A Novel Accelerometer Mounting Method for Sensing Performance Improvement in Acoustic Measurements From the Knee

Non-Invasive Assessment of Cartilage Damage of the Human Knee Using Acoustic Emission Monitoring: A Pilot Cadaver Study

OBJECTIVE

Knee osteoarthritis is currently one of the top causes of disability in older population, a rate that will only increase in the future due to an aging population and the prevalence of obesity. However, objective assessment of treatment outcomes and remote evaluation are still in need of further development. Acoustic emission (AE) monitoring in knee diagnostics has been successfully adopted in the past; however, a wide discrepancy among the adopted AE techniques and analyses exists. This pilot study determined the most suitable metrics to differentiate progressive cartilage damage and the optimal frequency range and placement of AE sensors.

METHODS

Knee AEs were recorded in the 100-450 kHz and 15-200kH frequency ranges from a cadaver specimen in knee flexion/extension. Four stages of artificially inflicted cartilage damage and two sensor positions were investigated.

RESULTS

AE events in the lower frequency range and the following parameters provided better distinction between intact and damaged knee: hit amplitude, signal strength, and absolute energy. The medial condyle area of the knee was less prone to artefacts and unsystematic noise. Multiple reopenings of the knee compartment in the process of introducing the damage negatively affected the quality of the measurements.

CONCLUSION

Results may improve AE recording techniques in future cadaveric and clinical studies.

SIGNIFICANCE

This was the first study to evaluate progressive cartilage damage using AEs in a cadaver specimen. The findings of this study encourage further investigation of joint AE monitoring techniques.

A Wearable Multimodal Sensing System for Tracking Changes in Pulmonary Fluid Status, Lung Sounds, and Respiratory Markers

Heart failure (HF) exacerbations, characterized by pulmonary congestion and breathlessness, require frequent hospitalizations, often resulting in poor outcomes. Current methods for tracking lung fluid and respiratory distress are unable to produce continuous, holistic measures of cardiopulmonary health. We present a multimodal sensing system that captures bioimpedance spectroscopy (BIS), multi-channel lung sounds from four contact microphones, multi-frequency impedance pneumography (IP), temperature, and kinematics to track changes in cardiopulmonary status. We first validated the system on healthy subjects (n = 10) and then conducted a feasibility study on patients (n = 14) with HF in clinical settings. Three measurements were taken throughout the course of hospitalization, and parameters relevant to lung fluid status—the ratio of the resistances at 5 kHz to those at 150 kHz (K)—and respiratory timings (e.g., respiratory rate) were extracted. We found a statistically significant increase in K (p < 0.05) from admission to discharge and observed respiratory timings in physiologically plausible ranges. The IP-derived respiratory signals and lung sounds were sensitive enough to detect abnormal respiratory patterns (Cheyne–Stokes) and inspiratory crackles from patient recordings, respectively. We demonstrated that the proposed system is suitable for detecting changes in pulmonary fluid status and capturing high-quality respiratory signals and lung sounds in a clinical setting.

Motion Artifact Resistant Mounting of Acoustic Emission Sensors for Knee Joint Monitoring

Among the many diverse methods of recording biological signals, sound and acoustic emission monitoring are becoming popular for data acquisition; however, these sensors tend to be very susceptible to motion artefacts and noise. In the case of joint monitoring, this issue is even more significant, considering that joint sounds are recorded during limb movements to establish joint health and performance. This paper investigates different sensor attachment methods for acoustic emission monitoring of the knee, which could lead to reduced motion and skin movement artefacts and improve the quality of sensory data sets. As a proof-of-concept study, several methods were tested over a range of exercises to evaluate noise resistance and signal quality. The signals least affected by motion artefacts were recorded when using high-density ethylene-vinyl acetate (EVA) foam holders, attached to the skin with double-sided biocompatible adhesive tape. Securing and isolating the connecting cable with foam is also recommended to avoid noise due to the cable movement.Clinical Relevance- The results of this study will be useful in joint AE monitoring, as well as in other methods of body sound recording that involve the mounting of relatively heavy sensors, such as phonocardiography and respiratory monitoring.

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.