Optical Flow Estimation Improves Automated Seizure Detection in Neonatal EEG.

ABSTRACT:

Purpose

Existing automated seizure detection algorithms report sensitivities between 43% and 77% and specificities between 56% and 90%. The algorithms suffer from false alarms when applied to neonatal EEG because of the high degree of nurse handling and rhythmic patting used to soothe neonates. Computer vision technology that quantifies movement in real time could distinguish artifactual motion and improve automated neonatal seizure detection algorithms.

Methods

The authors used video EEG recordings from 43 neonates undergoing monitoring for seizures as part of the NEOLEV2 clinical trial. The Persyst neonatal automated seizure detection algorithm ran in real time during study EEG acquisitions. Computer vision algorithms were applied to extract detailed accounts of artifactua

SUBMITTER: Martin JR 

PROVIDER: S-EPMC7887141 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature