Automated EEG sleep staging in the term-age baby using a Generative Modelling approach

Journal article

Objective

We develop a method for automated four-state sleep classification of preterm and term-born babies at term-age of 38–40 weeks postmenstrual age (the age since the last menstrual cycle of the mother) using multichannel electroencephalogram (EEG) recordings. At this critical age, EEG differentiates from broader quiet sleep (QS) and active sleep (AS) stages to four, more complex states, and the quality and timing of this differentiation is indicative of the level of brain development. However, existing methods for automated sleep classification remain focussed only on QS and AS sleep classification.

Approach

EEG features were calculated from 16 EEG recordings, in 30 s epochs, and personalized feature scaling used to correct for some of the inter-recording variability, by standardizing each recording’s feature data using its mean and standard deviation. Hidden Markov models (HMMs) and Gaussian mixture models (GMMs) were trained, with the HMM incorporating knowledge of the sleep state transition probabilities. Performance of the GMM and HMM (with and without scaling) were compared, and Cohen’s kappa agreement calculated between the estimates and clinicians’ visual labels.

Main results

For four-state classification, the HMM proved superior to the GMM. With the inclusion of personalized feature scaling, mean kappa (±standard deviation) was 0.62 (±0.16) compared to the GMM value of 0.55 (±0.15). Without feature scaling, kappas for the HMM and GMM dropped to 0.56 (±0.18) and 0.51 (±0.15), respectively.

Significance

This is the first study to present a successful method for the automated staging of four states in term-age sleep using multichannel EEG. Results suggested a benefit in incorporating transition information using an HMM, and correcting for inter-recording variability through personalized feature scaling. Determining the timing and quality of these states are indicative of developmental delays in both preterm and term-born babies that may lead to learning problems by school age.

Authors: Pillay, K; Dereymaeker, A; Jansen, K; Naulaers, G; Van Huffel, S

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Institute of Physics
• Journal: Journal of Neural Engineering
• Volume: 15
• Pages: 036004
• Publication date: 2018-02-27
• Acceptance date: 2018-01-30
• DOI: 10.1088/1741-2552/aaab73
• EISSN: 1741-2552
• ISSN: 1741-2560
• Keywords: EEG; Gaussian mixture models; automated sleep staging; generative models; term newborn; hidden markov models; preterm newborn