Improved multi-stage neonatal seizure detection using a heuristic classifier and a data-driven post-processor
| Autor: | Amir Hossein Ansari, Katrien Jansen, Charlotte Dielman, Paul Govaert, Anneleen Dereymaeker, S. Van Huffel, L. De Wispelaere, Vladimir Matic, Renate Swarte, P.J. Cherian, Jan Vervisch, M. De Vos, Gunnar Naulaers |
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| Přispěvatelé: | Neurology, Pediatrics |
| Rok vydání: | 2016 |
| Předmět: |
Support Vector Machine
Databases Factual Heuristic (computer science) Computer science 0206 medical engineering 02 engineering and technology Infant Newborn Diseases Data-driven Constant false alarm rate 03 medical and health sciences 0302 clinical medicine Post processor Seizures Physiology (medical) Heuristics Humans Neonatal seizure Retrospective Studies SISTA business.industry Infant Newborn Electroencephalography Pattern recognition 020601 biomedical engineering Sensory Systems Support vector machine Neurology Neurology (clinical) Artificial intelligence business Classifier (UML) 030217 neurology & neurosurgery |
| Zdroj: | Clinical Neurophysiology, 127(9), 3014-3024. Elsevier Ireland Ltd |
| ISSN: | 1872-8952 1388-2457 |
| Popis: | OBJECTIVE: After identifying the most seizure-relevant characteristics by a previously developed heuristic classifier, a data-driven post-processor using a novel set of features is applied to improve the performance. METHODS: The main characteristics of the outputs of the heuristic algorithm are extracted by five sets of features including synchronization, evolution, retention, segment, and signal features. Then, a support vector machine and a decision making layer remove the falsely detected segments. RESULTS: Four datasets including 71 neonates (1023h, 3493 seizures) recorded in two different university hospitals, are used to train and test the algorithm without removing the dubious seizures. The heuristic method resulted in a false alarm rate of 3.81 per hour and good detection rate of 88% on the entire test databases. The post-processor, effectively reduces the false alarm rate by 34% while the good detection rate decreases by 2%. CONCLUSION: This post-processing technique improves the performance of the heuristic algorithm. The structure of this post-processor is generic, improves our understanding of the core visually determined EEG features of neonatal seizures and is applicable for other neonatal seizure detectors. SIGNIFICANCE: The post-processor significantly decreases the false alarm rate at the expense of a small reduction of the good detection rate. publisher: Elsevier articletitle: Improved multi-stage neonatal seizure detection using a heuristic classifier and a data-driven post-processor journaltitle: Clinical Neurophysiology articlelink: http://dx.doi.org/10.1016/j.clinph.2016.06.018 content_type: article copyright: © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. ispartof: Clinical Neurophysiology vol:127 issue:9 pages:3014-3024 ispartof: location:Netherlands status: published |
| Databáze: | OpenAIRE |
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