Validation of a New AI Seizure Detection Algorithm for Pre-term Neonates, Term Neonates and Infants

作者：A. K. Gupta, Leonardo Tozzi, Tanaya Puranik, Suma Anand, Courtney Juliano, Maite La Vega-Talbott, Rana Said, Avantika Singh, Joseph Sullivan, Nicholas S. Abend, Cecil D. Hahn, Baharan Kamousi · 发表于：medRxiv · 年份：2025 · DOI：10.1101/2025.10.31.25339264 · 被引用次数：1 · 研究领域：Neonatal and fetal brain pathology、EEG and Brain-Computer Interfaces、ECG Monitoring and Analysis

Abstract Seizures are frequent in the first year of life and they are associated with increased risk of long-term neurological disability. Accurate diagnosis of seizures in neonates and infants requires continuous electroencephalogram (EEG) monitoring with expert interpretation. While rapid detection of seizures allows timely management and may influence neurodevelopmental outcomes, it may not be feasible in settings without round-the-clock expert interpretation. We evaluated the real-world performance of Clarity (Ceribell Inc.), a novel algorithm for automated seizure detection from EEG of preterm neonates, term neonates, and infants in a validation dataset of patients of postnatal age 0-1 years and gestational age 22-42 weeks who underwent continuous EEG monitoring at three hospitals in the United States. We quantified algorithm performance using the following metrics: (1) area under the curve for classification of 10-second EEG segments as seizure or no seizure; (2) sensitivity and false positives in detecting seizure episodes; and (3) sensitivity, specificity and negative/positive predictive value in identifying recordings where seizure duration reached or exceeded predetermined thresholds. The validation dataset consisted of 756 EEG recordings with a cumulative duration of 5230 hours (median: 2.94 hours, interquartile interval: 1.46-12.62 hours) from 167 preterm neonates, 323 term neonates, and 266 infants. A total of 568 seizures occurred in 75 recordings, with a cumula...