{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.12680",
  "author_total": 7,
  "authors": [
    {
      "name": "John M. O'Toole"
    },
    {
      "name": "Sean R. Mathieson"
    },
    {
      "name": "Aurel Luca"
    },
    {
      "name": "Soraia Ventura"
    },
    {
      "name": "Sean Griffin"
    },
    {
      "name": "Geraldine B. Boylan"
    },
    {
      "name": "Robert Hogan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2kzgmppkznj11.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.12680",
  "fulltext_url": null,
  "id": "2kzgmppkznj11",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.12680",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "eess.SP"
    ],
    [
      "arxiv",
      "physics.med-ph"
    ],
    [
      "openalex",
      "2735|Neonatal and fetal brain pathology"
    ]
  ],
  "openalex": "W7221517876",
  "pdf_url": "https://arxiv.org/pdf/2610.12680",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2kzgmppkznj11.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:28:36Z",
      "native_id": "2610.12680",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.12680"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.med-ph",
      "subfield": "biological-medical"
    }
  ],
  "title": "Expert-level detection of low-voltage intervals in EEG as a robust biomarker of neonatal encephalopathy",
  "title_text": "Expert-level detection of low-voltage intervals in EEG as a robust biomarker of neonatal encephalopathy",
  "type": "preprint",
  "updated_at": "2026-10-30T12:28:36Z",
  "url": "https://research.draconitic.com/paper/2kzgmppkznj11",
  "venue": "arXiv",
  "versions": []
}