Year: 2026
Type: article
Abstract: Objective. Periodic discharges (PDs) and rhythmic delta activity (RDA) are common electroencephalographic patterns in critically ill patients that require detailed characterization—including lateralization, spatial localization, and frequency estimation—according to the American Clinical Neurophysiology Society 2021 standardized terminology. Manual characterization is subjective, time-consuming, and exhibits substantial inter-rater variability. This study presents a comprehensive automated system for characterizing all four major subtypes: lateralized PDs (LPDs), generalized PDs (GPD), lateralized RDA (LRDA), and generalized RDA (GRDA). Approach. Two complementary pipelines were developed: the PD-Profiler and the RDA-Profiler. The PD-Profiler combines a per-channel convolutional neural network (Channelpd-Net) with hemisphere-specific learned evidence traces (HemiCET-UNet), 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 unique patients. Main results. Four electroencephalographers—three (SZ, TZ, AS) not involved in algorithm development, plus the primary annotator (MW) — 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 (EA) intraclass correlation coefficient (ICC) 0.931 vs. EE 0.897; paired segment-bootstrap Δ = + 0.034 , 95% confidence interval [ + 0.016 , + 0.055 ] , p < 0.001 ), and was statistically indistinguishable from EE on every other attribute tested: GPD frequency (EA ICC 0.980 vs. EE 0.973; Δ = + 0.007 , [ − 0.000 , + 0.017 ] , p = 0.061 ), GRDA frequency ( Δ = − 0.020 , p = 0.056 ), LRDA frequency ( Δ = − 0.055 , p = 0.435 ), LPD laterality (EA κ 0.945 vs. EE 0.974; Δ = − 0.030 , p = 0.079
Source: Journal of Neural Engineering
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $3,325
Awards: Comparative effectiveness of EEG-guided anti-seizure treatment in acute brain injury, Late-onset Unexplained Epilepsy as a Risk Factor for Dementia, Low Neurophysiologic Resistance to Anesthetics as a Marker of Preclinical/Prodromal Alzheimer's Disease and Neurovascular Pathology, Delirium risk and Inattention, Comparative Safety of Seizure Prophylaxis within the Medicare Program, Data-Driven Sleep Biomarkers of Brain Health, Heart Health, and Mortality, Prospective Validation of Neurophysiologic Outcome Prediction in Acute Brain Injury
OpenAlex ID: W7203690944