Modelo basado en support vector machine para la estimación de la variabilidad de la frecuencia cardíaca

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.