Real-time classification of exercise exertion levels using discriminant analysis of HRV l

In Cheol Jeong, Joseph Finkelstein

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Heart rate variability (HRV) was shown to reflect activation of sympathetic nervous system however it is not clear which set of HRV parameters is optimal for real-time classification of exercise exertion levels. There is no studies that compared potential of two types of HRV parameters (time-domain and frequency-domain) in predicting exercise exertion level using discriminant analysis. The main goal of this study was to compare potential of HRV time-domain parameters versus HRV frequency-domain parameters in classifying exercise exertion level. Rest, exercise, and recovery categories were used in classification models. Overall 79.5% classification agreement by the time-domain parameters as compared to overall 52.8% classification agreement by frequency-domain parameters demonstrated that the time-domain parameters had higher potential in classifying exercise exertion levels.

Original languageEnglish (US)
Title of host publicationEnabling Health Informatics Applications
EditorsJohn Mantas, Mowafa S. Househ, Arie Hasman
PublisherIOS Press
Pages171-174
Number of pages4
ISBN (Electronic)9781614995371
DOIs
StatePublished - Jan 1 2015
Event13th International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2015 - Athens, Greece
Duration: Jul 9 2015Jul 11 2015

Publication series

NameStudies in Health Technology and Informatics
Volume213
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Other

Other13th International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2015
CountryGreece
CityAthens
Period7/9/157/11/15

Keywords

  • Discriminant analysis
  • Exercise
  • Heart rate variability

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

Fingerprint Dive into the research topics of 'Real-time classification of exercise exertion levels using discriminant analysis of HRV l'. Together they form a unique fingerprint.

Cite this