Classification with a Deferral Option and Low-Trust Filtering for Automated Seizure Detection.

ABSTRACT: Wearable technology will become available and allow prolonged electroencephalography (EEG) monitoring in the home environment of patients with epilepsy. Neurologists analyse the EEG visually and annotate all seizures, which patients often under-report. Visual analysis of a 24-h EEG recording typically takes one to two hours. Reliable automated seizure detection algorithms will be crucial to reduce this analysis. We investigated such algorithms on a dataset of behind-the-ear EEG measurements. Our first aim was to develop a methodology where part of the data is deferred to a human expert, who performs perfectly, with the goal of obtaining an (almost) perfect detection sensitivity (DS). Prediction confidences are determined by temperature scaling of the classification model outputs and trust

SUBMITTER: Becker T 

PROVIDER: S-EPMC7913713 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature