Comparative utility of time and frequency HRV domains for automated classification of exercise exertion levels

In Cheol Jeong, Joseph Finkelstein

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

Abstract

Exercise exertion results in activation of sympathetic nervous system. Heart rate variability (HRV) has been used to analyze activity of sympathetic nervous system (ANS). However, approaches to use HRV for exercise exertion analysis were not explored systematically. The main goal of this study was to develop classification algorithms to determine level of exercise exertion in real time and to compare potential of HRV time domain parameters versus HRV frequency domain parameters versus combined time and frequency parameter set. Discriminant analysis was used to identify optimal parameter sets and to develop algorithms for classification of exercise exertion levels. Time-domain HRV parameters demonstrated higher classification accuracy (95.6%) as compared to frequency-domain parameters (82.2%). Combing HRV parameters from time and frequency domains improves classification accuracy (97.8%). Our results suggested that HRV analysis can be used to automatically classify exercise exertion levels. Future studies should focus on more granular approach in identifying different stages of exercise process. Evaluation of classification algorithms should be based on larger sample of diverse representatives of different age, sex and health condition groups.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages983-989
Number of pages7
ISBN (Print)9781467367981
DOIs
StatePublished - Dec 16 2015
EventIEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 - Washington, United States
Duration: Nov 9 2015Nov 12 2015

Other

OtherIEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
CountryUnited States
CityWashington
Period11/9/1511/12/15

Fingerprint

Heart Rate
Sympathetic Nervous System
Neurology
Discriminant Analysis
Discriminant analysis
Chemical activation
Health

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Health Informatics
  • Biomedical Engineering

Cite this

Jeong, I. C., & Finkelstein, J. (2015). Comparative utility of time and frequency HRV domains for automated classification of exercise exertion levels. In Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 (pp. 983-989). [7359817] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2015.7359817

Comparative utility of time and frequency HRV domains for automated classification of exercise exertion levels. / Jeong, In Cheol; Finkelstein, Joseph.

Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 983-989 7359817.

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

Jeong, IC & Finkelstein, J 2015, Comparative utility of time and frequency HRV domains for automated classification of exercise exertion levels. in Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015., 7359817, Institute of Electrical and Electronics Engineers Inc., pp. 983-989, IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015, Washington, United States, 11/9/15. https://doi.org/10.1109/BIBM.2015.7359817
Jeong IC, Finkelstein J. Comparative utility of time and frequency HRV domains for automated classification of exercise exertion levels. In Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 983-989. 7359817 https://doi.org/10.1109/BIBM.2015.7359817
Jeong, In Cheol ; Finkelstein, Joseph. / Comparative utility of time and frequency HRV domains for automated classification of exercise exertion levels. Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 983-989
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