DOI:
https://doi.org/10.14483/23448393.22185Published:
2025-04-15Issue:
Vol. 30 No. 1 (2025): January-AprilSection:
Biomedical EngineeringOptimal Selection of Intrinsic Mode Functions Applied to Seizure Detection
Selección óptima de funciones de modo intrínseco aplicada a la detección de convulsiones
Keywords:
Seizure identification, empirical mode decomposition, optimal selection of IMFs, intrinsic mode functions, discrimination metrics (en).Keywords:
Identificación de convulsiones, descomposición modal empírica, selección óptima de IMFs, funciones modales intrínsecas, métricas de discriminación (es).Downloads
Abstract (en)
Context: Epilepsy is a severe chronic neurological disorder with considerable incidence due to recurrent seizures. These seizures can be detected and diagnosed noninvasively using an electroencephalogram. Empirical mode decomposition has shown excellent results in identifying epileptic crises.
Method: This study addressed a significant gap by proposing a novel approach for the automated selection of the most relevant intrinsic mode functions (IMFs) using empirical mode decomposition and discrimination metrics such as the Minkowski distance, the mean square error, cross-correlation, and the entropy function. The main objective was to address the challenge of determining the optimal number of IMFs required to accurately reconstruct brain activity signals.
Results:The results were promising, as they facilitated the identification of IMFs that contained the most relevant information, marking a significant advancement in the field. To validate these findings, standard methods including the correlation coefficient, the p-value, and the Wasserstein distance were employed. Additionally, an EEGLAB-based brain mapping was conducted, adding robustness and credibility to the results obtained.
Conclusions: Our method is a fundamental tool that enhances epileptic seizure identification from EEG signals, with significant clinical implications in the diagnosis and treatment of epilepsy.
Abstract (es)
Contexto: La epilepsia es un trastorno neurológico crónico grave con una incidencia considerable debido a convulsiones recurrentes. Estas convulsiones pueden ser detectadas de manera no invasiva y diagnosticadas mediante un electroencefalograma. La descomposición modal empírica ha mostrado excelentes resultados en la identificación de crisis epilépticas.
Métodos: Este estudio abordó una brecha significativa al proponer un enfoque novedoso para la selección automatizada de las funciones de modo intrínseco (IMF) más relevantes utilizando descomposición empírica de modo y métricas de discriminación tales como la distancia de Minkowski, el error cuadrático medio, la correlación cruzada y la función de entropía. El objetivo primario fue abordar el desafío de determinar el número óptimo de IMF requeridas para reconstruir con precisión las señales de actividad cerebral.
Resultados: Los resultados fueron prometedores, pues facilitaron la identificación de IMF que contenían la información más relevante, marcando un avance significativo en el campo. Para validar estos hallazgos, se emplearon métodos estándar, incluyendo el coeficiente de correlación, el valor p y la métrica de Wasserstein. Además, se realizó un mapeo cerebral con EEGLAB, lo que agregó robustez y credibilidad a los resultados obtenidos.
Conclusiones: Nuestro método es una herramienta fundamental que permite mejorar la identificación de convulsiones epilépticas a partir de señales de EEG, con importantes implicaciones clínicas en el diagnóstico y tratamiento de la epilepsia.
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Copyright (c) 2025 Luis Daladier Guerrero Otoya, Maximiliano Bueno-Lopez, Eduardo Giraldo Suárez, Marta Molinas Cabrera
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