Classification of newborn EEG maturity with Bayesian averaging over decision trees

EEG experts can assess a newborn’s brain maturity by visual analysis of age-related patterns in sleep EEG. It is highly desirable to make the results of assessment most accurate and reliable. However, the expert analysis is limited in capability to provide the estimate of uncertainty in assessments. Bayesian inference has been shown providing the most accurate estimates of uncertainty by using Markov Chain Monte Carlo (MCMC) integration over the posterior distribution. The use of MCMC enables to approximate the desired distribution by sampling the areas of interests in which the density of distribution is high. In practice, the posterior distribution can be multimodal, and so that the existing MCMC techniques cannot provide the proportional sampling from the areas of interest. The lack of prior information makes MCMC integration more difficult when a model parameter space is large and cannot be explored in detail within a reasonable time. In particular, the lack of information about EEG feature importance can affect the results of Bayesian assessment of EEG maturity. In this paper we explore how the posterior information about EEG feature importance can be used to reduce a negative influence of disproportional sampling on the results of Bayesian assessment. We found that the MCMC integration tends to oversample the areas in which a model parameter space includes one or more features, the importance of which counted in terms of their posterior use is low. Using this finding, we proposed to cure the results of MCMC integration and then described the results of testing the proposed method on a set of sleep EEG recordings

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

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

Cortical networks show characteristic recruitment patterns after somatosensory stimulation by pneumatically evoked repetitive hand movements husin newborn infants

Controlled assessment of functional cortical networks is an unmet need in the clinical research of noncooperative subjects, such as infants. We developed an automated, pneumatic stimulation method to actuate naturalistic movements of an infant's hand, as well as an analysis pipeline for assessing the elicited electroencephalography (EEG) responses and related cortical networks. Twenty newborn infants with perinatal asphyxia were recruited, including 7 with mild-to-moderate hypoxic-ischemic encephalopathy (HIE). Statistically significant corticokinematic coherence (CKC) was observed between repetitive hand movements and EEG in all infants, peaking near the contralateral sensorimotor cortex. CKC was robust to common sources of recording artifacts and to changes in vigilance state. A wide recruitment of cortical networks was observed with directed phase transfer entropy, also including areas ipsilateral to the stimulation. The extent of such recruited cortical networks was quantified using a novel metric, Spreading Index, which showed a decrease in 4 (57%) of the infants with HIE. CKC measurement is noninvasive and easy to perform, even in noncooperative subjects. The stimulation and analysis pipeline can be fully automated, including the statistical evaluation of the cortical responses. Therefore, the CKC paradigm holds great promise as a scientific and clinical tool for controlled assessment of functional cortical networks.Peer reviewe

Optimiser le réchauffement chez le nouveau-né asphyxié soumis à l'hypothermie thérapeutique

L'encéphalopathie hypoxique ischémique néonatale (EHI) reste la cause principale de mortalité chez le nouveau-né à terme. Un tiers des survivants vont développer des séquelles neurologiques, dont la paralysie cérébrale (PC), l'épilepsie et un retard intellectuel. Afin d'améliorer leur pronostic, ces nouveau-nés sont soumis à l'hypothermie thérapeutique (HT) qui débute au plus tard 6 heures après la naissance, pour une durée totale de 72 heures, suivie d'un réchauffement graduel (0.5°C/h). Il a été démontré que cette thérapie à effet neuroprotecteur diminue considérablement l'étendue des lésions cérébrales et la fréquence des séquelles neurologiques. Or, des études animales suggèrent que l'hypothermie sans sédation avec opioïdes n'est pas bénéfique. Selon les observations qui ont été réalisées, les porcelets traités avec la thérapie, mais sans l’administration d’analgésique ont manifesté des signes d’instabilités et de tremblements exagérés. On ignorait jusqu’à présent dans quelle mesure ces résultats tirés des expérimentations animales pouvaient être généralisables au nouveau-né. Ainsi, mon projet de maîtrise vise à mieux comprendre les facteurs qui risquent de compromettre les effets bénéfiques de la thérapie de refroidissement, dans le but d’optimiser la neuroprotection et d’améliorer le développement des nourrissons atteints d’EHI. Nous avons comme objectif principal d’évaluer l’association entre les doses d’opioïdes consommées pendant l’HT, le degrée de tremblement, et l’évolution de l’index de discontinuité à l’EEG au fil des 72h de l’HT, du réchauffement et jusqu’à 12 heures post-HT. Pour répondre à l’objectif, nous avons conduit une étude chez 21 nouveau-nés avec EHI soumis à l’HT, et dont les principaux résultats ont montré des associations significatives entre les fortes doses d’opioïdes administrés à l’enfant (r = - 0.493, p = 0.023), les frissons réduits pendant l’HT (r = 0.513, p = 0.017) et l’amélioration du rythme cérébrale d’EEG. Ces résultats sont décrits de manière plus approfondie dans le Chapitre 2 qui présente la version de l’article soumis à la revue Journal of Pediatrics, et le Chapitre 3 qui présente un retour sur la littérature à la lumière de nos trouvailles. Quant au Chapitre 4, nous y élaborons les possibilités de perspectives futures et les retombées cliniques de nos résultats. À long terme, nous espérons que nos travaux permettront l’ouverture d’une nouvelle piste d’amélioration de la neuroprotection, en favorisant systématiquement une meilleure prise en charge de la douleur et du stress induit par le refroidissement.Neonatal hypoxic-ischemic encephalopathy (HIE) remains the leading cause of death and mortality in the term infant. A third of the survivors will develop neurological sequelae including cerebral palsy (CP), epilepsy and mental retardation. In order to improve their prognosis, these newborns undergo therapeutic hypothermia (TH), which begins no later than 6 hours after birth, maintained for a total duration of 72 hours and followed by gradual rewarming (0.5°C/h). This neuroprotective therapy has been shown to significantly decrease the extent of brain injury and the frequency of neurological sequelae. Results from animal studies revealed that ongoing hypothermia without proper anesthesia is not beneficial. Based on the observations that have been reported, piglets treated with TH with no analgesics have shown signs of instability and excessive tremors. Until now, the extent to which these results from animal experiments could be generalized to the newborn remained unknown. Thus, the purpose of my master’s project was to better understand the clinical factors that may compromise the beneficial effects of TH, in an attempt to optimize neuroprotection and improve the neurological outcome of HIE infants. Our main objective was to assess the associations between opioid doses consumed during TH, shivering recorded during TH, and the evolution of EEG discontinuity index over the course of TH, rewarming and up to 12 hours post-TH. To meet the objective, we conducted a study in 21 newborns with HIE undergoing TH, and the results have shown significant associations between high doses of opioid administered (r = - 0.493, p = 0.023), reduced shivering stress (r = 0.513, p = 0.017) and improved EEG background activity. The key findings of the study are described in more detail in Chapter 2, which presents the original manuscript submitted for publication to the “Journal of Pediatrics”, and Chapter 3, which presents a review of the literature in light of our results. In Chapter 4, we discuss future perspectives and the clinical significance of our results. At last, we hope that our study will open up new avenues for improving neuroprotection, by systematically promoting a better management of pain and cooling-induced stress