arXiv：cs.LG· John M. O'Toole, Sean R. Mathieson, Aurel Luca, Soraia Ventura, Sean Griffin, Geraldine B. Boylan, Robert Hogan·· 11 小时前
用 ConvNeXt 检测新生儿 EEG 低电压间期，实现专家级新生儿脑病生物标志物识别
Expert-level detection of low-voltage intervals in EEG as a robust biomarker of neonatal encephalopathy
AI 导读
研究提出仅由幅度和时长定义的低电压间期（LVI），并训练 ConvNeXt 模型进行检测，在 123 名新生儿 654 小时逐通道 EEG、51,162 个 LVI 标注上验证，AUC 达 0.996。
正文
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 ($\Delta \kappa = 0.025$, 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 $\rho=0.801$), a moderate association with HIE grades ($\rho=0.384$), a weaker association with seizure burden ($\rho=0.244$), 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.
| Comments: | 22 pages, 8 figures, and 9 tables including the appendix. Under review |
| Subjects: | Machine Learning (cs.LG); Signal Processing (eess.SP); Medical Physics (physics.med-ph) |
| ACM classes: | I.2.1 |
| Cite as: | arXiv:2610.12680 [cs.LG] |
| (or arXiv:2610.12680v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12680 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: John M. O'Toole [view email]
[v1]
Thu, 8 Oct 2026 20:04:08 UTC (3,993 KB)
来源：arXiv：cs.LG · arxiv.org