Journal
ANNALS OF BIOMEDICAL ENGINEERING
Volume 41, Issue 4, Pages 775-785Publisher
SPRINGER
DOI: 10.1007/s10439-012-0710-5
Keywords
Electroencephalography; EEG; Newborn; Neonate; Background; Wigner-Ville distribution; Multi-class linear discriminant classifier; Hypoxic-ischaemic encephalopathy; Automated EEG grading system
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Funding
- Science Foundation Ireland [10/IN.1/B3036]
- Science Foundation Ireland (SFI) [10/IN.1/B3036] Funding Source: Science Foundation Ireland (SFI)
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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, kappa = 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.
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