Concept-drifts adaptation for machine learning EEG epilepsy seizure prediction

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: April 8, 2024

Abstract Seizure prediction remains a challenge, with approximately 30% of patients unresponsive to conventional treatments. Addressing this issue is crucial for improving patients’ quality life, as timely intervention can mitigate the impact seizures. In research field, it critical identify preictal interval, transition from regular brain activity seizure. While previous studies have explored various Electroencephalogram (EEG) based methodologies prediction, few been clinically applicable. Recent underlined dynamic nature EEG data, characterised by data changes time, known concept drifts, highlighting need automated methods detect and adapt these changes. study, we investigate effectiveness automatic drift adaptation in seizure prediction. Three patient-specific approaches 10-minute horizon are compared: algorithm incorporating window adjustment method optimising performance Support Vector Machines (Backwards-Landmark Window), data-batch (seizures) selection using logistic regression (Seizure-batch Regression), integration classifiers (Dynamic Weighted Ensemble). These incorporate retraining process after each use combination univariate linear features SVM classifiers. The Firing Power was used post-processing technique generate alarms before were compared control approach on typical machine learning pipeline, considering group 37 Temporal Lobe Epilepsy EPILEPSIAE database. best-performing Window) achieved results 0.75 ± 0.33 sensitivity 1.03 1.00 false positive rate per hour. This new strategy performed above chance 89% surrogate predictor, whereas only validated 46%.

Language: Английский

EEG Waveband Computation for Seizure Brain State Identification

Published: Jan. 1, 2025

Language: Английский

Optimizing Machine Translation: A Benchmarking Suite for Efficiency and Quality Enhancement

Published: Oct. 24, 2024

Language: Английский