ArticleIEEE journal of translational engineering in health and medicine2017
Toward a Personalized Real-Time Diagnosis in Neonatal Seizure Detection.
Article in IEEE journal of translational engineering in health and medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- TATPat based explainable EEG model for neonatal seizure detection.Scientific reports · 2024Article
- The development of neonatal neurointensive care.Pediatric research · 2024Review
- Pediatric Seizure Prediction in Scalp EEG Using a Multi-Scale Neural Network With Dilated Convolutions.IEEE journal of translational engineering in health and medicine · 2022Article
- Current Status and Future Directions of Neuromonitoring With Emerging Technologies in Neonatal Care.Frontiers in pediatrics · 2021Review
- Prognostic Value of Continuous Electroencephalogram Delta Power in Neonates With Hypoxic-Ischemic Encephalopathy.Journal of child neurology · 2020Article
- Applications of advanced signal processing and machine learning in the neonatal hypoxic-ischemic electroencephalogram.Neural regeneration research · 2020Article
- Monitoring Circulation During Transition in Extreme Low Gestational Age Newborns: What's on the Horizon?Frontiers in pediatrics · 2018Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The problem of creating a personalized seizure detection algorithm for newborns is tackled in this paper. A probabilistic framework for semi-supervised adaptation of a generic patient-independent neonatal seizure detector is proposed. A system that is based on a combination of patient-adaptive (generative) and patient-independent (discriminative) classifiers is designed and evaluated on a large database of unedited continuous multichannel neonatal EEG recordings of over 800 h in duration. It is shown that an improvement in the detection of neonatal seizures over the course of long EEG recordings is achievable with on-the-fly incorporation of patient-specific EEG characteristics. In the clinical setting, the employment of the developed system will maintain a seizure detection rate at 70% while halving the number of false detections per hour, from 0.4 to 0.2 FD/h. This is the first study to propose the use of online adaptation without clinical labels, to build a personalized diagnostic system for the detection of neonatal seizures.
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Registered trials
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