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

In Cheol Jeong; Joseph Finkelstein

doi:10.3233/978-1-61499-538-8-171

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

In Cheol Jeong, Joseph Finkelstein

School of Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

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 language	English (US)
Title of host publication	Enabling Health Informatics Applications
Editors	John Mantas, Mowafa S. Househ, Arie Hasman
Publisher	IOS Press
Pages	171-174
Number of pages	4
ISBN (Electronic)	9781614995371
DOIs	https://doi.org/10.3233/978-1-61499-538-8-171
State	Published - Jan 1 2015
Event	13th International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2015 - Athens, Greece Duration: Jul 9 2015 → Jul 11 2015

Publication series

Name	Studies in Health Technology and Informatics
Volume	213
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Other

Other	13th International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2015
Country/Territory	Greece
City	Athens
Period	7/9/15 → 7/11/15

Keywords

Discriminant analysis
Exercise
Heart rate variability

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics
Health Information Management

Access to Document

10.3233/978-1-61499-538-8-171

Cite this

Real-time classification of exercise exertion levels using discriminant analysis of HRV l. / Jeong, In Cheol; Finkelstein, Joseph.
Enabling Health Informatics Applications. ed. / John Mantas; Mowafa S. Househ; Arie Hasman. IOS Press, 2015. p. 171-174 (Studies in Health Technology and Informatics; Vol. 213).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Jeong, IC & Finkelstein, J 2015, Real-time classification of exercise exertion levels using discriminant analysis of HRV l. in J Mantas, MS Househ & A Hasman (eds), Enabling Health Informatics Applications. Studies in Health Technology and Informatics, vol. 213, IOS Press, pp. 171-174, 13th International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2015, Athens, Greece, 7/9/15. https://doi.org/10.3233/978-1-61499-538-8-171

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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.",

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AB - 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.

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

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Keywords

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