Grading hypoxic–ischemic encephalopathy severity in neonatal EEG using GMM supervectors and the support vector machine

Grading hypoxic–ischemic encephalopathy severity in neonatal EEG using GMM supervectors and the support vector machine

Highlights • An automated system for grading hypoxic–ischemic encephalopathy (HIE) severity using EEG is presented. • The classification approach is based on long-term statistical model based features. • The proposed system could act as a decision support system to assist health care professionals i...

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Veröffentlicht in:Clinical neurophysiology 2016-01, Vol.127 (1), p.297-309
Hauptverfasser: Ahmed, Rehan, Temko, Andriy, Marnane, William, Lightbody, Gordon, Boylan, Geraldine
Format: Artikel
Sprache:eng
Schlagworte:
Automated neonatal HIE EEG grading system
EEG
EEG analysis algorithms
Electroencephalography - classification
Electroencephalography - methods
Female
Gaussian mixture models
Humans
Hypoxia-Ischemia, Brain - classification
Hypoxia-Ischemia, Brain - diagnosis
Hypoxia-Ischemia, Brain - physiopathology
Hypoxic–ischemic encephalopathy
Infant, Newborn
Long term EEG features
Male
Neonatal EEG
Neurology
Support vector machine
Support Vector Machine - classification
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Zusammenfassung:Highlights • An automated system for grading hypoxic–ischemic encephalopathy (HIE) severity using EEG is presented. • The classification approach is based on long-term statistical model based features. • The proposed system could act as a decision support system to assist health care professionals in NICUs.
ISSN:1388-2457
1872-8952
DOI:10.1016/j.clinph.2015.05.024