Automated EEG sleep staging in the term-age baby using a generative modelling approach

Pillay, Kirubin; Dereymaeker, A; Jansen, Katrien; Naulaers, Gunnar; Huffel, Sabine Van; Vos, Maarten De

doi:10.1088/1741-2552/aaab73

Cited by 54 publications

(72 citation statements)

References 59 publications

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“…1  consisted of 89 EEG recordings from 26 patients with PMA range 27.3-42.0 weeks (median recording duration (IQR): 4 h 23 m (3 h 53 m-5 h 46 m), median number of recordings per patient: 3 (3-4)) and were recruited in the period February 2013 -September 2014. These were preselected based on a normal neurodevelopmental outcome at nine and 24 months of corrected age, clinically defined according to Pillay et al 32 and Dereymaeker et al 33 and based on the Bayley Scores of Infant mental and motor Development II (BSID-II).…”

Section: Methodsmentioning

confidence: 99%

“…Within each QS, non-QS, and full-cycle period, 113 different features were extracted from each channel in 30 second epochs. The majority of this feature set have also been used and defined in a previous study on automated sleep staging in term-age babies (see Pillay et al 32 ), and is summarised in Table 2. Further technical descriptions of these features are detailed in Appendix A.1.…”

Section: Methodsmentioning

confidence: 99%

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Applying a data-driven approach to quantify EEG maturational deviations in preterms with normal and abnormal neurodevelopmental outcomes

Pillay

Dereymaeker

Jansen

et al. 2020

Sci Rep

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Premature babies are subjected to environmental stresses that can affect brain maturation and cause abnormal neurodevelopmental outcome later in life. Better understanding this link is crucial to developing a clinical tool for early outcome estimation. We defined maturational trajectories between the electroencephalography (eeG)-derived 'brain-age' and postmenstrual age (the age since the last menstrual cycle of the mother) from longitudinal recordings during the baby's stay in the neonatal Intensive Care Unit. Data consisted of 224 recordings (65 patients) separated for normal and abnormal outcome at 9-24 months follow-up. Trajectory deviations were compared between outcome groups using the root mean squared error (RMSE) and maximum trajectory deviation (δ max). 113 features were extracted (per sleep state) to train a data-driven model that estimates brain-age, with the most prominent features identified as potential maturational and outcome-sensitive biomarkers. RMSE and δ max showed significant differences between outcome groups (cluster-based permutation test, p < 0.05). RMSE had a median (IQR) of 0.75 (0.60-1.35) weeks for normal outcome and 1.35 (1.15-1.55) for abnormal outcome, while δ max had a median of 0.90 (0.70-1.70) and 1.90 (1.20-2.90) weeks, respectively. Abnormal outcome trajectories were associated with clinically defined dysmature and disorganised EEG patterns, cementing the link between early maturational trajectories and neurodevelopmental outcome.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Applying a data-driven approach to quantify EEG maturational deviations in preterms with normal and abnormal neurodevelopmental outcomes

Pillay

Dereymaeker

Jansen

et al. 2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…Monitoring arousal or sleep state in neonates is routinely performed with electroencephalography (EEG; Dereymaeker et al, 2017;Pillay et al, 2018). However, introducing additional neuro-monitoring equipment into a study of an already challenging population can be time consuming and problematic.…”

Section: Introductionmentioning

confidence: 99%

Sleep State Modulates Resting-State Functional Connectivity in Neonates

et al. 2020

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The spontaneous cerebral activity that gives rise to resting-state networks (RSNs) has been extensively studied in infants in recent years. However, the influence of sleep state on the presence of observable RSNs has yet to be formally investigated in the infant population, despite evidence that sleep modulates resting-state functional connectivity in adults. This effect could be extremely important, as most infant neuroimaging studies rely on the neonate to remain asleep throughout data acquisition. In this study, we combine functional near-infrared spectroscopy with electroencephalography to simultaneously monitor sleep state and investigate RSNs in a cohort of healthy term born neonates. During active sleep (AS) and quiet sleep (QS) our newborn neonates show functional connectivity patterns spatially consistent with previously reported RSN structures. Our three independent functional connectivity analyses revealed stronger interhemispheric connectivity during AS than during QS. In turn, within hemisphere short-range functional connectivity seems to be enhanced during QS. These findings underline the importance of sleep state monitoring in the investigation of RSNs.

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“…All studies dealing with early days of preterm newborns were based on EEG analyses and only quiet sleep detection was performed [1315]. Only two studies dealing with preterm and full-term newborns proposed to identify four sleep states with EEG, but was focused on newborns at 38 to 42 weeks PMA [11,16].…”

Section: Resultsmentioning

confidence: 99%

Audio- and video-based estimation of the sleep stages of newborns in Neonatal Intensive Care Unit

Cabon

Porée

Simon

et al. 2019

Biomedical Signal Processing and Control

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Premature babies have several immature functions and begin their life under high medical supervision. Since the sleep organization diers across postmenstrual age, its analysis may give a good indication of the degree of brain maturation. However, sleep analysis (polysomnography or behavioral observation) is dicult to install, time consuming and cannot systematically be used. In this context, development of new ways to automatically monitor the neonates, using contactless modalities, is necessary. Therefore, this study presents an innovative non-invasive approach to semi-automatize the classication of infant behavioral sleep states. Methods First, three descriptors were extracted from audio and video recordings: vocalizations, motion and eye state of the baby. For this purpose, an original semi-automatic algorithm for the estimation of the eye state was proposed. Secondly, the three descriptors were used in order to obtain an estimation of the behavioral sleep states. Five classiers (K-Nearest Neighbors, Linear Discriminant Analysis, Support Vector Machine, Random Forest and Multi-Layer Perceptron) were compared to an expert annotation. Results Firstly, the comparison of the semi-automatic eye state estimation to manual annotations of 10 videos led to a mean accuracy of 99.4%. Secondly, sleep stage classication was performed. Best results were obtained with Random Forest, for Quiet Alert and Active Alert stages, with 93.5% and 99.0% of accuracy respectively. Conclusion The proposed method provides a high capacity to identify alert sleep stages but the dierentiation between Quiet Sleep and Active Sleep only by behavioral observations still remains a dicult task to achieve. Signicance Results presented in this paper are new since no similar approach was proposed in the literature in the context of neonatal intensive care unit. They augur well for the automatic sleep organization assessment to improve newborn care.

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Automated EEG sleep staging in the term-age baby using a generative modelling approach

Cited by 54 publications

References 59 publications

Applying a data-driven approach to quantify EEG maturational deviations in preterms with normal and abnormal neurodevelopmental outcomes

Applying a data-driven approach to quantify EEG maturational deviations in preterms with normal and abnormal neurodevelopmental outcomes

Sleep State Modulates Resting-State Functional Connectivity in Neonates

Audio- and video-based estimation of the sleep stages of newborns in Neonatal Intensive Care Unit

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