Electroencephalography and brain magnetic resonance imaging in asphyxia comparing cooled and non-cooled infants.

OBJECTIVE: The aim was to establish any differences in the predictive value of EEG and MRI for outcome in infants treated and not-treated with therapeutic hypothermia (HT) for perinatal asphyxia. We hypothesize that they are equally predictive and that combining both has the highest predictive value. STUDY DESIGN: We retrospectively compared data of infants with hypoxic-ischemic encephalopathy (HIE) who received HT (n = 45) between September 2009 and December 2013 with those of infants with HIE born between January 2004 and August 2009, before HT was available (NT, n = 37). All received conventional and/or amplitude-integrated EEG during the first days and early MRI (day 4-5). Associations of EEG, MRI and severe neurodevelopmental outcome (death or Bayley's -2SD below mean), were tested with a multivariable logistic regression analysis, corrected for HT. RESULTS: Forty-eight hours' EEG background pattern had a PPV of 92% and a NPV of 81% in HT, versus 100% and 58% in NT. MRI had a PPV of 71% and a NPV of 93% in HT, versus 82% and 75% in NT. The adjusted OR for adverse outcome was 0.013 (95% CI 0.002-0.154, p < 0.001) for EEG background normalization within 48 h and 32.19 (95% CI 4.84-214.25, p < 0.001) for abnormal MRI. CONCLUSION: The predictive value of EEG and MRI is equal in cooled and non-cooled infants with HIE. Our data show a higher predictive value (death and severe outcome) for EEG compared to MRI. In HIE, persistent abnormal EEG background pattern until 48 h, combined with abnormal early MRI is strongly predictive for poor neurodevelopment.

Weighted Performance Metrics for Automatic Neonatal Seizure Detection Using Multiscored EEG Data.

In neonatal intensive care units, there is a need for around the clock monitoring of electroencephalogram (EEG), especially for recognizing seizures. An automated seizure detector with an acceptable performance can partly fill this need. In order to develop a detector, an extensive dataset labeled by experts is needed. However, accurately defining neonatal seizures on EEG is a challenge, especially when seizure discharges do not meet exact definitions of repetitiveness or evolution in amplitude and frequency. When several readers score seizures independently, disagreement can be high. Commonly used metrics such as good detection rate (GDR) and false alarm rate (FAR) derived from data scored by multiple raters have their limitations. Therefore, new metrics are needed to measure the performance with respect to the different labels. In this paper, instead of defining the labels by consensus or majority voting, popular metrics including GDR, FAR, positive predictive value, sensitivity, specificity, and selectivity are modified such that they can take different scores into account. To this end, 353 hours of EEG data containing seizures from 81 neonates were visually scored by a clinical neurophysiologist, and then processed by an automated seizure detector. The scored seizures were mixed with false detections of an automated seizure detector and were relabeled by three independent EEG readers. Then, all labels were used in the proposed performance metrics and the result was compared with the majority voting technique and showed higher accuracy and robustness for the proposed metrics. Results were confirmed using a bootstrapping test.

Interrater agreement in visual scoring of neonatal seizures based on majority voting on a web-based system: The Neoguard EEG database.

OBJECTIVE: To assess interrater agreement based on majority voting in visual scoring of neonatal seizures. METHODS: An online platform was designed based on a multicentre seizure EEG-database. Consensus decision based on 'majority voting' and interrater agreement was estimated using Fleiss' Kappa. The influences of different factors on agreement were determined. RESULTS: 1919 Events extracted from 280h EEG of 71 neonates were reviewed by 4 raters. Majority voting was applied to assign a seizure/non-seizure classification. 44% of events were classified with high, 36% with moderate, and 20% with poor agreement, resulting in a Kappa value of 0.39. 68% of events were labelled as seizures, and in 46%, all raters were convinced about electrographic seizures. The most common seizure duration was <30s. Raters agreed best for seizures lasting 60-120s. There was a significant difference in electrographic characteristics of seizures versus dubious events, with seizures having longer duration, higher power and amplitude. CONCLUSIONS: There is a wide variability in identifying rhythmic ictal and non-ictal EEG events, and only the most robust ictal patterns are consistently agreed upon. Database composition and electrographic characteristics are important factors that influence interrater agreement. SIGNIFICANCE: The use of well-described databases and input of different experts will improve neonatal EEG interpretation and help to develop uniform seizure definitions, useful for evidence-based studies of seizure recognition and management.