GROND: Automated characterization of periodic and rhythmic EEG patterns.

Abstract

Objective: Periodic discharges (PDs) and rhythmic delta activity (RDA) are common EEG patterns in critically ill patients that require characterization of lateralization, spatial localization, and frequency. Manual characterization is subjective and time-consuming. We present an automated system for all four major subtypes: lateralized and generalized PDs (LPD, GPD) and lateralized and generalized RDA (LRDA, GRDA). Approach. The PD-Profiler combines a per-channel convolutional neural network, hemisphere-specific learned evidence traces, dynamic programming with an approximately periodic prior, and discharge-locked topographic localization. The RDA-Profiler uses iterative narrowband Hilbert refinement for frequency and lateralization, with phase-locking-value analysis for spatial extent. Both pipelines were trained and evaluated on 12,425 EEG segments from 11,729 patients. Main results. Four electroencephalographers independently scored 200 stratified segments per subtype. The algorithm exceeded expert-expert (EE) agreement on LPD frequency (expert-algorithm (EA) ICC 0.931 versus EE 0.897; p<0.001) and was statistically indistinguishable from EE on GPD frequency (EA 0.980 versus EE 0.973; p=0.061), GRDA frequency (p=0.056), LRDA frequency (p=0.435), LPD laterality (EA kappa 0.945 versus EE 0.974; p=0.079), and LRDA laterality (EA kappa 0.961 versus EE 0.993; p=0.269). No attribute fell significantly below the EE ceiling. PD discharge timing achieved an F1 of 0.817 at single-sample precision (mean absolute timing error 3.1 ms). PD spatial localization reached 97.3% of expert inter-rater Jaccard agreement, and RDA spatial extent matched EE agreement. Significance. GROND reaches expert-level reliability across lateralization, spatial localization, discharge timing, and frequency for PDs and RDA. In discordant cases, experts judged its frequency estimates more accurate than the original labels in 94% of cases, supporting reproducible characterization at scale.