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:
- grading background EEG abnormalities on an open benchmark dataset,
- classifying clinical hypoxic-ischaemic encephalopathy (HIE) grades and comparing with a normal EEG dataset,
- seizure prediction, and
- 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.