Machine Learning Methods in the Classification of the Athletes Dehydration

Antonio Alvarez, Erika Severeyn, Jesus Velasquez, Sara Wong, Gilberto Perpinan, Monica Huerta

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Regular physical activity brings improvement in health and quality of life. It is associated with reduced risk of several diseases such as cardiovascular disease and type 2 diabetes. Despite all the benefits associated with regular exercise, there are some problems that athletes face up, one of which is dehydration during and after an intense session. Studies have shown that during a protocol of dehydration of three stages, rest stage before the exercise (RE), post-exercise (PE) and after hydration (PH), there are alterations in the electrocardiographic signal observed from the heart rate variability (HRV) parameters (RR-interval, SDRR, and RMSSD). Support vector machine (SVM) and k-means have been used in bioengineering for pathologies detection and classification. The aim of this research is to evaluate the classification capability of time domain HRV parameters in the detection of dehydration in a population of athletes using SVM and k-means. A three-stage dehydration protocol was implemented (RE, PE, and PH) in a database of 16 athletes, in each stage of the protocol 10 minutes electrocardiographic signal acquisition was performed, and the RR-interval, RMSSD, and SDRR were calculated. The results obtained in this work suggest that the SVM method classifies more efficiently the stages of the dehydration protocol than k-means clustering. On the other hand, the variable that best categorized the dehydration stages was the RR-interval obtained with the VMS method with accuracy, precision and recall above 0.60. The findings of this research encourage the hypothesis that dehydration could be studied from the electrocardiographic signal.

Original languageEnglish
Title of host publication2019 IEEE 4th Ecuador Technical Chapters Meeting, ETCM 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728137643
DOIs
StatePublished - Nov 2019
Event4th IEEE Ecuador Technical Chapters Meeting, ETCM 2019 - Guayaquil, Ecuador
Duration: 13 Nov 201915 Nov 2019

Publication series

Name2019 IEEE 4th Ecuador Technical Chapters Meeting, ETCM 2019

Conference

Conference4th IEEE Ecuador Technical Chapters Meeting, ETCM 2019
CountryEcuador
CityGuayaquil
Period13/11/1915/11/19

Bibliographical note

Funding Information:
This work was funded by the Research and Development Deanery of Salesian Polytechnic University and the Research and Development Deanery of the Simon Bolivar University (DID)

Funding Information:
ACKNOWLEDGMENT This work was funded by the Research and Development Deanery of Salesian Polytechnic University and the Research and Development Deanery of the Simón Bolívar University (DID).

Publisher Copyright:
© 2019 IEEE.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • dehydration
  • heart rate variability
  • k-means
  • physical activity
  • support vector machine

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