Improving Accuracy of Noninvasive Hemoglobin Monitors: A Functional Regression Model for Streaming SpHb Data

Devashish Das, Kalyan S. Pasupathy, Nadeem N. Haddad, M. Susan Hallbeck, Martin D. Zielinski, Mustafa Y. Sir

Research output: Contribution to journalArticle

Abstract

Objective: The purpose of this paper is to develop a method for improving the accuracy of SpHb monitors, which are noninvasive hemoglobin monitoring tools, leading to better critical care protocols in trauma care. Methods: The proposed method is based on fitting smooth spline functions to SpHb measurements collected over a time window and then using a functional regression model to predict the true HgB value for the end of the time window. Results: The accuracy of the proposed method is compared to traditional methods. The mean absolute error between the raw SpHb measurements and the gold standard hemoglobin measurements was 1.26 g/Dl. The proposed method reduced the mean absolute error to 1.08 g/Dl. [1] Conclusion: Fitting a smooth function to SpHb measurements improves the accuracy of Hgb predictions. Significance: Accurate prediction of current and future HgB levels can lead to sophisticated decision models that determine the optimal timing and amount of blood product transfusions.

Original languageEnglish (US)
Article number8410784
Pages (from-to)759-767
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Volume66
Issue number3
DOIs
StatePublished - Mar 2019

Keywords

  • Functional regression
  • improving accuracy
  • noninvasive hemoglobin monitors

ASJC Scopus subject areas

  • Biomedical Engineering

Fingerprint Dive into the research topics of 'Improving Accuracy of Noninvasive Hemoglobin Monitors: A Functional Regression Model for Streaming SpHb Data'. Together they form a unique fingerprint.

  • Cite this