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<title>Implementación de un paquete de software para el ajuste de modelos FMM. Aplicación a la interpretación automática de la señal del electrocardiograma</title>
<creator>Lamela Pérez, Adrián</creator>
<contributor>Rueda Sabater, María Cristina</contributor>
<contributor>Vivaracho Pascual, Carlos Enrique</contributor>
<contributor>Universidad de Valladolid. Escuela de Ingeniería Informática de Valladolid</contributor>
<description>Cyrcadian clock, cell cycle, astrophysics, are only the top of a much bigger iceberg&#xd;
of oscillatory signals. The study of this signals has been adressed since the decade of&#xd;
90's, but it is now when the improvements of computer science allow us to achieve more&#xd;
results on these studies.&#xd;
Many models have been developed, including Cosinor methodology and some machine&#xd;
learning techniques. However, the  rst one displays one major drawback, failing&#xd;
to represent a vast number of morphologies, while the second one acts as a  black box &#xd;
with no enough precission.&#xd;
This work focuses on a novel approach called Frecuency Modulated Möbius (FMM)&#xd;
developed by the research group  Inferencia con Restricciones  (University of Valladolid)&#xd;
in 2019. It also serves as the natural sequel to a previous work of this author [6]. Three&#xd;
papers establish the basis of the methodology and interesting applications. The  rst one&#xd;
describes the FMM methodology and was published in 2019 [1]. The second one studies&#xd;
the application of this model in an automatic analysis of electrocardiogram data, which&#xd;
is currently under revision [3]. The last one focuses on details of implementation, and&#xd;
it is still in development [2]. The author of this TFM has participated in the last two&#xd;
of them.&#xd;
Chapter 1 describes the theoretical details of FMM model and its derivations, including&#xd;
the multicomponent FMM model, restricted FMM model, and ECG-based FMM&#xd;
model. In Chapter 2, we discuss in more depth the details and implications of an automatic&#xd;
analysis of ECGs based on this methodology. Last Chapter is focused on practical&#xd;
uses of these models, and examples of use of the software developed.</description>
<date>2021-01-12</date>
<date>2021-01-12</date>
<date>2020</date>
<type>info:eu-repo/semantics/masterThesis</type>
<identifier>http://uvadoc.uva.es/handle/10324/44944</identifier>
<language>spa</language>
<rights>info:eu-repo/semantics/openAccess</rights>
<rights>http://creativecommons.org/licenses/by-nc-nd/4.0/</rights>
<rights>Attribution-NonCommercial-NoDerivatives 4.0 Internacional</rights>
</thesis></metadata></record></GetRecord></OAI-PMH>