# Expert-level detection of low-voltage intervals in EEG as a robust biomarker of neonatal encephalopathy

arXiv: https://arxiv.org/abs/2610.12680 · HTML: https://mlsanity.com/paper/2610.12680/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 46 references that are in the map.
- First posted on arXiv 8 Oct 2026.

## Builds on (most cited first, 9 of 9)

- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Scaling convolutional neural networks achieves expert-level seizure detection in neonatal EEG](https://mlsanity.com/paper/2405.09911/) (2024)
- [NEURAL: quantitative features for newborn EEG using Matlab](https://mlsanity.com/paper/1704.05694/) (2017)
- [Validating an SVM-based neonatal seizure detection algorithm for generalizability, non-inferiority and clinical efficacy](https://mlsanity.com/paper/2202.12023/) (2022)
- [Suitability of an inter-burst detection method for grading hypoxic-ischemic encephalopathy in newborn EEG](https://mlsanity.com/paper/1907.02877/) (2019)
- [Identifying trace alternant activity in neonatal EEG using an inter-burst detection approach](https://mlsanity.com/paper/2005.05559/) (2020)
- [Neonatal EEG graded for severity of background abnormalities in hypoxic-ischaemic encephalopathy](https://mlsanity.com/paper/2206.04420/) (2022)
- [Honest and Reliable Evaluation and Expert Equivalence Testing of Automated Neonatal Seizure Detection](https://mlsanity.com/paper/2508.04899/) (2025)

## Abstract (arXiv)

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 (Delta kappa = 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.