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arXiv Machine Learning By John M. O'Toole, Sean R. Mathieson, Aurel Luca, Soraia Ventura, Sean Griffin, Geraldine B. Boylan, Robert Hogan

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

Read the original on arXiv Machine Learning →

The study introduces low‑voltage intervals (LVIs) as an amplitude‑ and duration‑based marker of neonatal EEG discontinuity and develops a ConvNeXt AI model that detects LVIs with expert‑level accuracy (AUC = 0.996, MCC = 0.926). In a cohort of 444 neonates, LVI percentage strongly correlates with EEG background grades, moderately with hypoxic‑ischaemic encephalopathy severity, and predicts seizures (AUC = 0.872). The results demonstrate that automated LVI quantification can serve as a robust biomarker for neonatal encephalopathy and seizure risk.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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