Caracterización de Fibrilación Ventricular para Predicción de Riesgo de Muerte Súbita Cardíaca basado en Análisis Wavelet
Sudden Cardiac Death describes unexpected natural death from a cardiac cause in a short period of time, this is one of the main causes of death in the world and represents more than 50% of deaths from Cardiovascular Diseases and Ventricular Fibrillation is the most frequent peculiarity that leads to...
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| Formato: | tesis de maestría |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2022 |
| País: | México |
| Recursos: | Instituto Nacional de Astrofísica, Óptica y Electrónica |
| Repositorio: | Repositorio Institucional del INAOE |
| Idioma: | español |
| OAI Identifier: | oai:inaoe.repositorioinstitucional.mx:1009/2371 |
| Acesso em linha: | http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/2371 |
| Access Level: | acceso abierto |
| Palavra-chave: | info:eu-repo/classification/Inspec/ECG info:eu-repo/classification/Inspec/Fibrilación Ventricular info:eu-repo/classification/Inspec/Muerte Súbita Cardíaca info:eu-repo/classification/Inspec/Wavelet info:eu-repo/classification/cti/1 info:eu-repo/classification/cti/22 info:eu-repo/classification/cti/2209 info:eu-repo/classification/cti/220990 |
| Resumo: | Sudden Cardiac Death describes unexpected natural death from a cardiac cause in a short period of time, this is one of the main causes of death in the world and represents more than 50% of deaths from Cardiovascular Diseases and Ventricular Fibrillation is the most frequent peculiarity that leads to this as it can lead to the inability of the heart to pump blood effectively and thus its survival rate decreases by every minute until it leads to death due to sudden cardiac arrest. Therefore, early prediction of this arrhythmia is very important for timely treatment and a higher survival rate. The Electrocardiogram is one of the most important techniques for diagnosing heart disease, to deal with these problems. In this paper, we propose a method to classify these signals using Wavelet Packet Entropy. Specifically, we first decompose the QT complexes of these signals with the help of Wavelet Packet Decomposition, and then calculate the entropy from the decomposed coefficients as representative features, and finally train a Random Forest-based classification model with which carry out experiments that allow the characterization of Ventricular Fibrillation to predict the risk of Sudden Cardiac Death. The experimental results show that the proposed methodology is promising for Electrocardiogram classification, allowing prediction with greater anticipation, accuracy and precision than the state of the art. |
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