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Performance improved method for subtracted blood volume spectrometry using empirical mode decomposition.

Hongzhi Gao1, Qipeng Lu, Haiquan Ding

  • 1State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.

Bio-Medical Materials and Engineering
|November 12, 2013
PubMed
Summary

Empirical mode decomposition (EMD) effectively reduces noise in subtracted blood volume spectrometry (SBVS), a near-infrared spectroscopy technique. This noise reduction significantly improves the accuracy and reliability of biochemical sensing calibration models.

Keywords:
Near-infrared spectroscopyempirical mode decompositionnoninvasive biochemical sensingsubtracted blood volume spectrometry

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Area of Science:

  • Biomedical Engineering
  • Spectroscopy
  • Signal Processing

Background:

  • Subtracted blood volume spectrometry (SBVS) is a near-infrared spectroscopy (NIRS) method for noninvasive biochemical sensing.
  • SBVS effectively removes background information but introduces significant noise, hindering calibration model performance.

Purpose of the Study:

  • To investigate the application of Empirical Mode Decomposition (EMD) for noise reduction in SBVS.
  • To enhance the performance of SBVS calibration models by mitigating spectral noise.

Main Methods:

  • Empirical Mode Decomposition (EMD) was applied to filter noise from subtracted spectra obtained via SBVS.
  • Performance evaluation used criteria including average correlation coefficient and Euclidean distance metrics (average and standard deviation).
  • EMD filtering was tested on spectra with varying differential pathlength factor (ΔL) values.

Main Results:

  • EMD filtering demonstrably improved all evaluated performance criteria for SBVS spectra.
  • For spectra with ΔL=0.5mm, the correlation coefficient increased from 0.9970 to 0.9999.
  • EMD processing led to decreased average Euclidean distance (0.0265 to 0.0118) and standard deviation (0.0148 to 0.0033), also improving Partial Least Squares (PLS) models.

Conclusions:

  • Empirical Mode Decomposition is a highly effective method for noise reduction in SBVS.
  • EMD significantly enhances the accuracy and reliability of NIRS-based biochemical sensing using SBVS.
  • This technique shows promise for improving the overall performance of SBVS in various applications.