Tendencias y perspectivas en la detección de depresión mediante el análisis de  señales Electroencefalográficas (EEG): una Revisión Sistemática de la Literatura.

Teofilo Habiram Obispo Bustillos; Nimrod  González Franco; Juan Gabriel  González Serna; Dante  Mújica Vargas; Noé Alejandro  Castro Sánchez

doi:10.29057/xikua.v11i22.10561

Teofilo Habiram Obispo Bustillos Centro Nacional de Investigación y Desarrollo Tecnológico https://orcid.org/0000-0002-9955-7428
Nimrod González Franco Centro Nacional de Investigación y Desarrollo Tecnológico https://orcid.org/0000-0002-1051-1379
Juan Gabriel González Serna Centro Nacional de Investigación y Desarrollo Tecnológico https://orcid.org/0000-0002-1874-9402
Dante Mújica Centro Nacional de Investigación y Desarrollo Tecnológico https://orcid.org/0000-0001-8665-4096
Noé Castro Centro Nacional de Investigación y Desarrollo Tecnológico https://orcid.org/0000-0002-8083-3891

DOI: https://doi.org/10.29057/xikua.v11i22.10561

Palabras clave: Depresión, EEG, Aprendizaje Automático, Aprendizaje Profundo

Resumen

Actualmente el diagnóstico de la depresión se realiza mediante entrevistas clínicas; teniendo en cuenta la información que aporta el paciente, se utilizan cuestionarios o test los cuales nos ayudan a tener una aproximación de la gravedad del padecimiento. Para evitar la intervención humana en este tipo de diagnósticos, se podría recurrir al análisis de señales electroencefalográficas (EEG) con diferentes técnicas de Aprendizaje Automático. Esta Revisión Sistemática de la Literatura (RSL) tiene como objetivo sintetizar las tendencias actuales sobre la detección de la depresión a través de señales EEG y modelos de Aprendizaje Automático. Se realizó una búsqueda en PubMed, IEEExplore, ScienceDirect y SpringerLink donde se obtuvieron 41 trabajos en bases de datos y 50 trabajos a través de otras fuentes, de los que se seleccionaron 20 artículos publicados entre 2020 y 2022, los cuales presentan una comparación entre diferentes métodos de procesamiento y clasificación. En su gran mayoría, los trabajos demuestran una tasa de precisión mayor al 80% en una clasificación binaria (Depresión/No Depresión), resultado del estudio de diferentes metodologías y modelos de Aprendizaje Automático. Este trabajo tiene como objetivo revisar la literatura para ofrecer una sección transversal útil para determinar los métodos de clasificación mayormente utilizados.

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