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Correcting waveform bias using principal component analysis: Applications in multicentre motion analysis studies.

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Area of Science:

  • Biomechanics
  • Statistical analysis
  • Medical imaging

Background:

  • Multicentre three-dimensional motion analyses are challenging due to waveform data inconsistencies between sites.
  • Principal component analysis (PCA) is a statistical method for quantifying waveform variability and group differences.

Purpose of the Study:

  • To propose and validate a PCA-based correction technique for multicentre motion analysis data.
  • To address nuisance variations arising from combining waveform data from different centers.

Main Methods:

  • A post-processing correction technique utilizing PCA was developed to remove inter-centre bias in waveform data.
  • The method was tested on synthesized gait kinematic crosstalk and real-world knee arthroplasty patient data from two centers.

Main Results:

  • The PCA-based correction successfully removed induced crosstalk from knee joint angle data.
  • Significant differences in implant types were identified in knee arthroplasty data after removing multicentre variation.
  • The technique corrected waveform bias, ensuring dataset means agreed without information loss.

Conclusions:

  • The proposed PCA-based technique effectively corrects for differences between waveform datasets in post-processing.
  • This method has the potential to facilitate multicentre motion analysis studies, overcoming current data integration challenges.