title: An automated system for grading EEG abnormality in term neonates with hypoxic-ischaemic encephalopathy.
abstract: Automated analysis of the neonatal EEG has the potential to assist clinical decision making for neonates with hypoxic-ischaemic encephalopathy. This paper proposes a method of automatically grading the degree of abnormality in an hour long epoch of neonatal EEG. The automated grading system (AGS) was based on a multi-class linear classifier grading of short-term epochs of EEG which were converted into a long-term grading of EEG using a majority vote operation. The features used in the AGS were summary measurements of two sub-signals extracted from a quadratic time-frequency distribution: the amplitude modulation and instantaneous frequency. These sub-signals were based on a model of EEG as a multiplication of a coloured random process with a slowly varying pseudo-periodic waveform and may be related to macroscopic neurophysiological function. The 4 grade AGS had a classification accuracy of 83% compared to human annotation of the EEG (level of agreement, κ = 0.76). Features estimated on the developed sub-signals proved more effective at grading the EEG than measures based solely on the EEG and the incorporation of additional sub-grades based on EEG states into the AGS also improved performance.
authors.affiliation: Neonatal Brain Research Group, University College Cork, Cork, Ireland. n.stevenson@ucc.ie
authors.forename: N J
authors.initials: NJ
authors.lastname: Stevenson
authors.forename: I
authors.initials: I
authors.lastname: Korotchikova
authors.forename: A
authors.initials: A
authors.lastname: Temko
authors.forename: G
authors.initials: G
authors.lastname: Lightbody
authors.forename: W P
authors.initials: WP
authors.lastname: Marnane
authors.forename: G B
authors.initials: GB
authors.lastname: Boylan
mesh_terms: D001698:Biomedical Engineering; D003936:Diagnosis, Computer-Assisted / Q000706:statistics & numerical data*; D004569:Electroencephalography / Q000145:classification / Q000706:statistics & numerical data*; D006801:Humans; D020925:Hypoxia-Ischemia, Brain / Q000175:diagnosis*; D007231:Infant, Newborn; D016014:Linear Models; D008991:Monitoring, Physiologic / Q000706:statistics & numerical data; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors