Unsupervised EEG preictal interval identification in patients with drug-resistant epilepsy.

ABSTRACT: Typical seizure prediction models aim at discriminating interictal brain activity from pre-seizure electrographic patterns. Given the lack of a preictal clinical definition, a fixed interval is widely used to develop these models. Recent studies reporting preictal interval selection among a range of fixed intervals show inter- and intra-patient preictal interval variability, possibly reflecting the heterogeneity of the seizure generation process. Obtaining accurate labels of the preictal interval can be used to train supervised prediction models and, hence, avoid setting a fixed preictal interval for all seizures within the same patient. Unsupervised learning methods hold great promise for exploring preictal alterations on a seizure-specific scale. Multivariate and univariate linear and no

SUBMITTER: Leal A 

PROVIDER: S-EPMC9842648 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature