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Predictive Modeling for Seizure Detection in Pharmacoresistant Epilepsy: a machine learning approach

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Guluma Escamilla, María José
Polanco Flórez, Santiago Andrés
Abstract (Spanish)
Este estudio propone un enfoque basado en aprendizaje automático para identificar patrones preictales e ictales en señales EEG y estimar el tiempo restante hasta el inicio de una crisis. Usando la base de datos CHB-MIT Scalp EEG, se desarrolló un sistema automático de etiquetado y se aplicaron técnicas de procesamiento de señales como la FFT y la transformada wavelet de Morlet, junto con una selección de características basada en la entropía de Shannon. Se implementó una red neuronal convolucional temporal (CNN) para realizar tareas de clasificación y regresión.
Abstract (English)
This study presents a machine learning-based approach to identify preictal and ictal EEG patterns and estimate time to seizure onset. Using the CHB-MIT Scalp EEG dataset, we developed an automated labeling pipeline and applied signal processing techniques such as FFT and Morlet wavelet transforms, along with Shannon entropy-based feature selection. A Temporal Convolutional Neural Network (CNN) was implemented to perform both classification and regression tasks.
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31 páginas
URI: https://repositorio.escuelaing.edu.co/handle/001/3617
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BA - Trabajos Dirigidos de Biomédica
References

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