Patients classification on weaning trials using Neural Networks and Wavelet Transform

Carlos Arizmendi, Juan Viviescas, Hernando GonzÁlez, Beatriz Giraldo

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

1 Scopus citations

Abstract

The determination of the optimal time of the patients in weaning trial process from mechanical ventilation, between patients capable of maintaining spontaneous breathing and patients that fail to maintain spontaneous breathing, is a very important task in intensive care unit. Wavelet Transform (WT) and Neural Networks (NN) techniques were applied in order to develop a classifier for the study of patients on weaning trial process. The respiratory pattern of each patient was characterized through different time series. Genetic Algorithms (GA) and Forward Selection were used as feature selection techniques. A classification performance of 77.00.. 0.06% of well classified patients, was obtained using a NN and GA combination, with only 6 variables of the 14 initials.

Original languageEnglish
Title of host publicationIntegrating Information Technology and Management for Quality of Care
PublisherIOS Press
Pages107-110
Number of pages4
ISBN (Print)9781614994220
DOIs
StatePublished - 2014
Event12th International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2014 - Athens, Greece
Duration: 10 Jul 201413 Jul 2014

Publication series

NameStudies in Health Technology and Informatics
Volume202
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Conference

Conference12th International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2014
Country/TerritoryGreece
CityAthens
Period10/07/1413/07/14

Keywords

  • Discrete Wavelet Transform
  • Genetic Algorithms
  • Neural Networks
  • Weaning process

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