GROND: Automated Characterization of Periodic and Rhythmic EEG Patterns

Résumé fourni par la source

**Objective.** Periodic discharges (PDs) and rhythmic delta activity (RDA) are common electroencephalographic (EEG) patterns in critically ill patients that require detailed characterization -- including lateralization, spatial localization, and frequency estimation -- according to the American Clinical Neurophysiology Society (ACNS) 2021 standardized terminology. Manual characterization is subjective, time-consuming, and exhibits substantial inter-rater variability. This project presents GROND (Generalized Rhythmic and Oscillatory Neurophysiology Descriptor), a comprehensive automated system for characterizing all four major subtypes: lateralized periodic discharges (LPD), generalized periodic discharges (GPD), lateralized rhythmic delta activity (LRDA), and generalized rhythmic delta activity (GRDA). **Approach.** Two complementary pipelines were developed. The **PD-Profiler** combines a per-channel convolutional neural network (ChannelPD-Net) with hemisphere-specific learned evidence traces (HemiCET-UNet), dynamic programming under an approximately-periodic prior, and discharge-locked topographic localization. The **RDA-Profiler** uses iterative narrowband Hilbert refinement (NB-Hilbert) for frequency and lateralization, with phase- locking value (PLV) analysis for spatial extent. Both pipelines were trained and evaluated on 12,425 EEG segments from 11,729 unique patients. **Main results.** Four electroencephalographers -- three not involved in algorithm development, plus the primary annotator -- independently scored 200 stratified segments per pattern subtype, with analysis restricted to segments accepted by a majority of the four raters. The algorithm _exceeded_ expert -expert (EE) inter-rater agreement on LPD frequency (mean expert-algorithm intraclass correlation coefficient (ICC) 0.931 vs. EE 0.897; paired segment- bootstrap Δ = +0.034, 95% CI [+0.016, +0.055], p < 0.001), and was statistically indistinguishable from EE on every other attribute tested: GPD frequency (p = 0.061), GRDA frequency (p = 0.056), LRDA frequency (p = 0.435), LPD laterality (p = 0.079) and LRDA laterality (mean expert-algorithm κ 0.961 vs. EE 0.993; p = 0.269). No attribute fell significantly below the expert- expert ceiling. PD discharge timing achieved an F1 of 0.817 at single-sample precision (mean absolute timing error 3.1 ms at 200 Hz). Lateralization area under the receiver operating characteristic curve (AUC) was 0.989 for PD hemisphere, 0.909 for LPD vs. GPD, and 0.837 for RDA. PD spatial localization reached 97.3% of expert inter-rater Jaccard agreement. **Significance.** This is the first system to reach expert-level inter-rater reliability across lateralization, spatial localization, discharge timing and frequency for both periodic and rhythmic EEG patterns. In post-hoc review of discordant cases, experts judged the algorithm's frequency estimates more accurate than the original expert labels in 94% of cases. Automated characterization can now both substitute for and improve manual annotation in critical-care EEG.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.