A comprehensive evaluation of interpretable artificial intelligence for epileptic seizure diagnosis using an electroencephalogram: A systematic review.

ABSTRACT:

Background

Epilepsy is a sensitive social and health issue that causes sudden death in epilepsy. Awake and sleep electroencephalogram (EEG) first test confirms 80% of patients with confirmed epilepsy. Explainable artificial intelligence (XAI) for epileptic seizures (ESs) emerged to overcome drawbacks of artificial intelligence (AI) models like lack of right to explain, fairness, and trustworthiness, and an overwhelming paper was published. However, there is a lack of reporting interpretable and performance tradeoffs, stating the most interpretable AI applied, describing the most useful waveforms learned in XAI models, documenting areas of interest, and identifying the relationship between frequency bands and epilepsy. Therefore, this systematic review aims to comprehensively evalua

SUBMITTER: Gurmessa DK 

PROVIDER: S-EPMC11907617 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature