Identifying Optimal Features from Heart Rate Variability for Early Detection of Sepsis in Pediatric Intensive Care

P. Amiri, A. Derakhshan, B. Gharib, Y.H. Liu, M. Mirzaaghayan

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Sepsis as bacterial infection is the most common and costly causes of mortality in critically ill patients. The early diagnosis of sepsis is significantly important for effective treatment. In this study, over a period of two years, the electrocardiogram of nearly 500 pediatric and neonate patients with heart diseases were collected in 24 hours before diagnosis. The collected data of 22 patients were studied including 11 sepsis patients with positive blood cultures and 11 non-sepsis patients. After extracting the HRV (Heart Rate Variability) signal, 28 linear and nonlinear features according to previous research were extracted. By using the relative entropy method as a feature selection technique, the extracted features were evaluated for their ability to discriminate the data in sepsis and non-sepsis groups, and the best features were entered into the classification process. Using the four classification models of SVM, LDA, KNN and Decision Tree, the accuracy of 86.36% was obtained with Decision Tree for discrimination of sepsis patients from other patients.

Original languageEnglish
Title of host publicationProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
PublisherIEEE
Publication dateJul 2019
Pages1425-1428
ISBN (Print)978-1-5386-1312-2
ISBN (Electronic)978-1-5386-1311-5
DOIs
Publication statusPublished - Jul 2019
Externally publishedYes
Event41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Berlin, Germany
Duration: 23. Jul 201927. Jul 2019

Conference

Conference41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Country/TerritoryGermany
CityBerlin
Period23/07/201927/07/2019
SeriesI E E E Engineering in Medicine and Biology Society. Conference Proceedings
ISSN2375-7477

Keywords

  • Child
  • Critical Care
  • Early Diagnosis
  • Electrocardiography
  • Heart Rate
  • Humans
  • Infant, Newborn
  • Sepsis/diagnosis

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