Database Proposal for Correlation of Glucose and Photoplethysmography Signals

Christian Salamea, Erick Narvaez, Melisa Montalvo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

This work presents a Database that contains Photoplethysmography signals, glucose levels, weight, height and age of 217 patients. The information of biologic activity was obtained using the handle Empatica E4 Wristband, the glucose level using laboratory blood chemistry analyzers (Cobas 6000), and the physical parameters using standardized instruments. The database comprises a forward training a total of 5576 samples and another segment of validation to a total of 2164 samples. The Database has been used to evaluate different prediction techniques based on Machine Learning (Random Forest, Artificial Neural Network, Support Vector Machine, Gradient Boosting Machine). The implementation of these algorithms provides up to 90% average accuracy, a correlation of 0.88 and a satisfactory evaluation in the Error Diagram of Clarke. According to the results obtained, the proposed database is appropriate for training and verification of existing correlation between photoplethysmography signals and blood glucose level.

Original languageEnglish
Title of host publicationAdvances in Emerging Trends and Technologies - Volume 2
EditorsMiguel Botto-Tobar, Joffre León-Acurio, Angela Díaz Cadena, Práxedes Montiel Díaz
PublisherSpringer Verlag
Pages44-53
Number of pages10
ISBN (Print)9783030320324
DOIs
StatePublished - 1 Jan 2020
Event1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019 - quito, Ecuador
Duration: 29 May 201931 May 2019

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1067
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019
Country/TerritoryEcuador
Cityquito
Period29/05/1931/05/19

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

  • Database
  • Glucose
  • Machine Learning
  • MFCCs
  • PPG

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