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De la anotación a la predicción: Predicción temprana del riesgo de convulsiones de grado hospitalario a partir de EEG

Norah Alharbi¹, Mashael Aldayel², Shrooq Alsenan³

¹Department of Internal Medicine, College of Medicine, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

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|February 13, 2026

Resumen

Este resumen es generado por máquina.

Este estudio presenta un modelo de IA para el análisis automatizado de EEG, que predice el riesgo de convulsiones identificando patrones interictales. El algoritmo Random Forest alcanzó una precisión del 96,50 %, mejorando la eficiencia diagnóstica.

Palabras clave:

inteligencia artificial (IA)electroencefalografía (EEG)epilepsia unidad de monitorización de convulsiones (UMC)predicción de convulsiones

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Área de la Ciencia:

Neurociencia
Inteligencia Artificial
Diagnóstico Médico

Sus antecedentes:

La revisión manual de electroencefalogramas (EEG) es lenta y requiere mucha mano de obra.
Se necesitan herramientas automatizadas de análisis de EEG para mejorar la eficiencia clínica y la precisión diagnóstica.
Los métodos actuales a menudo se centran en la detección de convulsiones durante los estados ictales.

Objetivo del estudio:

Desarrollar y validar un modelo de IA para la interpretación automatizada de grabaciones de EEG en adultos.
Centrarse en la predicción temprana del riesgo de convulsiones a través del reconocimiento de patrones interictales.
Distinguir entre EEG normales y anormales, incluyendo varios tipos de anomalías.

Principales métodos:

Se implementaron tres algoritmos de clasificación de IA: Support Vector Machine (SVM), Random Forest (RF) y K-Nearest Neighbors (KNN).
El modelo se diseñó para clasificar los EEG en normales, anomalías no epilépticas, descargas epilépticas y convulsiones electrográficas.
Se validó el rendimiento del modelo en un conjunto de datos de grabaciones de EEG en adultos.

Principales resultados:

El algoritmo Random Forest (RF) demostró un rendimiento óptimo.
Se logró una precisión del 96,50 % en la identificación de la actividad EEG normal.
El sistema de IA mejora la eficiencia, la coherencia y la accesibilidad de la interpretación del EEG.

Conclusiones:

La herramienta de IA apoya a los médicos en el diagnóstico de afecciones neurológicas y el seguimiento del progreso del paciente.
Ofrece un enfoque innovador para mejorar los plazos de diagnóstico y la toma de decisiones clínicas.
Valioso en entornos con acceso limitado a neurofisiólogos.