Classification of cycling exercise status using short-term heart rate variability

In Cheol Jeong, Joseph Finkelstein

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

Abstract

Introduction of effective home-based exercise programs in older adults and people with chronic conditions requires implementation of appropriate safeguards to prevent possible side effects of strenuous exercise. In each exercise program the following exercise modes can be generally recognized: rest, main exercise, and exercise recovery. However, approaches for automated identification of these exercise modes have not been studied systematically. The primary purpose of this study was (1) to assess whether time-domain HRV parameters differ depending on exercise mode; (2) to identify optimal set of time-domain parameters for automated classification of exercise mode and build a classification model. Using discriminant analysis, two HRV parameters (RRtri and MeanRR) were identified which yielded 80% classification success in identifying correct exercise mode by applying generated discriminant functions.

Original languageEnglish (US)
Title of host publication2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1782-1785
Number of pages4
ISBN (Print)9781424479290
DOIs
StatePublished - Nov 2 2014
Event2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 - Chicago, United States
Duration: Aug 26 2014Aug 30 2014

Other

Other2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
CountryUnited States
CityChicago
Period8/26/148/30/14

Fingerprint

Heart Rate
Discriminant Analysis
Discriminant analysis
Recovery

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications
  • Biomedical Engineering

Cite this

Jeong, I. C., & Finkelstein, J. (2014). Classification of cycling exercise status using short-term heart rate variability. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 (pp. 1782-1785). [6943954] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2014.6943954

Classification of cycling exercise status using short-term heart rate variability. / Jeong, In Cheol; Finkelstein, Joseph.

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 1782-1785 6943954.

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

Jeong, IC & Finkelstein, J 2014, Classification of cycling exercise status using short-term heart rate variability. in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014., 6943954, Institute of Electrical and Electronics Engineers Inc., pp. 1782-1785, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, Chicago, United States, 8/26/14. https://doi.org/10.1109/EMBC.2014.6943954
Jeong IC, Finkelstein J. Classification of cycling exercise status using short-term heart rate variability. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 1782-1785. 6943954 https://doi.org/10.1109/EMBC.2014.6943954
Jeong, In Cheol ; Finkelstein, Joseph. / Classification of cycling exercise status using short-term heart rate variability. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 1782-1785
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