Reliability and accuracy of EEG interpretation for estimating age in preterm infants

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Stevenson , N J , Tataranno , M-L , Kaminska , A , Pavlidis , E , Clancy , R R , Griesmaier , E , Roberts , J A , Klebermass-Schrehof , K & Vanhatalo , S 2020 , ' Reliability and accuracy of EEG interpretation for estimating age in preterm infants ' , Annals of Clinical and Translational Neurology , vol. 7 , no. 9 , pp. 1564-1573 .

Title: Reliability and accuracy of EEG interpretation for estimating age in preterm infants
Author: Stevenson, Nathan J.; Tataranno, Maria-Luisa; Kaminska, Anna; Pavlidis, Elena; Clancy, Robert R.; Griesmaier, Elke; Roberts, James A.; Klebermass-Schrehof, Katrin; Vanhatalo, Sampsa
Contributor organization: HUS Medical Imaging Center
Kliinisen neurofysiologian yksikkö
Department of Neurosciences
Children's Hospital
Helsinki University Hospital Area
HUS Children and Adolescents
Neuroscience Center
Helsinki Institute of Life Science HiLIFE
Date: 2020-09
Language: eng
Number of pages: 10
Belongs to series: Annals of Clinical and Translational Neurology
ISSN: 2328-9503
Abstract: Objectives: To determine the accuracy of, and agreement among, EEG and aEEG readers' estimation of maturity and a novel computational measure of functional brain age (FBA) in preterm infants. Methods: Seven experts estimated the postmenstrual ages (PMA) in a cohort of recordings from preterm infants using cloud-based review software. The FBA was calculated using a machine learning-based algorithm. Error analysis was used to determine the accuracy of PMA assessments and intraclass correlation (ICC) was used to assess agreement between experts. Results: EEG recordings from a PMA range 25 to 38 weeks were successfully interpreted. In 179 recordings from 62 infants interpreted by all human readers, there was moderate agreement between experts (aEEG ICC = 0.724; 95%CI:0.658-0.781 and EEG ICC = 0.517; 95%CI:0.311-0.664). In 149 recordings from 61 infants interpreted by all human readers and the FBA algorithm, random and systematic errors in visual interpretation of PMA were significantly higher than the computational FBA estimate. Tracking of maturation in individual infants showed stable FBA trajectories, but the trajectories of the experts' PMA estimate were more likely to be obscured by random errors. The accuracy of visual interpretation of PMA estimation was compromised by neurodevelopmental outcome for both aEEG and EEG review. Interpretation: Visual assessment of infant maturity is possible from the EEG or aEEG, with an average of human experts providing the highest accuracy. Tracking PMA of individual infants was hampered by errors in experts' estimates. FBA provided the most accurate maturity assessment and has potential as a biomarker of early outcome.
3112 Neurosciences
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion

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