A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction.

ABSTRACT: Seizure prediction may improve the quality of life of patients suffering from drug-resistant epilepsy, which accounts for about 30% of the total epileptic patients. The pre-ictal period determination, characterized by a transitional stage between normal brain activity and seizure, is a critical step. Past approaches failed to attain real-world applicability due to lack of generalization capacity. More recently, deep learning techniques may outperform traditional classifiers and handle time dependencies. However, despite the existing efforts for providing interpretable insights, clinicians may not be willing to make high-stake decisions based on them. Furthermore, a disadvantageous aspect of the more usual seizure prediction pipeline is its modularity and significant independence between st

SUBMITTER: Pinto MF 

PROVIDER: S-EPMC7873127 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature