Model Based on Support Vector Machine for the Estimation of the Heart Rate Variability

Catalina Maria Hernández-Ruiz; Sergio Andrés Villagrán Martínez; Johan Enrique Ortiz Guzmán; Paulo Alonso Gaona Garcia

doi:10.1007/978-3-030-01421-6_19

Model Based on Support Vector Machine for the Estimation of the Heart Rate Variability

Catalina Maria Hernández-Ruiz, Sergio Andrés Villagrán Martínez, Johan Enrique Ortiz Guzmán, Paulo Alonso Gaona Garcia

Research output: Chapter in Book/Report › Conference contribution

4 Scopus citations

Abstract

This paper shows the design, implementation and analysis of a Machine Learning (ML) model for the estimation of Heart Rate Variability (HRV). Through the integration of devices and technologies of the Internet of Things, a support tool is proposed for people in health and sports areas who need to know an individual’s HRV. The cardiac signals of the subjects were captured through pectoral bands, later they were classified by a Support Vector Machine algorithm that determined if the HRV is depressed or increased. The proposed solution has an efficiency of 90.3% and it’s the initial component for the development of an application oriented to physical training that suggests exercise routines based on the HRV of the individual.

Original language	English (US)
Title of host publication	Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings
Editors	Yannis Manolopoulos, Barbara Hammer, Ilias Maglogiannis, Vera Kurkova, Lazaros Iliadis
Publisher	Springer
Pages	186-194
Number of pages	9
ISBN (Print)	9783030014209
DOIs	https://doi.org/10.1007/978-3-030-01421-6_19
State	Published - Jan 1 2018
Event	27th International Conference on Artificial Neural Networks, ICANN 2018 - Rhodes, Greece Duration: Oct 4 2018 → Oct 7 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11140 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	27th International Conference on Artificial Neural Networks, ICANN 2018
Country/Territory	Greece
City	Rhodes
Period	10/4/18 → 10/7/18

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-01421-6_19

Cite this

Hernández-Ruiz, C. M., Villagrán Martínez, S. A., Ortiz Guzmán, J. E., & Gaona Garcia, P. A. (2018). Model Based on Support Vector Machine for the Estimation of the Heart Rate Variability. In Y. Manolopoulos, B. Hammer, I. Maglogiannis, V. Kurkova, & L. Iliadis (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings (pp. 186-194). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11140 LNCS). Springer. https://doi.org/10.1007/978-3-030-01421-6_19

Hernández-Ruiz, Catalina Maria ; Villagrán Martínez, Sergio Andrés ; Ortiz Guzmán, Johan Enrique et al. / Model Based on Support Vector Machine for the Estimation of the Heart Rate Variability. Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings. editor / Yannis Manolopoulos ; Barbara Hammer ; Ilias Maglogiannis ; Vera Kurkova ; Lazaros Iliadis. Springer, 2018. pp. 186-194 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{e426e84c667e408c907c01639c4dd5da,

title = "Model Based on Support Vector Machine for the Estimation of the Heart Rate Variability",

abstract = "This paper shows the design, implementation and analysis of a Machine Learning (ML) model for the estimation of Heart Rate Variability (HRV). Through the integration of devices and technologies of the Internet of Things, a support tool is proposed for people in health and sports areas who need to know an individual{\textquoteright}s HRV. The cardiac signals of the subjects were captured through pectoral bands, later they were classified by a Support Vector Machine algorithm that determined if the HRV is depressed or increased. The proposed solution has an efficiency of 90.3% and it{\textquoteright}s the initial component for the development of an application oriented to physical training that suggests exercise routines based on the HRV of the individual.",

author = "Hern{\'a}ndez-Ruiz, {Catalina Maria} and {Villagr{\'a}n Mart{\'i}nez}, {Sergio Andr{\'e}s} and {Ortiz Guzm{\'a}n}, {Johan Enrique} and {Gaona Garcia}, {Paulo Alonso}",

year = "2018",

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doi = "10.1007/978-3-030-01421-6_19",

language = "English (US)",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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editor = "Yannis Manolopoulos and Barbara Hammer and Ilias Maglogiannis and Vera Kurkova and Lazaros Iliadis",

booktitle = "Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings",

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note = "27th International Conference on Artificial Neural Networks, ICANN 2018 ; Conference date: 04-10-2018 Through 07-10-2018",

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Hernández-Ruiz, CM, Villagrán Martínez, SA, Ortiz Guzmán, JE & Gaona Garcia, PA 2018, Model Based on Support Vector Machine for the Estimation of the Heart Rate Variability. in Y Manolopoulos, B Hammer, I Maglogiannis, V Kurkova & L Iliadis (eds), Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11140 LNCS, Springer, pp. 186-194, 27th International Conference on Artificial Neural Networks, ICANN 2018, Rhodes, Greece, 10/4/18. https://doi.org/10.1007/978-3-030-01421-6_19

Model Based on Support Vector Machine for the Estimation of the Heart Rate Variability. / Hernández-Ruiz, Catalina Maria; Villagrán Martínez, Sergio Andrés; Ortiz Guzmán, Johan Enrique et al.
Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings. ed. / Yannis Manolopoulos; Barbara Hammer; Ilias Maglogiannis; Vera Kurkova; Lazaros Iliadis. Springer, 2018. p. 186-194 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11140 LNCS).

Research output: Chapter in Book/Report › Conference contribution

TY - GEN

T1 - Model Based on Support Vector Machine for the Estimation of the Heart Rate Variability

AU - Hernández-Ruiz, Catalina Maria

AU - Villagrán Martínez, Sergio Andrés

AU - Ortiz Guzmán, Johan Enrique

AU - Gaona Garcia, Paulo Alonso

PY - 2018/1/1

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N2 - This paper shows the design, implementation and analysis of a Machine Learning (ML) model for the estimation of Heart Rate Variability (HRV). Through the integration of devices and technologies of the Internet of Things, a support tool is proposed for people in health and sports areas who need to know an individual’s HRV. The cardiac signals of the subjects were captured through pectoral bands, later they were classified by a Support Vector Machine algorithm that determined if the HRV is depressed or increased. The proposed solution has an efficiency of 90.3% and it’s the initial component for the development of an application oriented to physical training that suggests exercise routines based on the HRV of the individual.

AB - This paper shows the design, implementation and analysis of a Machine Learning (ML) model for the estimation of Heart Rate Variability (HRV). Through the integration of devices and technologies of the Internet of Things, a support tool is proposed for people in health and sports areas who need to know an individual’s HRV. The cardiac signals of the subjects were captured through pectoral bands, later they were classified by a Support Vector Machine algorithm that determined if the HRV is depressed or increased. The proposed solution has an efficiency of 90.3% and it’s the initial component for the development of an application oriented to physical training that suggests exercise routines based on the HRV of the individual.

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SN - 9783030014209

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 186

EP - 194

BT - Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings

A2 - Manolopoulos, Yannis

A2 - Hammer, Barbara

A2 - Maglogiannis, Ilias

A2 - Kurkova, Vera

A2 - Iliadis, Lazaros

PB - Springer

T2 - 27th International Conference on Artificial Neural Networks, ICANN 2018

Y2 - 4 October 2018 through 7 October 2018

ER -

Hernández-Ruiz CM, Villagrán Martínez SA, Ortiz Guzmán JE, Gaona Garcia PA. Model Based on Support Vector Machine for the Estimation of the Heart Rate Variability. In Manolopoulos Y, Hammer B, Maglogiannis I, Kurkova V, Iliadis L, editors, Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings. Springer. 2018. p. 186-194. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-01421-6_19

Model Based on Support Vector Machine for the Estimation of the Heart Rate Variability

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