EEG Epilepsy Seizure Prediction: The Post-processing Stage as a Chronology

Опубликована: Дек. 7, 2023

Almost one-third of epileptic patients fail to achieve seizure control through anti-epileptic drug administration. In those cases, prediction plays a significant role in clinical management and therapy. Seizure algorithms aim identify the preictal period that Electroencephalogram (EEG) signals can capture. However, this is associated with substantial heterogeneity. The present work proposes patient-specific using post-processing techniques explore existence set chronological brain events precedes seizures. study was conducted 37 from EPILEPSIAE database. designed methodology combines univariate linear features classifier based on Support Vector Machines (SVM) two handle pre-seizure temporality an easily explainable way, employing knowledge network theory. Chronological Firing Power approach, we considered as sequence three separated time. Cumulative assumed overlapping events. These methodologies were compared approach typical machine learning pipeline. Our results showed may improve performance. This new strategy performed above chance for 62% patients, whereas Control only validated 49% its models.

Язык: Английский

Machine Learning Methods in Seizure Prediction and Forecasting: What Is the Best Approach?

Опубликована: Дек. 7, 2023

Traditional treatments do not work on 33% of epileptic patients.Warning devices employing seizure prediction or forecasting algorithms could bring patients a newfound quality life. These would attempt to detect the preictal period, transitional moment between regular brain activity and seizure, warn user. Several past methodologies have been developed, triggering an alarm when detecting but few clinically applicable. Recent studies suggested paradigm change that takes probabilistic approach instead crisp one prediction. The is substituted by constant risk assessment analysis. To best our knowledge, no direct comparison using same database has made. This paper explores capable compares them with ones. Using data from EPILEPSIAE database, we developed several patient-specific different classifiers (a Logistic Regression, 15 Support Vector Machines ensemble, Shallow Neural Networks ensemble). Results show increase sensitivity in relative up 146% number displaying improvement over chance 200%. results suggest may be more suitable for warning than

Язык: Английский