An Automated System for Grading EEG Abnormality in Term Neonates with Hypoxic-Ischaemic Encephalopathy

• Published in: Annals of biomedical engineering Vol. 41; no. 4; pp. 775 - 785
• Main Authors: Stevenson, N. J., Korotchikova, I., Temko, A., Lightbody, G., Marnane, W. P., Boylan, G. B.
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.04.2013; Springer Nature B.V
• Subjects: Biochemistry; Biological and Medical Physics; Biomedical and Life Sciences; Biomedical Engineering; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; Classical Mechanics; Diagnosis, Computer-Assisted - statistics & numerical data; Electroencephalography - classification; Electroencephalography - statistics & numerical data; Humans; Hypoxia-Ischemia, Brain - diagnosis; Infant, Newborn; Linear Models; Monitoring, Physiologic - statistics & numerical data; Signal Processing, Computer-Assisted; Time Factors; Hypoxic-ischaemic encephalopathy; Wigner-Ville distribution; Newborn; Neonate; Background; EEG; Electroencephalography; Multi-class linear discriminant classifier; Automated EEG grading system
• ISSN: 0090-6964; 1573-9686
• DOI: 10.1007/s10439-012-0710-5

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.