2026-04-27T13:46:47Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/449442024-05-27T12:36:19Zcom_10324_38col_10324_787

Rueda Sabater, María Cristina Vivaracho Pascual, Carlos Enrique Lamela Pérez, Adrián Universidad de Valladolid. Escuela de Ingeniería Informática de Valladolid 2021-01-12T13:29:40Z 2021-01-12T13:29:40Z 2020 http://uvadoc.uva.es/handle/10324/44944 Cyrcadian clock, cell cycle, astrophysics, are only the top of a much bigger iceberg of oscillatory signals. The study of this signals has been adressed since the decade of 90's, but it is now when the improvements of computer science allow us to achieve more results on these studies. Many models have been developed, including Cosinor methodology and some machine learning techniques. However, the rst one displays one major drawback, failing to represent a vast number of morphologies, while the second one acts as a black box with no enough precission. This work focuses on a novel approach called Frecuency Modulated Möbius (FMM) developed by the research group Inferencia con Restricciones (University of Valladolid) in 2019. It also serves as the natural sequel to a previous work of this author [6]. Three papers establish the basis of the methodology and interesting applications. The rst one describes the FMM methodology and was published in 2019 [1]. The second one studies the application of this model in an automatic analysis of electrocardiogram data, which is currently under revision [3]. The last one focuses on details of implementation, and it is still in development [2]. The author of this TFM has participated in the last two of them. Chapter 1 describes the theoretical details of FMM model and its derivations, including the multicomponent FMM model, restricted FMM model, and ECG-based FMM model. In Chapter 2, we discuss in more depth the details and implications of an automatic analysis of ECGs based on this methodology. Last Chapter is focused on practical uses of these models, and examples of use of the software developed. Departamento de Informática (Arquitectura y Tecnología de Computadores, Ciencias de la Computación e Inteligencia Artificial, Lenguajes y Sistemas Informáticos) Departamento de Estadística e Investigación Operativa Máster en Inteligencia de Negocio y Big Data en Entornos Seguros / Business Intelligence and Big Data in Cyber-Secure Environments application/pdf spa info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional FMM Modelo estadístico Electrocardiograma Implementación de un paquete de software para el ajuste de modelos FMM. Aplicación a la interpretación automática de la señal del electrocardiograma info:eu-repo/semantics/masterThesis