA), we obtained accuracy 74.4%, sensitivity 52.6% and specificity 78.3%.

CONCLUSION

The VAG-OA dataset can be used not only for knee joint health monitoring but also for clinical diagnosis. The designed framework on VAG-OA dataset has high classification accuracy, which is of great value to monitor knee joint health using VAG signals during daily activities. The results also demonstrate that the designed framework significantly outperforms the baselines and several state-of-the-art methods.

A Feasibility Study on Tribological Origins of Knee Acoustic Emissions

OBJECTIVE

Considering the knee as a fluid-lubricated system, articulating surfaces undergo different lubrication modes and generate joint acoustic emissions (JAEs) while moving. The goal of this study is to compare knee biomechanical signals against synchronously recorded joint sounds and assess the hypothesis that these JAEs are attributed to tribological origins.

METHODS

JAE, electromyography, ground reaction force signals, and motion capture markers were synchronously recorded from 10 healthy subjects while performing two-leg and one-leg squat exercises. The biomechanical signals were processed with standard inverse dynamic analysis through musculoskeletal modeling, and a tribological parameter, lubrication coefficient, was calculated from these signals. Besides, JAEs were divided into short windows, and 64 time-frequency features were extracted. The lubrication coefficients and joint sound features of the two-leg squats were used to label the windows and train a classifier that discriminates the knee lubrication modes only based on JAE features. Then, the classifier was used to predict the label of one-leg squat JAE windows. To evaluate these results, the predicted joint sound labels were directly compared against the associated lubrication coefficients.

RESULTS

The trained classifier achieves a high test-accuracy of 84% distinguishing lubrication modes of the one-leg squat JAE windows. The Pearson correlation coefficient between the estimated friction coefficient and the predicted JAE scores was 0.830.08. Furthermore, the lubrication coefficient threshold, separating two lubrication modes, was calculated from joint sound labels, and it decreased by half from two-leg to one-leg squats. This result was consistent with the tribological changes in the knee load as it was inversely doubled in one-leg squats. These results verify that JAEs contain salient information on knee tribology.

SIGNIFICANCE

This study supports the potential use of JAEs as a quantitative digital biomarker to extract tribological information about joint lubrication modes and loading conditions. Since arthritis and many other conditions impact the roughness of cartilage and other surfaces within the knee, the use of JAEs in clinical applications could thereby have broad implications for studying joint frictions and monitoring joint structural changes with wearable devices.

Quantifying Asymmetry between Medial and Lateral Compartment Knee Loading Forces using Acoustic Emissions

OBJECTIVE

Osteoarthritis is the most common type of knee arthritis that can be affected by excessive and compressive loads and can affect one or more compartments of the knee: medial, lateral, and patellofemoral. The medial compartment tends to be the most vulnerable to injuries and research suggests that a better understanding of the medial to lateral load distribution conditions could provide insights to the quantitative usage of knee compartments in activities of daily life.

METHODS

To that end, we present a novel method to quantify the directional bias of asymmetry between the medial and lateral compartment knee joint load by recording knee acoustical emissions and analyzing them using a deep neural network in a subject independent model. We placed four miniature contact microphones on the medial and lateral sides of the patella on both the left and right leg. We compared the handcrafted audio features with the automated features extracted from the convolutional autoencoder which is an unsupervised model that learns the comprehensive representation of the input to determine whether these automated features can better represent the signals characteristic in regard to the structural asymmetry of the knee joint. The input to the convolutional auto encoder (CAE) is a time-frequency representation and different types of these images such as spectrogram and scalogram are compared. We also compared the multi-sensor fusion approach with the performance of a single sensor to determine the robustness of using multiple sensors.

RESULTS

Using a representation learning based approach, we developed a subject independent classification model capable of classifying the asymmetry of the medial and lateral joint load across subjects (accuracy = 83%).

CONCLUSION

The result indicates that wavelet coherence which is the time-frequency correlation of two signals using a wavelet transform yields the best accuracy.

SIGNIFICANCE

These findings suggest that acoustic signals could potentially quantify the direction of medial to lateral load distribution which would broaden the implications for wearable sensing technology for monitoring cartilage health and factors responsible for cartilage breakdown and assessing appropriate rehabilitation exercises without overloading on one side.

Acoustic Emissions From Loaded and Unloaded Knees to Assess Joint Health in Patients With Juvenile Idiopathic Arthritis

OBJECTIVE

We studied and compared joint acoustical emissions (JAEs) in loaded and unloaded knees as digital biomarkers for evaluating knee health status during the course of treatment in patients with juvenile idiopathic arthritis (JIA).

METHODS

JAEs were recorded from 38 participants, performing 10 repetitions of unloaded flexion/extension (FE) and loaded squat exercises. A novel algorithm was developed to detect and exclude rubbing noise and loose microphone artifacts from the signals, and then 72 features were extracted. These features were down-selected based on different criteria to train three logistic regression classifiers. The classifiers were trained with healthy and pre-treatment data and were used to predict the knee health scores of post-treatment data for the same patients with JIA who had a follow-up recording. This knee health score represents the probability of having JIA in a subject (0 for healthy and 1 for arthritis).

RESULTS

Post-treatment knee health scores were lower than pre-treatment scores, agreeing with the clinical records of successful treatment. Regarding loaded versus unloaded knee scores, the squats achieved a higher score on average compared to FEs.

CONCLUSION

In healthy subjects with smooth cartilage, the knee scores of squats and FEs were similar indicating that vibrations from the friction of articulating surfaces do not significantly change by the joint load. However, in subjects with JIA, the scores of squats were higher than the scores of FEs, revealing that these two exercises contain different, possibly clinically relevant, information that could be used to further improve this novel assessment modality in JIA.

Quantifying Signal Quality for Joint Acoustic Emissions Using Graph-Based Spectral Embedding

We present a new method for quantifying signal quality of joint acoustic emissions (JAEs) from the knee during unloaded flexion/extension (F/E) exercises. For ten F/E cycles, JAEs were recorded, in a clinical setting, from 34 healthy knees and 13 with a meniscus tear (n=24 subjects). The recordings were first segmented by F/E cycle and described using time and frequency domain features. Using these features, a symmetric k-nearest neighbor graph was created and described using a spectral embedding. We show how the underlying community structure of JAEs was comparable across joint health levels and was highly affected by artifacts. Each F/E cycle was scored by its distance from a diverse set of manually annotated, clean templates and removed if above the artifact threshold. We validate this methodology by showing an improvement in the distinction between the JAEs of healthy and injured knees. Graph community factor (GCF) was used to detect the number of communities in each recording and describe the heterogeneity of JAEs from each knee. Before artifact removal, there was no significant difference between the healthy and injured groups due to the impact of artifacts on the community construction. Following implementation of artifact removal, we observed improvement in knee health classification. The GCF value for the meniscus tear group was significantly higher than the healthy group (p<0.01). With more JAE recordings being taken in the clinic and at home, this paper addresses the need for a robust artifact removal method which is necessary for an accurate description of joint health.

An Interpretable Experimental Data Augmentation Method to Improve Knee Health Classification Using Joint Acoustic Emissions

The characteristics of joint acoustic emissions (JAEs) measured from the knee have been shown to contain information regarding underlying joint health. Researchers have developed methods to process JAE measurements and combined them with machine learning algorithms for knee injury diagnosis. While these methods are based on JAEs measured in controlled settings, we anticipate that JAE measurements could enable accessible and affordable diagnosis of acute knee injuries also in field-deployable settings. However, in such settings, the noise and interference would be greater than in sterile, laboratory environments, which could decrease the performance of existing knee health classification methods using JAEs. To address the need for an objective noise and interference detection method for JAE measurements as a step towards field-deployable settings, we propose a novel experimental data augmentation method to locate and then, remove the corrupted parts of JAEs measured in clinical settings. In the clinic, we recruited 30 participants, and collected data from both knees, totaling 60 knees (36 healthy and 24 injured knees) to be used subsequently for knee health classification. We also recruited 10 healthy participants to collect artifact and joint sounds (JS) click templates, which are audible, short duration and high amplitude JAEs from the knee. Spectral and temporal features were extracted, and clinical data was augmented in five-dimensional subspace by fusing the existing clinical dataset into experimentally collected templates. Then knee scores were calculated by training and testing a linear soft classifier utilizing leave-one-subject-out cross-validation (LOSO-CV). The area under the curve (AUC) was 0.76 for baseline performance without any window removal with a logistic regression classifier (sensitivity = 0.75, specificity = 0.78). We obtained an AUC of 0.86 with the proposed algorithm (sensitivity = 0.80, specificity = 0.89), and on average, 95% of all clinical data was used to achieve this performance. The proposed algorithm improved knee health classification performance by the added information through identification and collection of common artifact sources in JAE measurements. This method when combined with wearable systems could provide clinically relevant supplementary information for both underserved populations and individuals requiring point-of-injury diagnosis in field-deployable settings.

Time-Frequency Analysis and Parameterisation of Knee Sounds for Non-Invasive Detection of Osteoarthritis

OBJECTIVE

In this work the potential of non-invasive detection of knee osteoarthritis is investigated using the sounds generated by the knee joint during walking.

METHODS

The information contained in the time-frequency domain of these signals and its compressed representations is exploited and their discriminant properties are studied. Their efficacy for the task of normal vs abnormal signal classification is evaluated using a comprehensive experimental framework. Based on this, the impact of the feature extraction parameters on the classification performance is investigated using Classification and Regression Trees, Linear Discriminant Analysis and Support Vector Machine classifiers.

RESULTS

It is shown that classification is successful with an area under the Receiver Operating Characteristic curve of 0.92.

CONCLUSION

The analysis indicates improvements in classification performance when using non-uniform frequency scaling and identifies specific frequency bands that contain discriminative features.

SIGNIFICANCE

Contrary to other studies that focus on sit-to-stand movements and knee flexion/extension, this study used knee sounds obtained during walking. The analysis of such signals leads to non-invasive detection of knee osteoarthritis with high accuracy and could potentially extend the range of available tools for the assessment of the disease as a more practical and cost effective method without requiring clinical setups.

Reconstruction of dual tasking gait pattern in Parkinson’s disease subjects using radial basis function based artificial intelligence

Vertical Ground Reaction Force (VGRF) is a force obtained during gait cycle beneath the feet and is used to screen the severity of Parkinson’s disease (PD) patient’s in clinical environment. This article investigates the VGRF signals (left and right) semblance nature among PD patients and control subjects as a function of time and possibility of reconstructing dual tasking VGRF signal from normal walking VGRF signals using radial basis function (RBF) based artificial intelligence (AI). There are many traditional methods for gait analysis and these methods are purely subjective and none made semblance analysis of same subjects gait pattern in different tasking. In order to overcome the difficulties faced by PD patients, RBF based AI is proposed in this research to reconstruct the dual tasking VGRF signal from normal walking VGRF signal. 93 PD patients with mean age: 66.3 years (63% men), and 73 healthy controls with mean age: 66.3 years (55% men) datasets are used in this work. Proposed RBF network is trained on VGRF signals obtained in normal walking and dual tasking conditions from control. The network was trained with 60% of VGRF data and tested on remaining 40% data. Semblance analysis results are encouraging, and it shows that semblance is high in PD patients than control subjects during dual tasking (P < 0.05). In order to test the findings of semblance analysis, we explicitly reconstruct VGRF signal of clinically significant dual tasking from VGRF signal of normal walking by the proposed RBF method. Findings proved that the proposed RBF network can reconstruct dual tasking VGRF signal of PD patients from their normal walking VGRF signal with high cross correlation (P < 0.0001). These findings pave way for a new adjunct tool to diagnose the gait dynamics of PD patients using the proposed reconstruction method.

Vibroarthrographic Signal Spectral Features in 5-Class Knee Joint Classification

Vibroarthrography (VAG) is a non-invasive and potentially widely available method supporting the joint diagnosis process. This research was conducted using VAG signals classified to five different condition classes: three stages of chondromalacia patellae, osteoarthritis, and control group (healthy knee joint). Ten new spectral features were proposed, distinguishing not only neighboring classes, but every class combination. Additionally, Frequency Range Maps were proposed as the frequency feature extraction visualization method. The results were compared to state-of-the-art frequency features using the Bhattacharyya coefficient and the set of ten different classification algorithms. All methods evaluating proposed features indicated the superiority of the new features compared to the state-of-the-art. In terms of Bhattacharyya coefficient, newly proposed features proved to be over 25% better, and the classification accuracy was on average 9% better.

Vibroarthrographic analysis of patellofemoral joint arthrokinematics during squats with increasing external loads

Background

The patellofemoral joint (PFJ) provides extremely low kinetic friction, which results in optimal arthrokinematic motion quality. Previous research showed that these friction-reducing properties may be diminished due to the increase in articular contact forces. However, this phenomenon has not been analyzed in vivo during functional daily-living activities. The aim of this study was the vibroarthrographic assessment of changes in PFJ arthrokinematics during squats with variated loads.

Methods

114 knees from 57 asymptomatic subjects (23 females and 34 males) whose ages ranged from 19 to 26 years were enrolled in this study. Participants were asked to perform 3 trials: 4 repetitions of bodyweight squats (L0), 4 repetitions of 10 kg barbell back loaded squats (L10), 4 repetitions of 20 kg barbell back loaded squats (L20). During the unloaded and loaded (L10, L20) squats, vibroarthrographic signals were collected using an accelerometer placed on the patella and were described by the following parameters: variation of mean square (VMS), mean range (R4), and power spectral density for frequency of 50–250 Hz (P1) and 250–450 Hz (P2).

Results

Obtained results showed that the lowest values were noted in the unloaded condition and that the increased applied loads had a significant concomitant increase in all the aforementioned parameters bilaterally (p < 0.05).

Conclusion

This phenomenon indicates that the application of increasing knee loads during squats corresponds to higher intensity of vibroacoustic emission, which might be related to higher contact stress and kinetic friction as well as diminished arthrokinematic motion quality.

Vibroarthrography, arthrophonography — methods for non-invasive detection of the knee cartilage damage

Phonoarthrography, vibration arthrography are non-invasive methods for assessing the condition of cartilage and the knee joint as a whole based on the sounds made by the joint movement. Acoustic sensors (accelerometers, microphones) are attached to the knee to measure the knee joint noise both in control groups (young adults and elderly subjects) and in patients with knee osteoarthropathies. Different authors propose different methods for attaching sensors, documenting and analyzing the joint sounds. The identified specific features allowed distinguishing between asymptomatic knee joints and those with osteoarthropathies. Acoustic signals were recorded and processed, and their frequency characteristics were determined and classified. The classification effectiveness correlated with the existing diagnostic tests and hence phonoarthrography and vibration arthrography can be qualified as a useful diagnostic aid.

Using Knee Acoustical Emissions for Sensing Joint Health in Patients with Juvenile Idiopathic Arthritis: A Pilot Study

In this paper, we present a pilot study evaluating novel methods for assessing joint health in patients with Juvenile Idiopathic Arthritis (JIA) using wearable acoustical emission measurements from the knees. Measurements were taken from four control subjects with no known knee injuries, and from four subjects with JIA, before and after treatment. Time and frequency domain features were extracted from the acoustical emission signals and used to compute a knee audio score. The score was used to separate out the two groups of subjects based solely on the sounds their joints produce. It was created using a soft classifier based on gradient boosting trees. The knee audio scores ranged from 0-1 with 0 being a healthy knee and 1 being an involved joint with arthritis. Leave-one-subject-out cross-validation (LOSO-CV) was used to validate the algorithm. The average of the right and left knee audio scores was 0.085±0.099 and 0.89±0.012 for the control group and group with JIA, respectively (p<0.05). The average knee audio score for the subjects with JIA decreased from 0.89±0.012 to 0.25±0.20 following successful treatment (p<0.05). The knee audio score metric successfully distinguished between the control subjects and subjects with JIA. The scores calculated before and after treatment accurately reflected the observed clinical course of the subjects with JIA. After successful treatment, the subjects with JIA were classified as healthy by the algorithm. Knee acoustical emissions provide a novel and cost-effective method for monitoring JIA, and can be used as an objective guide for assessing treatment efficacy.

Analyzing the audio signals of degenerative arthritis with an electronic stethoscope

The advance of modern medical technology has extended people's life and increased the average age of the society. Some chronic diseases are due to the aging of the population and knee joint aging is a common disease in the elderly. Common joint pathology contains degenerative arthritis, arthroncus of knees, and gouty arthritis. Knee joints are the largest and the most complicated joints in a human body as well as the joint bearing huge pressure. Wrong posture, overuse, or vigorous exercise often cause injuries to knee joints, and such injuries could easily result in joint pathology and patients falling and breaking bones due to pain and powerlessness. An acoustic wave technology, aiming at knee joints, is designed to examine a patient's current condition of joints. An electronic stethoscope or high-resolution recording equipment is utilized for collecting necessary signals, through which the wide-frequency audio signals of knee joints could be measured for the analyses and statistics in a back-end computer. Besides, it could classify the groups with healthy and degenerative knee joints to assist physicians in proceeding non-invasive joint degeneration examination clinically and doing the most suitable rehabilitation therapy.

Acoustical Emission Analysis by Unsupervised Graph Mining: A Novel Biomarker of Knee Health Status

OBJECTIVE

To study knee acoustical emission patterns in subjects with acute knee injury immediately following injury and several months after surgery and rehabilitation.

METHODS

We employed an unsupervised graph mining algorithm to visualize heterogeneity of the high-dimensional acoustical emission data, and then to derive a quantitative metric capturing this heterogeneity-the graph community factor (GCF). A total of 42 subjects participated in the studies. Measurements were taken once each from 33 healthy subjects with no known previous knee injury, and twice each from 9 subjects with unilateral knee injury: first, within seven days of the injury, and second, 4-6 months after surgery when the subjects were determined to start functional activities. Acoustical signals were processed to extract time and frequency domain features from multiple time windows of the recordings from both knees, and k-nearest neighbor graphs were then constructed based on these features.

RESULTS

The GCF calculated from these graphs was found to be 18.5 ± 3.5 for healthy subjects, 24.8 ± 4.4 (p = 0.01) for recently injured, and 16.5 ± 4.7 (p = 0.01) at 4-6 months recovery from surgery.

CONCLUSION

The objective GCF scores changes were consistent with a medical professional's subjective evaluations and subjective functional scores of knee recovery.

SIGNIFICANCE

Unsupervised graph mining to extract GCF from knee acoustical emissions provides a novel, objective, and quantitative biomarker of knee injury and recovery that can be incorporated with a wearable joint health system for use outside of clinical settings, and austere/under resourced conditions, to aid treatment/therapy.

Analysis and multiclass classification of pathological knee joints using vibroarthrographic signals

BACKGROUND AND OBJECTIVE

Vibroarthrography (VAG) is a method developed for sensitive and objective assessment of articular function. Although the VAG method is still in development, it shows high accuracy, sensitivity and specificity when comparing results obtained from controls and the non-specific, knee-related disorder group. However, the multiclass classification remains practically unknown. Therefore the aim of this study was to extend the VAG method classification to 5 classes, according to different disorders of the patellofemoral joint.

METHODS

We assessed 121 knees of patients (95 knees with grade I-III chondromalacia patellae, 26 with osteoarthritis) and 66 knees from 33 healthy controls. The vibroarthrographic signals were collected during knee flexion/extension motion using an acceleration sensor. The genetic search algorithm was chosen to select the most relevant features of the VAG signal for classification. Four different algorithms were used for classification of selected features: logistic regression with automatic attribute selection (SimpleLogistic in Weka), multilayer perceptron with sigmoid activation function (MultilayerPerceptron), John Platt's sequential minimal optimization algorithm implementation of support vector classifier (SMO) and random forest tree (RandomForest). The generalization error of classification algorithms was evaluated by stratified 10-fold cross-validation.

RESULTS

We obtained levels of accuracy and AUC metrics over 90%, more than 93% sensitivity and more than 84% specificity for the logistic regression-based method (SimpleLogistic) for a 2-class classification. For the 5-class method, we obtained 69% and 90% accuracy and AUC respectively, and sensitivity and specificity over 91% and 69%.

CONCLUSIONS

The results of this study confirm the high usefulness of quantitative analysis of VAG signals based on classification techniques into normal and pathological knees and as a promising tool in classifying signals of various knee joint disorders and their stages.

Assessment of Relationships Between Joint Motion Quality and Postural Control in Patients With Chronic Ankle Joint Instability

Study Design Controlled laboratory study, cross-sectional. Background Lateral ankle sprains are among the most common injuries encountered during athletic participation. Following the initial injury, there is an alarmingly high risk of reinjury and development of chronic ankle instability (CAI), which is dependent on a combination of factors, including sensorimotor deficits and changes in the biomechanical environment of the ankle joint. Objective To evaluate CAI-related disturbances in arthrokinematic motion quality and postural control and the relationships between them. Methods Sixty-three male subjects (31 with CAI and 32 healthy controls) were enrolled in the study. For arthrokinematic motion quality analysis, the vibroarthrographic signals were collected during ankle flexion/extension motion using an acceleration sensor and described by variability (variance of mean squares [VMS]), amplitude (mean of 4 maximal and 4 minimal values [R4]), and frequency (vibroarthrographic signal bands of 50 to 250 Hz [P1] and 250 to 450 Hz [P2]) parameters. Using the Biodex Balance System, single-leg dynamic balance was measured by overall, anteroposterior, and mediolateral stability indices. Results Values of vibroarthrographic parameters (VMS, R4, P1 and P2) were significantly higher in the CAI group than those in the control group (P<.01). Similar results were obtained for all postural control parameters (overall, anteroposterior, and mediolateral stability indices; P<.05). Moreover, correlations between the overall stability index and VMS, and P1 and P2, as well as between the anteroposterior stability index and P1 and P2, were observed in the CAI patient group, but not in controls. Conclusion In patients with CAI, deficits in both quality of ankle arthrokinematic motion and postural control were present. Therefore, physical therapy interventions focused on improving ankle neuromuscular control and arthrokinematic function are necessary in CAI patient care. J Orthop Sports Phys Ther 2017;47(8):570-577. Epub 4 Nov 2016. doi:10.2519/jospt.2017.6836.

Wearable knee health system employing novel physiological biomarkers

Knee injuries and chronic disorders, such as arthritis, affect millions of Americans, leading to missed workdays and reduced quality of life. Currently, after an initial diagnosis, there are few quantitative technologies available to provide sensitive subclinical feedback to patients regarding improvements or setbacks to their knee health status; instead, most assessments are qualitative, relying on patient-reported symptoms, performance during functional tests, and physical examinations. Recent advances have been made with wearable technologies for assessing the health status of the knee (and potentially other joints) with the goal of facilitating personalized rehabilitation of injuries and care for chronic conditions. This review describes our progress in developing wearable sensing technologies that enable quantitative physiological measurements and interpretation of knee health status. Our sensing system enables longitudinal quantitative measurements of knee sounds, swelling, and activity context during clinical and field situations. Importantly, we leverage machine-learning algorithms to fuse the low-level signal and feature data of the measured time series waveforms into higher level metrics of joint health. This paper summarizes the engineering validation, baseline physiological experiments, and human subject studies-both cross-sectional and longitudinal-that demonstrate the efficacy of using such systems for robust knee joint health assessment. We envision our sensor system complementing and advancing present-day practices to reduce joint reinjury risk, to optimize rehabilitation recovery time for a quicker return to activity, and to reduce health care costs.

Quantification of knee vibroarthrographic signal irregularity associated with patellofemoral joint cartilage pathology based on entropy and envelope amplitude measures

BACKGROUND AND OBJECTIVE

Injury of knee joint cartilage may result in pathological vibrations between the articular surfaces during extension and flexion motions. The aim of this paper is to analyze and quantify vibroarthrographic (VAG) signal irregularity associated with articular cartilage degeneration and injury in the patellofemoral joint.

METHODS

The symbolic entropy (SyEn), approximate entropy (ApEn), fuzzy entropy (FuzzyEn), and the mean, standard deviation, and root-mean-squared (RMS) values of the envelope amplitude, were utilized to quantify the signal fluctuations associated with articular cartilage pathology of the patellofemoral joint. The quadratic discriminant analysis (QDA), generalized logistic regression analysis (GLRA), and support vector machine (SVM) methods were used to perform signal pattern classifications.

RESULTS

The experimental results showed that the patients with cartilage pathology (CP) possess larger SyEn and ApEn, but smaller FuzzyEn, over the statistical significance level of the Wilcoxon rank-sum test (p<0.01), than the healthy subjects (HS). The mean, standard deviation, and RMS values computed from the amplitude difference between the upper and lower signal envelopes are also consistently and significantly larger (p<0.01) for the group of CP patients than for the HS group. The SVM based on the entropy and envelope amplitude features can provide superior classification performance as compared with QDA and GLRA, with an overall accuracy of 0.8356, sensitivity of 0.9444, specificity of 0.8, Matthews correlation coefficient of 0.6599, and an area of 0.9212 under the receiver operating characteristic curve.

CONCLUSIONS

The SyEn, ApEn, and FuzzyEn features can provide useful information about pathological VAG signal irregularity based on different entropy metrics. The statistical parameters of signal envelope amplitude can be used to characterize the temporal fluctuations related to the cartilage pathology.

Age-related impairment of quality of joint motion in vibroarthrographic signal analysis

Aging is associated with degenerative changes in articular surfaces leading to quantitative and qualitative impairment of joint motion. Therefore, the aim of this study is to evaluate an age-related quality of the patellofemoral joint (PFJ) motion in the vibroarthrographic (VAG) signal analysis. Two hundred and twenty individuals were enrolled in this study and divided into five groups according to age. The VAG signals were collected during flexion/extension knee motion using an acceleration sensor and described using four parameters (VMS, P1, P2, and H). We observed that values of parameters VMS, P1, and P2 increase in accordance with the age, but H level decreases. The most significant differences were achieved between the youngest and the oldest participants' groups. Moreover, we show that parameters VMS, P1, and P2 positively correlate with age, contrary to negatively associated H parameter. Our results suggest that the impairment of joint motion is a result of age-related osteoarticular degenerative changes.

Joint motion quality in vibroacoustic signal analysis for patients with patellofemoral joint disorders

BACKGROUND

Chondromalacia, lateral patellar compression syndrome and osteoarthritis are common patellofemoral joint disorders leading to functional and/or structural disturbances in articular surfaces. The objective of the study was to evaluate their impact on joint motion quality via the vibroacoustic signal generated during joint movement analysis.

METHODS

Seventy-three patients (30 with chondromalacia, 21 with lateral patellar compression syndrome, and 22 with osteoarthritis) and 32 healthy controls were tested during flexion/extension knee motion for vibroacoustic signals using an acceleration sensor. Estimated parameters: variation of mean square (VMS), difference between mean of four maximum and mean of four minimum values (R4), power spectral density for frequency of 50-250 Hz (P1) and 250-450 Hz (P2) were analyzed.

RESULTS

Vibroacoustic signals recorded for particular disorders were characterized by significantly higher values of parameters in comparison to the control group. Moreover, differences were found among the various types of patellofemoral joint disturbances. Chondromalacia and osteoarthritis groups showed differences in all parameters examined. In addition, osteoarthritis patients exhibited differences in VMS, P1 and P2 values in comparison to lateral patellar compression syndrome patients. However, only the value of R4 was found to differ between knees with lateral patellar compression syndrome and those with chondromalacia.

CONCLUSION

Our results suggest that particular disorders are characterized by specific vibroacoustic patterns of waveforms as well as values of analyzed parameters.

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

High-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.

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.

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.

Statistics corner: A guide to appropriate use of correlation coefficient in medical research

Correlation is a statistical method used to assess a possible linear association between two continuous variables. It is simple both to calculate and to interpret. However, misuse of correlation is so common among researchers that some statisticians have wished that the method had never been devised at all. The aim of this article is to provide a guide to appropriate use of correlation in medical research and to highlight some misuse. Examples of the applications of the correlation coefficient have been provided using data from statistical simulations as well as real data. Rule of thumb for interpreting size of a correlation coefficient has been provided.

Statistical analysis of gait maturation in children based on probability density functions

Analysis of gait patterns in children is useful for the study of maturation of locomotor control. In this paper, we utilized the Parzen-window method to estimate the probability density functions (PDFs) of the stride interval for 50 children. With the estimated PDFs, the statistical measures, i.e., averaged stride interval (ASI), variation of stride interval (VSI), PDF skewness (SK), and PDF kurtosis (KU), were computed for the gait maturation in three age groups (aged 3-5 years, 6-8 years, and 10-14 years) of young children. The results indicated that the ASI and VSI values are significantly different between the three age groups. The VSI is decreased rapidly until 8 years of age, and then continues to be decreased at a slower rate. The SK values of the PDFs for all of the three age groups are positive, which shows a slight imbalance in the stride interval distribution within each age group. In addition, the decrease of the KU values of the PDFs is age-dependent, which suggests the effects of the musculo-skeletal growth on the gait maturation in young children.

ARTICULAR SOUND AND CLINICAL STAGES IN KNEE ARTHROPATHY

Objective: To clarify the pathophysiology of knee arthropathy, articular sound in the knee joint was recorded using an accelerometer, vibroarthrography (VAG), during standing-up and sitting-down movements in patients with osteoarthropathy (OA) of the knees. Methods: VAG signals and angular changes of the knee joint during standing-up and sitting-down movements were recorded in patients with OA, including 17 knees with OA at Kellgren–Lawrence stage I and II, 16 knees with OA at III and IV stages, and 20 knees of age-matched control subjects. Results: The level of VAG signals was greater in knees with a higher stage of OA at 50–99 and 100–149 Hz among the groups (ANOVA with Tukey–Kramer multiple comparisons test, p < 0.01). The VAG signals did not correlate with WOMAC-pain or physical scores. Conclusions: We considered that the increase in VAG signals in these ranges of frequency corresponded with pathological changes of OA, but not self-reported clinical symptoms. This method of VAG can be used by clinicians during interventions to obtain pathological information regarding structural changes of the knee joint.

Effective collaborative learning in biomedical education using a web-based infrastructure

This paper presents a feature-rich web-based system used for biomedical education at the undergraduate level. With the powerful groupware features provided by the wiki system, the instructors are able to establish a community-centered mentoring environment that capitalizes on local expertise to create a sense of online collaborative learning among students. The web-based infrastructure can help the instructors effectively organize and coordinate student research projects, and the groupware features may support the interactive activities, such as interpersonal communications and data sharing. The groupware features also provide the web-based system with a wide range of additional ways of organizing collaboratively developed materials, which makes it become an effective tool for online active learning. Students are able to learn the ability to work effectively in teams, with an improvement of project management, design collaboration, and technical writing skills. With the fruitful outcomes in recent years, it is positively thought that the web-based collaborative learning environment can perform an excellent shift away from the conventional instructor-centered teaching to community- centered collaborative learning in the undergraduate education.

Reconstruction of gastric slow wave from finger photoplethysmographic signal using radial basis function neural network

Extraction of extra-cardiac information from photoplethysmography (PPG) signal is a challenging research problem with significant clinical applications. In this study, radial basis function neural network (RBFNN) is used to reconstruct the gastric myoelectric activity (GMA) slow wave from finger PPG signal. Finger PPG and GMA (measured using Electrogastrogram, EGG) signals were acquired simultaneously at the sampling rate of 100 Hz from ten healthy subjects. Discrete wavelet transform (DWT) was used to extract slow wave (0-0.1953 Hz) component from the finger PPG signal; this slow wave PPG was used to reconstruct EGG. A RBFNN is trained on signals obtained from six subjects in both fasting and postprandial conditions. The trained network is tested on data obtained from the remaining four subjects. In the earlier study, we have shown the presence of GMA information in finger PPG signal using DWT and cross-correlation method. In this study, we explicitly reconstruct gastric slow wave from finger PPG signal by the proposed RBFNN-based method. It was found that the network-reconstructed slow wave provided significantly higher (P < 0.0001) correlation (≥ 0.9) with the subject's EGG slow wave than the correlation obtained (≈0.7) between the PPG slow wave from DWT and the EEG slow wave. Our results showed that a simple finger PPG signal can be used to reconstruct gastric slow wave using RBFNN method.

A PDF-based classification of gait cadence patterns in patients with amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a type of neurological disease due to the degeneration of motor neurons. During the course of such a progressive disease, it would be difficult for ALS patients to regulate normal locomotion, so that the gait stability becomes perturbed. This paper presents a pilot statistical study on the gait cadence (or stride interval) in ALS, based on the statistical analysis method. The probability density functions (PDFs) of stride interval were first estimated with the nonparametric Parzen-window method. We computed the mean of the left-foot stride interval and the modified Kullback-Leibler divergence (MKLD) from the PDFs estimated. The analysis results suggested that both of these two statistical parameters were significantly altered in ALS, and the least-squares support vector machine (LS-SVM) may effectively distinguish the stride patterns between the ALS patients and healthy controls, with an accurate rate of 82.8% and an area of 0.87 under the receiver operating characteristic curve.

An enhanced algorithm for knee joint sound classification using feature extraction based on time-frequency analysis

Vibroarthrographic (VAG) signals, generated by human knee movement, are non-stationary and multi-component in nature and their time-frequency distribution (TFD) provides a powerful means to analyze such signals. The objective of this paper is to improve the classification accuracy of the features, obtained from the TFD of normal and abnormal VAG signals, using segmentation by the dynamic time warping (DTW) and denoising algorithm by the singular value decomposition (SVD). VAG and knee angle signals, recorded simultaneously during one flexion and one extension of the knee, were segmented and normalized at 0.5 Hz by the DTW method. Also, the noise within the TFD of the segmented VAG signals was reduced by the SVD algorithm, and a back-propagation neural network (BPNN) was used to classify the normal and abnormal VAG signals. The characteristic parameters of VAG signals consist of the energy, energy spread, frequency and frequency spread parameter extracted by the TFD. A total of 1408 segments (normal 1031, abnormal 377) were used for training and evaluating the BPNN. As a result, the average classification accuracy was 91.4 (standard deviation +/-1.7) %. The proposed method showed good potential for the non-invasive diagnosis and monitoring of joint disorders such as osteoarthritis.

Analysis of vibroarthrographic signals with features related to signal variability and radial-basis functions

Knee-joint sounds or vibroarthrographic (VAG) signals contain diagnostic information related to the roughness, softening, breakdown, or the state of lubrication of the articular cartilage surfaces. Objective analysis of VAG signals provides features for pattern analysis, classification, and noninvasive diagnosis of knee-joint pathology of various types. We propose parameters related to signal variability for the analysis of VAG signals, including an adaptive turns count and the variance of the mean-squared value computed during extension, flexion, and a full swing cycle of the leg, for the purpose of classification as normal or abnormal, that is, screening. With a database of 89 VAG signals, screening efficiency of up to 0.8570 was achieved, in terms of the area under the receiver operating characteristics curve, using a neural network classifier based on radial-basis functions, with all of the six proposed features. Using techniques for feature selection, the turns counts for the flexion and extension parts of the VAG signals were chosen as the top two features, leading to an improved screening efficiency of 0.9174. The proposed methods could lead to objective criteria for improved selection of patients for clinical procedures and reduce healthcare costs.

Modeling and classification of knee-joint vibroarthrographic signals using probability density functions estimated with Parzen windows

Diagnostic information related to the articular cartilage surfaces of knee-joints may be derived from vibro-arthrographic (VAG) signals. Although several studies have proposed many different types of parameters for the analysis and classification of VAG signals, no statistical modeling methods have been explored to represent the fundamental distinctions between normal and abnormal VAG signals. In the present work, we derive models of probability density functions (PDFs), using the Parzen-window approach, to represent the basic statistical characteristics of normal and abnormal VAG signals. The Kullback-Leibler distance (KLD) is then computed between the PDF of the signal to be classified and the PDF models for normal and abnormal VAG signals. A classification accuracy of 73.03% was obtained with a database of 89 VAG signals. The screening efficiency was derived to be 0.6724, in terms of the area under the receiver operating characteristics curve.