Improved multi-stage neonatal seizure detection using a heuristic classifier and a data-driven post-processor

Highlights • An improved neonatal seizure detection method is discussed. • A set of characteristic features of seizures are identified by data-driven methods. • Described core characteristics of neonatal seizures can easily be used for other automated methods.

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Bibliographic Details
Published in:Clinical neurophysiology 2016-09, Vol.127 (9), p.3014-3024
Main Authors: Ansari, A.H, Cherian, P.J, Dereymaeker, A, Matic, V, Jansen, K, De Wispelaere, L, Dielman, C, Vervisch, J, Swarte, R.M, Govaert, P, Naulaers, G, De Vos, M, Van Huffel, S
Format: Article
Language:English
Subjects:
Automated neonatal seizure detection
Databases, Factual - standards
Electroencephalography - methods
Electroencephalography - standards
Heuristics - physiology
Humans
Hypoxic-ischemic encephalopathy
Infant, Newborn
Infant, Newborn, Diseases - diagnosis
Infant, Newborn, Diseases - physiopathology
Machine learning
Neurology
Retrospective Studies
Seizures - diagnosis
Seizures - physiopathology
Support Vector Machine - standards
Support vector machines
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Description
Summary:Highlights • An improved neonatal seizure detection method is discussed. • A set of characteristic features of seizures are identified by data-driven methods. • Described core characteristics of neonatal seizures can easily be used for other automated methods.
ISSN:1388-2457
1872-8952
DOI:10.1016/j.clinph.2016.06.018