Post-processing algorithm for removing soft-tissue movement artifacts from vibroarthrographic knee-joint signal.

Area of Science:

• Biomechanics
• Biomedical Engineering
• Signal Processing

Background:

• Vibroarthrographic (VAG) signals, derived from knee joint acoustics, offer noninvasive assessment.
• Sensor movement with soft tissues during joint motion introduces soft tissue movement artifacts (STMAs), causing signal baseline drift.
• Accurate VAG signal analysis is crucial for effective clinical applications.

Purpose of the Study:

• To develop and evaluate a novel algorithm for filtering soft tissue movement artifacts (STMAs) from vibroarthrographic (VAG) signals.
• To quantify the noise reduction effectiveness of the proposed STMA filtering method.
• To enhance the clinical utility of VAG signals by improving their accuracy.

Main Methods:

• Development of a post-processing algorithm to identify and remove STMAs from VAG signals.
• Collection of VAG signals and simultaneous noise data using accelerometers.
• Comparison of signal-to-noise ratios before and after applying the STMA filtering algorithm.

Main Results:

• The proposed algorithm effectively filters soft tissue movement artifacts (STMAs) from VAG signals.
• An 11.85% increase in peak signal-to-noise ratio was observed after artifact removal.
• A 28.18% increase in overall signal-to-noise ratio was achieved compared to unfiltered signals.

Conclusions:

• The developed post-processing method successfully removes STMAs, mitigating baseline wander in VAG signals.
• This noise reduction technique significantly enhances the quality and reliability of VAG data.
• The improved accuracy of VAG signals holds promise for advancing clinical diagnosis and monitoring of knee joint conditions.