Concept-drifts adaptation for machine learning EEG epilepsy seizure prediction.

ABSTRACT: Seizure prediction remains a challenge, with approximately 30% of patients unresponsive to conventional treatments. Addressing this issue is crucial for improving patients' quality of life, as timely intervention can mitigate the impact of seizures. In this research field, it is critical to identify the preictal interval, the transition from regular brain activity to a seizure. While previous studies have explored various Electroencephalogram (EEG) based methodologies for prediction, few have been clinically applicable. Recent studies have underlined the dynamic nature of EEG data, characterised by data changes with time, known as concept drifts, highlighting the need for automated methods to detect and adapt to these changes. In this study, we investigate the effectiveness of automatic co

SUBMITTER: Pontes ED 

PROVIDER: S-EPMC11001609 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature