Desarrollo de un sistema de IoT con machine learning para la detección temprana de crisis epilépticas
| dc.contributor.advisor | Salcedo Parra , Octavio José | |
| dc.contributor.advisor | Guio Mahecha, Laura Victoria | |
| dc.contributor.author | Peña Arismendi, Santiago Andrés | |
| dc.contributor.orcid | Salcedo Parra , Octavio José []0000-0002-0767-8522 | |
| dc.date.accessioned | 2024-10-25T16:09:07Z | |
| dc.date.available | 2024-10-25T16:09:07Z | |
| dc.date.created | 2024-08-29 | |
| dc.description | Esta investigación aborda la creación de un sistema basado en el Internet de las Cosas (IoT) y optimizado mediante técnicas de aprendizaje automático para la detección temprana de crisis epilépticas. Se desarrolló un algoritmo capaz de capturar señales biológicas a través de sensores portátiles y procesarlas en una arquitectura IoT. Los datos obtenidos del Hospital de la Misericordia en Bogotá fueron usados para validar diversos modelos, destacándose un árbol de decisión y una red neuronal perceptrón multicapa, que lograron precisiones de 92.47% y 93.56%, respectivamente. En entornos clínicos reales, estos modelos lograron una precisión del 65%. La arquitectura propuesta permite enviar alertas preventivas en tiempo real, ofreciendo una herramienta potencialmente eficaz para mejorar la calidad de vida de personas con epilepsia. | |
| dc.description.abstract | This research addresses the development of a system based on the Internet of Things (IoT) and optimized through machine learning techniques for the early detection of epileptic seizures. An algorithm was developed to capture biological signals through wearable sensors and process them within an IoT architecture. Data obtained from the Hospital de la Misericordia in Bogotá were used to validate various models, with a decision tree and a multilayer perceptron neural network standing out, achieving accuracies of 92.47% and 93.56%, respectively. In real clinical settings, these models achieved an accuracy of 65%. The proposed architecture allows for real-time preventive alerts, offering a potentially effective tool to improve the quality of life for people with epilepsy. | |
| dc.format.mimetype | ||
| dc.identifier.uri | http://hdl.handle.net/11349/42172 | |
| dc.language.iso | spa | |
| dc.publisher | Universidad Distrital Francisco José de Caldas | |
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| dc.rights.acceso | Abierto (Texto Completo) | |
| dc.rights.accessrights | OpenAccess | |
| dc.subject | Detección temprana de crisis epilépticas | |
| dc.subject | Internet de las Cosas (IoT) | |
| dc.subject | Aprendizaje automático en salud | |
| dc.subject | Sensores portátiles biomédicos | |
| dc.subject | Redes neuronales en epilepsia | |
| dc.subject | Monitoreo cardíaco predictivo | |
| dc.subject.keyword | Early detection of epileptic seizures | |
| dc.subject.keyword | Internet of Things (IoT) | |
| dc.subject.keyword | Machine learning in healthcare | |
| dc.subject.keyword | Wearable biomedical sensors | |
| dc.subject.keyword | Neural networks in epilepsy | |
| dc.subject.keyword | Predictive cardiac monitoring | |
| dc.subject.lemb | Maestría en Ciencias de la Información y las Comunicaciones -- Tesis y disertaciones académicas | |
| dc.subject.lemb | Internet de las Cosas (IoT) | |
| dc.subject.lemb | Aprendizaje automático en salud | |
| dc.subject.lemb | Detección temprana de crisis epilépticas | |
| dc.title | Desarrollo de un sistema de IoT con machine learning para la detección temprana de crisis epilépticas | |
| dc.title.titleenglish | Development of an IoT system with machine learning for the early detection of epileptic seizures | |
| dc.type | masterThesis | |
| dc.type.degree | Investigación-Innovación | |
| dc.type.driver | info:eu-repo/semantics/bachelorThesis |
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