Multisensors information fusion with neural networks for noninvasive blood glucose detection

  • Authors:

  • Wei Wang;Lanfeng Yan;Baowei Liu;Heng Zhang

  • Affiliations:

  • School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, China;People's Hospital of GanSu Province, Lanzhou, Gansu, China;School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, China;School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, China

  • Venue:
  • ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
  • Year:
  • 2005

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Abstract

A multisensors information fusion model (MIFM) based on the Mixture of Experts (ME) neural networks was designed to fuse the multi-sensors signals for infrared noninvasive blood glucose detection. ME algorithm greatly improved the precision of noninvasive blood glucose measurement with multisensors. The principle of ME, design and implementation of MIFM were described in details. The standard deviation of the error of predication (SO) was 0.88 mmol/l from blood and 0.65 mmol/l from water-glucose. The correlation coefficient (CC) to training data from blood analysis was 0. 9.