ArticleDecision support systems2015
Clinical implementation of a neonatal seizure detection algorithm.
Article in Decision support systems, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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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
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Ethics, Integrity, and Retributions of Digital Detection Surveillance Systems for Infectious Diseases: Systematic Literature Review.Journal of medical Internet research · 2021Pooled it
- Toward automated neonatal EEG analysis: multi-center validation of a reliable deep learning pipeline.Frontiers in neuroscience · 2026Article
- Quantitative EEG in the neonatal intensive care unit: Current application and future promise.Annals of the Child Neurology Society · 2023Review
- Detection and Severity Identification of Neonatal Seizure Using Deep Convolutional Neural Networks from Multichannel EEG Signal.Pediatric health, medicine and therapeutics · 2023Article
- Building an Open Source Classifier for the Neonatal EEG Background: A Systematic Feature-Based Approach From Expert Scoring to Clinical Visualization.Frontiers in human neuroscience · 2021Article
- 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
- Machine learning in critical care: state-of-the-art and a sepsis case study.Biomedical engineering online · 2018Observational
- An agent based architecture for high-risk neonate management at neonatal intensive care unit.Electronic physician · 2018Article
- Toward a Personalized Real-Time Diagnosis in Neonatal Seizure Detection.IEEE journal of translational engineering in health and medicine · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Technologies for automated detection of neonatal seizures are gradually moving towards cot-side implementation. The aim of this paper is to present different ways to visualize the output of a neonatal seizure detection system and analyse their influence on performance in a clinical environment. Three different ways to visualize the detector output are considered: a binary output, a probabilistic trace, and a spatio-temporal colormap of seizure observability. As an alternative to visual aids, audified neonatal EEG is also considered. Additionally, a survey on the usefulness and accuracy of the presented methods has been performed among clinical personnel. The main advantages and disadvantages of the presented methods are discussed. The connection between information visualization and different methods to compute conventional metrics is established. The results of the visualization methods along with the system validation results indicate that the developed neonatal seizure detector with its current level of performance would unambiguously be of benefit to clinicians as a decision support system. The results of the survey suggest that a suitable way to visualize the output of neonatal seizure detection systems in a clinical environment is a combination of a binary output and a probabilistic trace. The main healthcare benefits of the tool are outlined. The decision support system with the chosen visualization interface is currently undergoing pre-market European multi-centre clinical investigation to support its regulatory approval and clinical adoption.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.