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Noninvasive hemoglobin measurement using dynamic spectrum.

Xiaoqing YiGang LiLing Lin
Published in: The Review of scientific instruments (2018)
Spectroscopy methods for noninvasive hemoglobin (Hgb) measurement are interfered by individual difference and particular weak signal. In order to address these problems, we have put forward a series of improvement methods based on dynamic spectrum (DS), including instrument design, spectrum extraction algorithm, and modeling approach. The instrument adopts light sources composed of eight laser diodes with the wavelength range from 600 nm to 1100 nm and records photoplethysmography signals at eight wavelengths synchronously. In order to simplify the optical design, we modulate the light sources with orthogonal square waves and design the corresponding demodulation algorithm, instead of adopting a beam-splitting system. A newly designed algorithm named difference accumulation has been proved to be effective in improving the accuracy of dynamic spectrum extraction. 220 subjects are involved in the clinical experiment. An extreme learning machine calibration model between the DS data and the Hgb levels is established. Correlation coefficient and root-mean-square error of prediction sets are 0.8645 and 8.48 g/l, respectively. The results indicate that the Hgb level can be derived by this approach noninvasively with acceptable precision and accuracy. It is expected to achieve a clinic application in the future.
Keyphrases
  • deep learning
  • machine learning
  • light emitting
  • high resolution
  • photodynamic therapy
  • drinking water
  • mental health
  • primary care
  • climate change
  • heart rate
  • blood pressure
  • red blood cell
  • big data
  • monte carlo