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Weighted LS-SVM for function estimation applied to artifact removal in bio-signal processing.

Alexander Caicedo1, Sabine Van Huffel

  • 1Department of Electronic Engineering ESATSCD, Katholieke Universiteit Leuven, Belgium. alexander.caicedodorado@esat.kuleuven.be

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 25, 2010
PubMed
Summary

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This study introduces a novel weighted Least Squares Support Vector Machine (LS-SVM) method to improve function estimation from corrupted data. The new technique effectively reduces outlier impact and addresses border discontinuities in large time series, enhancing biomedical signal artifact removal.

Area of Science:

  • Machine Learning
  • Signal Processing
  • Biomedical Engineering

Background:

  • Weighted Least Squares Support Vector Machines (LS-SVM) are used for function estimation with corrupted data.
  • Existing LS-SVM methods struggle with large time series due to segmentation issues and border discontinuities.
  • Current solutions like committee or multilayer networks increase computational cost.

Purpose of the Study:

  • To propose a novel technique for LS-SVM that overcomes limitations in handling large, corrupted time series.
  • To reduce the impact of outliers and address border discontinuities without increasing computational complexity.
  • To apply the improved LS-SVM method for artifact removal in biomedical signals.

Main Methods:

  • A modified LS-SVM formulation incorporating an additional weight vector into the cost function.

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  • The proposed method integrates outlier weighting and discontinuity handling within a single framework.
  • Application of the technique to process and denoise biomedical signals.

    • Main Results:

    • The novel LS-SVM approach effectively handles large, corrupted time series data.
    • Border discontinuities are managed, leading to a more accurate final estimated function.
    • Successful removal of artifacts from biomedical signals was demonstrated.

    Conclusions:

    • The proposed LS-SVM technique offers an efficient alternative for function estimation from corrupted time series.
    • This method provides a computationally feasible solution for handling large datasets and signal artifacts.
    • The approach shows promise for improving the quality of biomedical signal analysis.