Inter-rater agreement on identification of electrographic seizures and periodic discharges in ICU EEG recordings

OBJECTIVE

This study investigated inter-rater agreement (IRA) among EEG experts for the identification of electrographic seizures and periodic discharges (PDs) in continuous ICU EEG recordings.

METHODS

Eight board-certified EEG experts independently identified seizures and PDs in thirty 1-h EEG segments which were selected from ICU EEG recordings collected from three medical centers. IRA was compared between seizure and PD identifications, as well as among rater groups that have passed an ICU EEG Certification Test, developed by the Critical Care EEG Monitoring Research Consortium (CCEMRC).

RESULTS

Both kappa and event-based IRA statistics showed higher mean values in identification of seizures compared to PDs (k=0.58 vs. 0.38; p<0.001). The group of rater pairs who had both passed the ICU EEG Certification Test had a significantly higher mean IRA in comparison to rater pairs in which neither had passed the test.

CONCLUSIONS

IRA among experts is significantly higher for identification of electrographic seizures compared to PDs. Additional instruction, such as the training module and certification test developed by the CCEMRC, could enhance this IRA.

SIGNIFICANCE

This study demonstrates more disagreement in the labeling of PDs in comparison to seizures. This may be improved by education about standard EEG nomenclature.

SIGNAL REGULARITY-BASED AUTOMATED SEIZURE DETECTION SYSTEM FOR SCALP EEG MONITORING

The purpose of the present study was to build a clinically useful automated seizure detection system for scalp EEG recordings. To achieve this, a computer algorithm was designed to translate complex multichannel scalp EEG signals into several dynamical descriptors, followed by the investigations of their spatiotemporal properties that relate to the ictal (seizure) EEG patterns as well as to normal physiologic and artifact signals. This paper describes in detail this novel seizure detection algorithm and reports its performance in a large clinical dataset.

Assessment of a scalp EEG-based automated seizure detection system

OBJECTIVE

The purpose of this study was to evaluate and validate an offline, automated scalp EEG-based seizure detection system and to compare its performance to commercially available seizure detection software.

METHODS

The test seizure detection system, IdentEvent™, was developed to enhance the efficiency of post-hoc long-term EEG review in epilepsy monitoring units. It translates multi-channel scalp EEG signals into multiple EEG descriptors and recognizes ictal EEG patterns. Detection criteria and thresholds were optimized in 47 long-term scalp EEG recordings selected for training (47 subjects, ∼3653h with 141 seizures). The detection performance of IdentEvent was evaluated using a separate test dataset consisting of 436 EEG segments obtained from 55 subjects (∼1200h with 146 seizures). Each of the test EEG segments was reviewed by three independent epileptologists and the presence or absence of seizures in each epoch was determined by majority rule. Seizure detection sensitivity and false detection rate were calculated for IdentEvent as well as for the comparable detection software (Persyst's Reveal®, version 2008.03.13, with three parameter settings). Bootstrap re-sampling was applied to establish the 95% confidence intervals of the estimates and for the performance comparison between two detection algorithms.

RESULTS

The overall detection sensitivity of IdentEvent was 79.5% with a false detection rate (FDR) of 2 per 24h, whereas the comparison system had 80.8%, 76%, and 74% sensitivity using its three detection thresholds (perception score) with FDRs of 13, 8, and 6 per 24h, respectively. Bootstrap 95% confidence intervals of the performance difference revealed that the two detection systems had comparable detection sensitivity, but IdentEvent generated a significantly (p<0.05) smaller FDR.

CONCLUSIONS

The study validates the performance of the IdentEvent™ seizure detection system.

SIGNIFICANCE

With comparable detection sensitivity, an improved false detection rate makes the automated seizure detection software more useful in clinical practice.