Automated cot-side tracking of functional brain age in preterm infants

Stevenson, Nathan J.; Oberdorfer, Lisa; Tataranno, Maria-Luisa; Breakspear, Michael; Colditz, Paul B.; Vries, Linda S. de; Benders, Manon J. N. L.; Klebermaß-Schrehof, Katrin; Vanhatalo, Sampsa; Roberts, James A.

doi:10.1101/848218

Cited by 1 publication

(2 citation statements)

References 72 publications

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“…Though it is easy to perform EEG monitors on neonates lasting for hours or even days at the bedside, the raw EEG signal data are usually very large, which takes experienced neurophysiologists several hours to interpret. Besides, that neonates' brain is developing complicates the evaluation of EEG pattern (4), especially among preterm neonates (5). O'Reilly designed a new EEG signal feature range EEG (rEEG) in 26 newborns with less than 29 weeks of gestational age and found that it is closely related to brain development and maturity (6).…”

Section: Introductionmentioning

confidence: 99%

“…O'Reilly designed a new EEG signal feature range EEG (rEEG) in 26 newborns with less than 29 weeks of gestational age and found that it is closely related to brain development and maturity (6). Similarly, Stevenson et al constructed a brain age prediction model based on EEG signal features from 65 preterm infants, which could greatly fit actual age and the predicted age difference could be used as a predictor of the neurodevelopmental outcome (5,7,8). These studies are all tested on small data sets, and there is no systematic analysis on how to apply the findings to the clinic.…”

Section: Introductionmentioning

confidence: 99%

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Development and validation of Auto-Neo-electroencephalography (EEG) to estimate brain age and predict report conclusion for electroencephalography monitoring data in neonatal intensive care units

Dong¹,

Kong²,

Xu³

et al. 2021

Ann Transl Med

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Background: Electroencephalography (EEG) monitoring is widely used in neonatal intensive care units (NICUs). However, conventional EEG report generation processes are time-consuming and labor-intensive.Therefore, an automatic, objective, and comprehensive pipeline for brain age estimation and EEG report conclusion prediction is urgently needed to assist clinician's decision-making. Methods:We recruited patients who underwent EEG monitoring from the NICU at Children's Hospital of Fudan University from Jan. 2016 to Mar. 2018. A total of 1,851 subjects were enrolled, including the patient's conceptional age (CA) and the clinical EEG report conclusion (normal, slightly abnormal, moderately abnormal, or severely abnormal). A total of 1,591 subjects were used to generate predictive models and 260 were used as the validation dataset. We developed Auto-Neo-EEG (an automatic prediction system to assist clinical neonatal EEG report generation), including signal feature extraction, supervised machine learning realized by gradient boosted models, to estimate brain age and predict EEG report conclusion. Results:The predicted results from the validation dataset were compared with the clinical observations to assess the performance. In the independent validation dataset, the model could achieve accordance 0.904 on estimating brain age for neonates with normal clinical EEG report conclusion, and differences between the predicted and observed brain age were strongly related with EEG report conclusion abnormality. Further, as for the EEG report conclusion prediction, the model could achieve area under the curve (AUC) of 0.984 for severely abnormal situations, and 0.857 for moderately abnormal ones. Conclusions:The Auto-Neo-EEG has the high accuracy of estimating brain age and EEG report conclusion, which can potentially greatly accelerate the EEG report generation processes assist in clinical decision making.

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Section: Introductionmentioning

confidence: 99%