Extraction of features from sleep EEG for Bayesian assessment of brain development

Brain development can be evaluated by experts analysing age-related patterns in sleep electroencephalograms (EEG). Natural variations in the patterns, noise, and artefacts affect the evaluation accuracy as well as experts' agreement. The knowledge of predictive posterior distribution allows experts to estimate confidence intervals within which decisions are distributed. Bayesian approach to probabilistic inference has provided accurate estimates of intervals of interest. In this paper we propose a new feature extraction technique for Bayesian assessment and estimation of predictive distribution in a case of newborn brain development assessment. The new EEG features are verified within the Bayesian framework on a large EEG data set including 1,100 recordings made from newborns in 10 age groups. The proposed features are highly correlated with brain maturation and their use increases the assessment accuracy

Sleep State Trend (SST), a bedside measure of neonatal sleep state fluctuations based on single EEG channels

Objective To develop and validate an automated method for bedside monitoring of sleep state fluctuations in neonatal intensive care units. Methods A deep learning-based algorithm was designed and trained using 53 EEG recordings from a long-term (a)EEG monitoring in 30 near-term neonates. The results were validated using an external dataset from 30 polysomnography recordings. In addition to training and validating a single EEG channel quiet sleep, we constructed Sleep State Trend (SST), a bedside-ready means for visualising classifier outputs. Results The accuracy of quiet sleep detection in the training data was 90%, and the accuracy was comparable (85-86 %) in all bipolar derivations available from the 4-electrode recordings. The algorithm generalised well to an external dataset, showing 81% overall accuracy despite different signal derivations. SST allowed an intuitive, clear visualisation of the classifier output. Conclusions Fluctuations in sleep states can be detected at high fidelity from a single EEG channel, and the results can be visualised as a transparent and intuitive trend in the bedside monitors. Significance The Sleep State Trend (SST) may provide caregivers a real-time view of sleep state fluctuations and its cyclicity.Peer reviewe

EEG-Based Neonatal Sleep Stage Classification Using Ensemble Learning

Sleep stage classification can provide important information regarding neonatal brain development and maturation. Visual annotation, using polysomnography (PSG), is considered as a gold standard for neonatal sleep stage classification. However, visual annotation is time consuming and needs professional neurologists. For this reason, an internet of things and ensemble-based automatic sleep stage classification has been proposed in this study. 12 EEG features, from 9 bipolar channels, were used to train and test the base classifiers including convolutional neural network, support vector machine, and multilayer perceptron. Bagging and stacking ensembles are then used to combine the outputs for final classification. The proposed algorithm can reach a mean kappa of 0.73 and 0.66 for 2-stage and 3-stage (wake, active sleep, and quiet sleep) classification, respectively. The proposed network works as a semi-real time application because a smoothing filter is used to hold the sleep stage for 3 min. The high-performance parameters and its ability to work in semi real-time makes it a promising candidate for use in hospitalized newborn infants

Neonatal cerebral function monitoring – understanding the amplitude integrated EEG

Amplitude integrated electroencephalography (aEEG) is produced by cerebral function monitors (CFM), and is increasingly used in neonates following research into hypothermia for hypoxic ischaemic encephalopathy in term infants. Formal training packages in aEEG in term infants are limited. aEEG is used less often in clinical practice in preterm infants, and requires an understanding of the normal changes seen with increasing gestational age. A number of classifications for aEEG interpretation exist; some purely for term neonates born, and others encompassing both preterm and term neonates. This article reviews the basics of aEEG, its indications and limitations. We also discuss its role in prognostication in term and preterm infants

Consensus protocol for EEG and amplitude-integrated EEG assessment and monitoring in neonates

The aim of this work is to establish inclusive guidelines on electroencephalography (EEG) applicable to all neonatal intensive care units (NICUs). Guidelines on ideal EEG monitoring for neonates are available, but there are significant barriers to their implementation in many centres around the world. These include barriers due to limited resources regarding the availability of equipment and technical and interpretive round-the-clock personnel. On the other hand, despite its limitations, amplitude-integrated EEG (aEEG) (previously called Cerebral Function Monitor [CFM]) is a common alternative used in NICUs. The Italian Neonatal Seizure Collaborative Network (INNESCO), working with all national scientific societies interested in the field of neonatal clinical neurophysiology, performed a systematic literature review and promoted interdisciplinary discussions among experts (neonatologists, paediatric neurologists, neurophysiologists, technicians) between 2017 and 2020 with the aim of elaborating shared recommendations. A consensus statement on videoEEG (vEEG) and aEEG for the principal neonatal indications was established. The authors propose a flexible frame of recommendations based on the complementary use of vEEG and aEEG applicable to the various neonatal units with different levels of complexity according to local resources and specific patient features. Suggestions for promoting cooperation between neonatologists, paediatric neurologists, and neurophysiologists, organisational restructuring, and teleneurophysiology implementation are provided

Temporal evolution of quantitative EEG within 3 days of birth in early preterm infants

For the premature newborn, little is known about changes in brain activity during transition to extra-uterine life. We aim to quantify these changes in relation to the longer-term maturation of the developing brain. We analysed EEG for up to 72 hours after birth from 28 infants bornPeer reviewe

An automated bedside measure for monitoring neonatal cortical activity: a supervised deep learning-based electroencephalogram classifier with external cohort validation

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Automated detection of artefacts in neonatal EEG with residual neural networks

Background and objective: To develop a computational algorithm that detects and identifies different arte-fact types in neonatal electroencephalography (EEG) signals. Methods: As part of a larger algorithm, we trained a Residual Deep Neural Network on expert human annotations of EEG recordings from 79 term infants recorded in a neonatal intensive care unit (112 h of 18-channel recording). The network was trained using 10 fold cross validation in Matlab. Artefact types included: device interference, EMG, movement, electrode pop, and non-cortical biological rhythms. Per-formance was assessed by prediction statistics and further validated on a separate independent dataset of 13 term infants (143 h of 3-channel recording). EEG pre-processing steps, and other post-processing steps such as averaging probability over a temporal window, were also included in the algorithm. Results: The Residual Deep Neural Network showed high accuracy (95%) when distinguishing periods of clean, artefact-free EEG from any kind of artefact, with a median accuracy for individual patient of 91% (IQR: 81%-96%). The accuracy in identifying the five different types of artefacts ranged from 57%-92%, with electrode pop being the hardest to detect and EMG being the easiest. This reflected the proportion of artefact available in the training dataset. Misclassification as clean was low for each artefact type, ranging from 1%-11%. The detection accuracy was lower on the validation set (87%). We used the algorithm to show that EEG channels located near the vertex were the least susceptible to artefact. Conclusion: Artefacts can be accurately and reliably identified in the neonatal EEG using a deep learning algorithm. Artefact detection algorithms can provide continuous bedside quality assessment and support EEG review by clinicians or analysis algorithms. (c) 2021 Elsevier B.V. All rights reserved.Peer reviewe