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

Knee acoustic emissions as a noninvasive biomarker of articular health in patients with juvenile idiopathic arthritis: a clinical validation in an extended study population

BACKGROUND

Joint acoustic emissions from knees have been evaluated as a convenient, non-invasive digital biomarker of inflammatory knee involvement in a small cohort of children with Juvenile Idiopathic Arthritis (JIA). The objective of the present study was to validate this in a larger cohort.

FINDINGS

A total of 116 subjects (86 JIA and 30 healthy controls) participated in this study. Of the 86 subjects with JIA, 43 subjects had active knee involvement at the time of study. Joint acoustic emissions were bilaterally recorded, and corresponding signal features were used to train a machine learning algorithm (XGBoost) to classify JIA and healthy knees. All active JIA knees and 80% of the controls were used as training data set, while the remaining knees were used as testing data set. Leave-one-leg-out cross-validation was used for validation on the training data set. Validation on the training and testing set of the classifier resulted in an accuracy of 81.1% and 87.7% respectively. Sensitivity / specificity for the training and testing validation was 88.6% / 72.3% and 88.1% / 83.3%, respectively. The area under the curve of the receiver operating characteristic curve was 0.81 for the developed classifier. The distributions of the joint scores of the active and inactive knees were significantly different.

CONCLUSION

Joint acoustic emissions can serve as an inexpensive and easy-to-use digital biomarker to distinguish JIA from healthy controls. Utilizing serial joint acoustic emission recordings can potentially help monitor disease activity in JIA affected joints to enable timely changes in therapy.

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.