Bibliographic citations
Albin, B., Rasmussen, A. (2021). Modelo tecnológico para el monitoreo y análisis del nivel de estrés cardiaco utilizando wearables [Tesis, Universidad Peruana de Ciencias Aplicadas (UPC)]. http://hdl.handle.net/10757/656674
Albin, B., Rasmussen, A. Modelo tecnológico para el monitoreo y análisis del nivel de estrés cardiaco utilizando wearables [Tesis]. PE: Universidad Peruana de Ciencias Aplicadas (UPC); 2021. http://hdl.handle.net/10757/656674
@misc{renati/396211,
title = "Modelo tecnológico para el monitoreo y análisis del nivel de estrés cardiaco utilizando wearables",
author = "Rasmussen Olivera, Alfredo Gabriel",
publisher = "Universidad Peruana de Ciencias Aplicadas (UPC)",
year = "2021"
}
Heart disease today represents the leading cause of death in the adult population nationwide, according to the Seguro Social de Salud (EsSalud). Based on statistical information, EsSalud indicates that only in the capital each day between 4 and 5 myocardial infarctions occur, of which more than two thirds of them occur in men. The project developed provides a tool for medicine, which optimizes the monitoring processes of cardiac stress in patients permanently. Likewise, the detection of abnormalities in cardiac behavior. Additionally, the model makes it possible to make patient information known to physicians in real time, which allows timely decisions to be made regarding the health of patients. This project will focus on the development of a technological model for the monitoring and analysis of the level of cardiac stress using wearables. The present technological model is subdivided between input, output, a support layer and 4 main phases: 1.Capture; 2.Transmission; 3.Processing; 4.Destination. It was decided to apply the model in open source technologies, so that once its operation has been demonstrated, it can be replicated on any platform and have the same result, without the need to lock it into a single solution. The biometric taking of patients in real time, out of a total of 72 hours monitored per patient, showed that on average 93.67% of the time the patients were monitored and that 12.50% of the patients had a cardiac abnormality.
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