Accurate detection of spontaneous seizures using a generalized linear model with external validation.

ABSTRACT:

Objective

Seizure detection is a major facet of electroencephalography (EEG) analysis in neurocritical care, epilepsy diagnosis and management, and the instantiation of novel therapies such as closed-loop stimulation or optogenetic control of seizures. It is also of increased importance in high-throughput, robust, and reproducible pre-clinical research. However, seizure detectors are not widely relied upon in either clinical or research settings due to limited validation. In this study, we create a high-performance seizure-detection approach, validated in multiple data sets, with the intention that such a system could be available to users for multiple purposes.

Methods

We introduce a generalized linear model trained on 141 EEG signal features for classification of seizures

SUBMITTER: Fumeaux NF 

PROVIDER: S-EPMC7953845 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature