Expert-level detection of low-voltage intervals in EEG as a robust biomarker of neonatal encephalopathy
Abstract
Background: Discontinuity in neonatal EEG is a critical marker of brain dysfunction but can be difficult to quantify visually. Inter-burst intervals (IBIs) are a key characteristic of discontinuity, yet their definition does not easily translate to AI detection. Objectives: To introduce low-voltage intervals (LVIs), defined exclusively by amplitude and duration, as a context-independent alternative to IBIs, develop an AI detection method for LVIs, and assess their clinical utility as a marker of brain injury. Methods: We developed a ConvNeXt model to detect LVIs and compared its performance with existing methods. We then tested the clinical relevance of detected LVIs across four applications: 1) grading background EEG abnormalities on an open benchmark dataset, 2) classifying clinical hypoxic-ischaemic encephalopathy (HIE) grades and comparing with a normal EEG dataset, 3) seizure prediction, and 4) association with seizure burden. Results: For training and validation, 654 hours of per-channel EEG from 123 neonates were reviewed and 51,162 LVIs were annotated. On held-out validation, ConvNeXt had an AUC of 0.996 and a Matthews correlation coefficient of 0.926. Model-reader agreement exceeded inter-reader agreement (Δκ= 0.025, 95% CI: 0.007 to 0.045, p<0.001), attaining expert-level equivalence. On a combined dataset of 444 neonates, percentage LVI showed a strong association with EEG grades (Spearman's ρ=0.801), a moderate association with HIE grades (ρ=0.384), a weaker association with seizure burden (ρ=0.244), and a strong seizure prediction capacity (AUC =0.872). Conclusion: AI detection of LVIs achieves expert-level equivalence, whereas quantitative features do not. Automated LVI quantification provides a robust clinical biomarker of neonatal encephalopathy associated with background EEG activity, HIE severity, and seizure risk.
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BibTeX
@misc{otoole2026expertlevel,
title = {{Expert-level detection of low-voltage intervals in EEG as a robust biomarker of neonatal encephalopathy}},
author = {John M. O'Toole and Sean R. Mathieson and Aurel Luca and Soraia Ventura and Sean Griffin and Geraldine B. Boylan and Robert Hogan},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.12680},
eprint = {2610.12680},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.12680},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Expert-level detection of low-voltage intervals in EEG as a robust biomarker of neonatal encephalopathy
AU - John M. O'Toole
AU - Sean R. Mathieson
AU - Aurel Luca
AU - Soraia Ventura
AU - Sean Griffin
AU - Geraldine B. Boylan
AU - Robert Hogan
PY - 2026
DA - 2026/10/08
PB - arXiv
DO - 10.48550/arxiv.2610.12680
UR - https://arxiv.org/abs/2610.12680
AB - Background: Discontinuity in neonatal EEG is a critical marker of brain dysfunction but can be difficult to quantify visually. Inter-burst intervals (IBIs) are a key characteristic of discontinuity, yet their definition does not easily translate to AI detection. Objectives: To introduce low-voltage intervals (LVIs), defined exclusively by amplitude and duration, as a context-independent alternative to IBIs, develop an AI detection method for LVIs, and assess their clinical utility as a marker of brain injury. Methods: We developed a ConvNeXt model to detect LVIs and compared its performance with existing methods. We then tested the clinical relevance of detected LVIs across four applications: 1) grading background EEG abnormalities on an open benchmark dataset, 2) classifying clinical hypoxic-ischaemic encephalopathy (HIE) grades and comparing with a normal EEG dataset, 3) seizure prediction, and 4) association with seizure burden. Results: For training and validation, 654 hours of per-channel EEG from 123 neonates were reviewed and 51,162 LVIs were annotated. On held-out validation, ConvNeXt had an AUC of 0.996 and a Matthews correlation coefficient of 0.926. Model-reader agreement exceeded inter-reader agreement (Δκ= 0.025, 95% CI: 0.007 to 0.045, p<0.001), attaining expert-level equivalence. On a combined dataset of 444 neonates, percentage LVI showed a strong association with EEG grades (Spearman's ρ=0.801), a moderate association with HIE grades (ρ=0.384), a weaker association with seizure burden (ρ=0.244), and a strong seizure prediction capacity (AUC =0.872). Conclusion: AI detection of LVIs achieves expert-level equivalence, whereas quantitative features do not. Automated LVI quantification provides a robust clinical biomarker of neonatal encephalopathy associated with background EEG activity, HIE severity, and seizure risk.
ER -