Model based on support vector machine for the estimation of the heart rate variability

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. © Springer Nature Switzerland AG 2018.

• Fecha de publicación: 2018
• Institución: Universidad del Rosario
• Repositorio: Repositorio EdocUR - U. Rosario
• Idioma: eng
• OAI Identifier: oai:repository.urosario.edu.co:10336/22528
• Acceso en línea: https://doi.org/10.1007/978-3-030-01421-6_19; https://repository.urosario.edu.co/handle/10336/22528
• Palabra clave: Internet of things; Neural networks; Patient monitoring; Support vector machines; Application-oriented; Cardiac signals; Heart rate variability; Heart-rate monitors; Internet of Things (IOT); Model-based OPC; Physical training; Support vector machine algorithm; Heart; Heart Rate Monitor (HRM); Heart rate variability (HRV); Internet of things (IOT); Support vector machine (SVM)
• License: http://purl.org/coar/access_right/c_abf2