Model Based on Support Vector Machine for the Estimation of the Heart Rate Variability-Reference-Cited by-同舟云学术

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

Published:2018 Issue: Volume: Page:186-194
ISSN:0302-9743
Container-title:Artificial Neural Networks and Machine Learning – ICANN 2018
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Author:

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

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-01421-6_19

Reference22 articles.

1. Pitale, R., Tajane, K., Umale, J.: Heart rate variability classification and feature extraction using support vector machine and PCA: an overview. J. Eng. Res. Appl. 4, 381–384 (2014)

2. Borchini, R., Veronesi, G., Bonzini, M., Gianfagna, F., Dashi, O., Ferrario, M.: Heart rate variability frequency domain alterations among healthy nurses exposed to prolonged work stress. Int. J. Environ. Res. Public Health 15, 113 (2018)

3. Communications in Computer and Information Science;C Hernández,2016

4. Giles, D., Draper, N., Neil, W.: Validity of the Polar V800 heart rate monitor to measure RR intervals at rest. Eur. J. Appl. Physiol. 116, 563–571 (2015)

5. Erkkila, M., Rae, R., Thurlin, T., Korva, T., Manninen, T.: Managing physiological exercise data. US Patent 9855463B2, 16 January 2014

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