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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Bibliographische Detailangaben
Veröffentlicht in:	Clinical neurophysiology 2016-09, Vol.127 (9), p.3014-3024
Hauptverfasser:	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:	Artikel
Sprache:	eng
Schlagworte:	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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Beschreibung
Zusammenfassung:	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