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2018 | OriginalPaper | Buchkapitel

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

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

Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2018

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Right Ventricle Segmentation in Cardiac MR Images Using U-Net with Partly Dilated Convolution

Nächstes Kapitel High-Resolution Generative Adversarial Neural Networks Applied to Histological Images Generation

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Titel: Model Based on Support Vector Machine for the Estimation of the Heart Rate Variability
verfasst von: Catalina Maria Hernández-Ruiz
Sergio Andrés Villagrán Martínez
Johan Enrique Ortiz Guzmán
Paulo Alonso Gaona Garcia
Verlag: Springer International Publishing
Buch: Artificial Neural Networks and Machine Learning – ICANN 2018
Print ISBN: 978-3-030-01420-9

Electronic ISBN: 978-3-030-01421-6

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-01421-6_19

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