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

Kristine L Richardson1, Sevda Gharehbaghi1, Goktug C Ozmen1

  • 1School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA.

IEEE Sensors Journal
|October 18, 2021
PubMed
Summary
This summary is machine-generated.

A new method accurately quantifies knee joint acoustic emissions (JAEs) signal quality. This technique improves distinguishing healthy knees from those with meniscus tears by removing artifacts, aiding joint health assessment.

Keywords:
acoustic emissionsjoint health scoresignal processingsignal qualityspectral distance

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Area of Science:

  • Biomechanics and Biomedical Engineering
  • Signal Processing
  • Musculoskeletal Health

Background:

  • Joint acoustic emissions (JAEs) offer insights into knee joint health during movement.
  • Current methods struggle with signal quality due to artifacts, hindering accurate analysis.
  • Distinguishing between healthy and injured knee JAEs requires robust signal processing.

Purpose of the Study:

  • To develop and validate a novel method for quantifying JAEs signal quality.
  • To improve the accuracy of classifying knee joint health using JAEs.
  • To address the challenge of artifacts in JAEs recordings.

Main Methods:

  • Recorded JAEs from healthy and meniscus-torn knees during flexion/extension (F/E).
  • Segmented JAEs by F/E cycle and extracted time/frequency domain features.
  • Constructed k-nearest neighbor graphs and applied spectral embedding for community structure analysis.
  • Developed an artifact removal technique based on distance from clean templates.

Main Results:

  • JAEs community structure was affected by artifacts, obscuring differences between healthy and injured knees initially.
  • Artifact removal significantly improved the distinction between healthy and injured knee JAEs.
  • The Graph Community Factor (GCF) was significantly higher in the meniscus tear group post-artifact removal (p<0.01).

Conclusions:

  • The proposed artifact removal method enhances the reliability of JAEs analysis for knee health assessment.
  • This technique is crucial for accurate JAEs interpretation in clinical and remote monitoring settings.
  • Improved classification of knee joint health, particularly identifying meniscus tears, is achievable with this method.