ArticleClinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology2016
Validation of an automated seizure detection algorithm for term neonates.
Article in Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- A machine-learning algorithm for neonatal seizure recognition: a multicentre, randomised, controlled trial.The Lancet. Child & adolescent health · 2020Trial
- Validation of a novel Bayesian predictive algorithm for detection of carbon dioxide retention using retrospective neonatal ICU data.Journal of perinatology : official journal of the California Perinatal Association · 2026Article
- Short-horizon neonatal seizure prediction using EEG-based deep learning.PLOS digital health · 2025Article
- Optimal channel and feature selection for automatic prediction of functional brain age of preterm infant based on EEG.Frontiers in neuroscience · 2025Article
- Analysis of the impact of deep learning know-how and data in modelling neonatal EEG.Scientific reports · 2024Article
- Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities.Journal of perinatology : official journal of the California Perinatal Association · 2024Review
- Quantifying Sharpness and Nonlinearity in Neonatal Seizure Dynamics.Cyborg and bionic systems (Washington, D.C.) · 2024Article
- Questions and Controversies in Neonatal Seizures.Children (Basel, Switzerland) · 2023Review
- The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review.NPJ digital medicine · 2023Review
- A Review on Machine Learning Approaches in Identification of Pediatric Epilepsy.SN computer science · 2022Article
- Development and validation of Auto-Neo-electroencephalography (EEG) to estimate brain age and predict report conclusion for electroencephalography monitoring data in neonatal intensive care units.Annals of translational medicine · 2021Article
- Precision Medicine in Neonates: A Tailored Approach to Neonatal Brain Injury.Frontiers in pediatrics · 2021Review
- Current Status and Future Directions of Neuromonitoring With Emerging Technologies in Neonatal Care.Frontiers in pediatrics · 2021Review
- Attention-Based Network for Weak Labels in Neonatal Seizure Detection.Proceedings of machine learning research · 2020Article
- Applications of advanced signal processing and machine learning in the neonatal hypoxic-ischemic electroencephalogram.Neural regeneration research · 2020Article
- Characterisation of neonatal seizures and their treatment using continuous EEG monitoring: a multicentre experience.Archives of disease in childhood. Fetal and neonatal edition · 2019Article
- Network dynamics in the healthy and epileptic developing brain.Network neuroscience (Cambridge, Mass.) · 2018Article
- Toward a Personalized Real-Time Diagnosis in Neonatal Seizure Detection.IEEE journal of translational engineering in health and medicine · 2017Article
- Phenobarbital reduces EEG amplitude and propagation of neonatal seizures but does not alter performance of automated seizure detection.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2016Article
- Monitoring of newborns at high risk for brain injury.Italian journal of pediatrics · 2016Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectiveThe objective of this study was to validate the performance of a seizure detection algorithm (SDA) developed by our group, on previously unseen, prolonged, unedited EEG recordings from 70 babies from 2 centres.
methodsEEGs of 70 babies (35 seizure, 35 non-seizure) were annotated for seizures by experts as the gold standard. The SDA was tested on the EEGs at a range of sensitivity settings. Annotations from the expert and SDA were compared using event and epoch based metrics. The effect of seizure duration on SDA performance was also analysed.
resultsBetween sensitivity settings of 0.5 and 0.3, the algorithm achieved seizure detection rates of 52.6-75.0%, with false detection (FD) rates of 0.04-0.36FD/h for event based analysis, which was deemed to be acceptable in a clinical environment. Time based comparison of expert and SDA annotations using Cohen's Kappa Index revealed a best performing SDA threshold of 0.4 (Kappa 0.630). The SDA showed improved detection performance with longer seizures.
conclusionThe SDA achieved promising performance and warrants further testing in a live clinical evaluation. SIGNIFICANCE: The SDA has the potential to improve seizure detection and provide a robust tool for comparing treatment regimens.
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