Caracterización de los potenciales evocados relacionados con eventos olfativos (OERP) provenientes del bulbo olfatorio en personas saludables empleando aprendizaje automático no supervisado
| dc.contributor.advisor | Cely Jiménez, Andrés | |
| dc.contributor.author | Losada Cerquera, Daniela | |
| dc.contributor.orcid | Losada Cerquera, Daniela [0000-0002-1838-3690] | |
| dc.date.accessioned | 2024-05-06T19:33:41Z | |
| dc.date.available | 2024-05-06T19:33:41Z | |
| dc.date.issued | 2024-03-22 | |
| dc.description.abstract | Las alteraciones olfativas y cognitivas se pueden identificar mediante la realización de test olfativos, los cuales se caracterizan en su mayoría por ser de tipo cualitativo. Numerosos estudios relacionan enfermedades neurodegenerativas y sistémicas graves con alteraciones en el olfato que se evidencian con mayor frecuencia en adultos mayores. El reto en el área de la salud es identificar marcadores preclínicos no invasivos que permita predecir mediante análisis de frecuencia y/o el tiempo de manera cuantitativa asociados a los eventos olfativos y sus alteraciones, por ello en esta investigación se plantea la implementación de aprendizaje computacional no supervisado K-meanspara la caracterización de los OERP en las señales electrobulbográficas (EBG) en el dominio del tiempo y en su densidad espectral (PSD power spectral density). | |
| dc.description.abstractenglish | Olfactory and cognitive alterations can be identified by means of olfactory tests, which are mostly qualitative in nature. Numerous studies relate serious neurodegenerative and systemic diseases with olfactory alterations that are more frequently evidenced in older adults. The challenge in the health area is to identify non-invasive preclinical markers that allow to predict by means of frequency and/or time analysis in a quantitative way associated with olfactory events and their alterations, therefore in this research we propose the implementation of unsupervised computational learning K-means for the characterization of the OERP in the electrobulbographic signals (EBG) in the time domain and in its spectral density (spectral density). and their spectral density (PSD power spectral density). | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12495/12086 | |
| dc.language.iso | es | |
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| dc.rights | Attribution-NoDerivatives 4.0 Internacional | esp |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | esp |
| dc.rights.accessrights | http://purl.org/coar/access_right/c_abf2 | spa |
| dc.rights.local | Acceso abierto | spa |
| dc.rights.uri | http://creativecommons.org/licenses/by-nd/4.0/ | spa |
| dc.subject | ERP olfativos | |
| dc.subject | Aprendizaje automático | |
| dc.subject | Señales EEG | |
| dc.subject | Potenciales Evocados | |
| dc.subject.keywords | Olfactory ERPs | |
| dc.subject.keywords | Machine learning | |
| dc.subject.keywords | Evoked potentials | |
| dc.subject.keywords | EEG signals | |
| dc.title | Caracterización de los potenciales evocados relacionados con eventos olfativos (OERP) provenientes del bulbo olfatorio en personas saludables empleando aprendizaje automático no supervisado | |
| dc.title.translated | Characterization of olfactory event-related evoked potentials (OERPs) from the olfactory bulb of event-related evoked potentials (OERPs) from the olfactory bulb in healthy individuals using unsupervised machine learning |
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