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• We use qEEG analysis to mimic a visual EEG analysis approach in neonatal hypoxic-ischemic encephalopathy. • Machine learning models were developed and validated on two independent EEG datasets. • The proposed model is effective in discriminating neonates requiring therapeutic hypothermia in the first 6 h of life. The study aimed to address the challenge of early assessment of neonatal hypoxic-ischemic encephalopathy (HIE) severity to identify candidates for therapeutic hypothermia (TH). The objective was to develop an automated classification model for neonatal EEGs, enabling accurate HIE severity assessment 24/7. EEGs recorded within 6 h of life after perinatal anoxia were visually graded into 3 severity groups (HIE French Classification) and quantified using 6 qEEG markers measuring amplitude, continuity and frequency content. Machine learning models were developed on a dataset of 90 EEGs and validated on an independent dataset of 60 EEGs. The selected model achieved an overall accuracy of 80.6% in the development phase and 80% in the validation phase. Notably, the model accurately identified 28 out of 30 children for whom TH was indicated after visual EEG analysis, with only 2 cases (moderate EEG abnormalities) not recommended for cooling. The combination of clinically relevant qEEG markers led to the development of an effective automated EEG classification model, particularly suited for the post-anoxic latency phase. This model successfully discriminated neonates requiring TH. The proposed model has potential as a bedside clinical decision support tool for TH. [ABSTRACT FROM AUTHOR]

• Brain State of the New Born (BSN) serves as a reliable bedside EEG assessment tool for measuring clinical outcomes. • Clear temporal dynamics in BSN levels are evident in outcome predictions during the first two days of life. • BSN provides an objective and high-resolution assessment of EEG background and enables accurate outcome prediction. To evaluate the utility of a fully automated deep learning -based quantitative measure of EEG background, Brain State of the Newborn (BSN), for early prediction of clinical outcome at four years of age. The EEG monitoring data from eighty consecutive newborns was analyzed using the automatically computed BSN trend. BSN levels during the first days of life (a of total 5427 hours) were compared to four clinical outcome categories: favorable, cerebral palsy (CP), CP with epilepsy, and death. The time dependent changes in BSN-based prediction for different outcomes were assessed by positive/negative predictive value (PPV/NPV) and by estimating the area under the receiver operating characteristic curve (AUC). The BSN values were closely aligned with four visually determined EEG categories (p < 0·001), as well as with respect to clinical milestones of EEG recovery in perinatal Hypoxic Ischemic Encephalopathy (HIE; p < 0·003). Favorable outcome was related to a rapid recovery of the BSN trend, while worse outcomes related to a slow BSN recovery. Outcome predictions with BSN were accurate from 6 to 48 hours of age: For the favorable outcome, the AUC ranged from 95 to 99% (peak at 12 hours), and for the poor outcome the AUC ranged from 96 to 99% (peak at 12 hours). The optimal BSN levels for each PPV/NPV estimate changed substantially during the first 48 hours, ranging from 20 to 80. We show that the BSN provides an automated, objective, and continuous measure of brain activity in newborns. The BSN trend discloses the dynamic nature that exists in both cerebral recovery and outcome prediction, supports individualized patient care, rapid stratification and early prognosis. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) can be used to assess depth of consciousness, but interpreting EEG can be challenging, especially in neonates whose EEG undergo rapid changes during the perinatal course. EEG can be processed into quantitative EEG (QEEG), but limited data exist on the range of QEEG for normal term neonates during wakefulness and sleep, baseline information that would be useful to determine changes during sedation or anesthesia. We aimed to determine the range of QEEG in neonates during awake, active sleep and quiet sleep states, and identified the ones best at discriminating between the three states. Normal neonatal EEG from 37 to 46 weeks were analyzed and classified as awake, quiet sleep, or active sleep. After processing and artifact removal, total power, power ratio, coherence, entropy, and spectral edge frequency (SEF) 50 and 90 were calculated. Descriptive statistics were used to summarize the QEEG in each of the three states. Receiver operating characteristic (ROC) curves were used to assess discriminatory ability of QEEG. 30 neonates were analyzed. QEEG were different between awake vs asleep states, but similar between active vs quiet sleep states. Entropy beta, delta2 power %, coherence delta2, and SEF50 were best at discriminating awake vs active sleep. Entropy beta had the highest AUC-ROC ≥ 0.84. Entropy beta, entropy delta1, theta power %, and SEF50 were best at discriminating awake vs quiet sleep. All had AUC-ROC ≥ 0.78. In active sleep vs quiet sleep, theta power % had highest AUC-ROC > 0.69, lower than the other comparisons. We determined the QEEG range in healthy neonates in different states of consciousness. Entropy beta and SEF50 were best at discriminating between awake and sleep states. QEEG were not as good at discriminating between quiet and active sleep. In the future, QEEG with high discriminatory power can be combined to further improve ability to differentiate between states of consciousness. [ABSTRACT FROM AUTHOR]

Electroencephalographic (EEG) monitoring is important for the diagnosis of hypoxic-ischemic (HI) brain injury in high-risk preterm infants. EEG monitoring is limited by the reliance on expert clinical observation. However, high-risk preterm infants often do not present observable symptoms due to their frailty. Thus, there is an urgent need to find better ways to automatically quantify changes in the EEG these high-risk babies. This article is a first step towards this goal. This innovative study demonstrates the effectiveness of deep Convolutional Neural Networks (CNN) pattern classifiers, trained on spectrally-detailed Wavelet Scalograms (WS) images derived from neonatal EEG sharp waves—a potential translational HI biomarker, at birth. The WS-CNN classifiers exhibit outstanding performance in identifying HI sharp waves within an exclusive clinical EEG recordings dataset of preterm infants immediately after birth. The work has impact as it demonstrates exceptional high accuracy of 99.34 ± 0.51% cross-validated across 13,624 EEG patterns over 48 h raw EEG at low 256 Hz clinical sampling rates. Furthermore, the WS-CNN pattern classifier is able to accurately identify the sharp-waves within the most critical first hours of birth (n = 8, 4:36 ± 1:09 h), regardless of potential morphological changes influenced by different treatments/drugs or the evolutionary ‘timing effects’ of the injury. This underscores its reliability as a tool for the identification and quantification of clinical EEG sharp-wave biomarkers at bedside.

Neonatal brain monitoring in the neonatal intensive care units (NICU) requires a continuous review of the spontaneous cortical activity, i.e., the electroencephalograph (EEG) background activity. This needs development of bedside methods for an automated assessment of the EEG background activity. In this paper, we present development of the key components of a neonatal EEG background classifier, starting from the visual background scoring to classifier design, and finally to possible bedside visualization of the classifier results. A dataset with 13,200 5-minute EEG epochs (8-- 16 channels) from 27 infants with birth asphyxia was used for classifier training after scoring by two independent experts. We tested three classifier designs based on 98 computational features, and their performance was assessed with respect to scoring system, pre- and post-processing of labels and outputs, choice of channels, and visualization in monitor displays. The optimal solution achieved an overall classification accuracy of 97% with a range across subjects of 81--100%. We identified a set of 23 features that make the classifier highly robust to the choice of channels and missing data due to artefact rejection. Our results showed that an automated bedside classifier of EEG background is achievable, and we publish the full classifier algorithm to allow further clinical replication and validation studies. [ABSTRACT FROM AUTHOR]

Preterm neonates are at risk of long-term neurodevelopmental impairments due to disruption of natural brain development. Electroencephalography (EEG) analysis can provide insights into brain development of preterm neonates. This study aims to explore the use of microstate (MS) analysis to evaluate global brain dynamics changes during maturation in preterm neonates with normal neurodevelopmental outcome. The dataset included 135 EEGs obtained from 48 neonates at varying postmenstrual ages (26.4 to 47.7 weeks), divided into four age groups. For each recording we extracted a 5-minute epoch during quiet sleep (QS) and during non-quiet sleep (NQS), resulting in eight groups (4 age group x 2 sleep states). We compared MS maps and corresponding (map-specific) MS metrics across groups using group-level maps. Additionally, we investigated individual map metrics. Four group-level MS maps accounted for approximately 70% of the global variance and showed non-random syntax. MS topographies and transitions changed significantly when neonates reached 37 weeks. For both sleep states and all MS maps, MS duration decreased and occurrence increased with age. The same relationships were found using individual maps, showing strong correlations (Pearson coefficients up to 0.74) between individual map metrics and post-menstrual age. Moreover, the Hurst exponent of the individual MS sequence decreased with age. The observed changes in MS metrics with age might reflect the development of the preterm brain, which is characterized by formation of neural networks. Therefore, MS analysis is a promising tool for monitoring preterm neonatal brain maturation, while our study can serve as a valuable reference for investigating EEGs of neonates with abnormal neurodevelopmental outcomes. [ABSTRACT FROM AUTHOR]

Early neurodevelopment is critically dependent on the structure and dynamics of spontaneous neuronal activity; however, the natural organization of newborn cortical networks is poorly understood. Recent adult studies suggest that spontaneous cortical activity exhibits discrete network states with physiological correlates. Here, we studied newborn cortical activity during sleep using hidden Markov modeling to determine the presence of such discrete neonatal cortical states (NCS) in 107 newborn infants, with 47 of them presenting with a perinatal brain injury. Our results show that neonatal cortical activity organizes into four discrete NCSs that are present in both cardinal sleep states of a newborn infant, active and quiet sleep, respectively. These NCSs exhibit state-specific spectral and functional network characteristics. The sleep states exhibit different NCS dynamics, with quiet sleep presenting higher fronto-temporal activity and a stronger brain-wide neuronal coupling. Brain injury was associated with prolonged lifetimes of the transient NCSs, suggesting lowered dynamics, or flexibility, in the cortical networks. Taken together, the findings suggest that spontaneously occurring transient network states are already present at birth, with significant physiological and pathological correlates; this NCS analysis framework can be fully automatized, and it holds promise for offering an objective, global level measure of early brain function for benchmarking neurodevelopmental or clinical research.

The accuracy of electroencephalogram (EEG) source localization is compromised because of head modelling errors. In this study, we investigated the effect of inaccuracy in the conductivity of head tissues and head model structural deficiencies on the accuracy of EEG source analysis in premature neonates. A series of EEG forward and inverse simulations was performed by introducing structural deficiencies into the reference head models to generate test models, which were then used to investigate head modelling errors caused by cerebrospinal fluid (CSF) exclusion, lack of grey matter (GM)–white matter (WM) distinction, fontanel exclusion and inaccuracy in skull conductivity. The modelling errors were computed between forward and inverse solutions obtained using the reference and test models generated for each deficiency. Our results showed that the exclusion of CSF from the head model had a strong widespread effect on the accuracy of the EEG source localization with position errors lower than 4.17 mm. The GM and WM distinction also caused strong localization errors (up to 3.5 mm). The exclusion of fontanels from the head model also strongly affected the accuracy of the EEG source localization for sources located beneath the fontanels with a maximum localization error of 4.37 mm. Similarly, inaccuracies in the skull conductivity caused errors in EEG forward and inverse modelling in sources beneath cranial bones. Our results indicate that the accuracy of EEG source imaging in premature neonates can be largely improved by using head models, which include not only the brain, skull and scalp but also the CSF, GM, WM and fontanels. [ABSTRACT FROM AUTHOR]

The number of electrodes used to acquire neonatal EEG signals varies between institutions. Therefore, tools for automatic EEG analysis, such as neonatal seizure detection algorithms, need to be able to handle different electrode montages in order to find widespread use. The aim of this study was to analyse the effect of montage on neonatal seizure detector performance. A full 18-channel montage was compared to reduced 3- and 8-channel montages using a convolutional neural network for seizure detection. Sensitivity decreased by 10 - 18 % for the reduced montages while specificity was mostly unaffected. Electrode artefacts and artefacts associated with biological rhythms caused incorrect classification of nonseizure activity in some cases, but these artefacts were filtered out in the 3-channel montage. Other types of artefacts had little effect. Reduced montages result in some reduction in classifier accuracy, but the performance may still be acceptable. Recording artefacts had a limited effect on detection accuracy.

Objective: Sharing medical data between institutions is difficult in practice due to data protection laws and official procedures within institutions. Therefore, most existing algorithms are trained on relatively small electroencephalogram (EEG) data sets which is likely to be detrimental to prediction accuracy. In this work, we simulate a case when the data can not be shared by splitting the publicly available data set into disjoint sets representing data in individual institutions. Methods and procedures: We propose to train a (local) detector in each institution and aggregate their individual predictions into one final prediction. Four aggregation schemes are compared, namely, the majority vote, the mean, the weighted mean and the Dawid-Skene method. The method was validated on an independent data set using only a subset of EEG channels. Results: The ensemble reaches accuracy comparable to a single detector trained on all the data when sufficient amount of data is available in each institution. Conclusion: The weighted mean aggregation scheme showed best performance, it was only marginally outperformed by the Dawid–Skene method when local detectors approach performance of a single detector trained on all available data. Clinical impact: Ensemble learning allows training of reliable algorithms for neonatal EEG analysis without a need to share the potentially sensitive EEG data between institutions.

This document is a corrigendum for an article titled "Building an open source classifier for the neonatal EEG background: a systematic feature-based approach from expert scoring to clinical visualization." The corrigendum corrects an error in the spelling of one of the author's names, changing "Saeed Montazeri Moghadam" to "Saeed Montazeri." The authors apologize for the error and state that it does not affect the scientific conclusions of the article. The corrigendum also includes copyright information and a note from the publisher stating that the views expressed in the article are solely those of the authors. [Extracted from the article]

Background: Artefact removal in neonatal electroencephalography (EEG) by visual inspection generally depends on the expertise of the operator, is time consuming and is not a consistent pre-processing step to the pipeline for the automated EEG analysis. Therefore, there is the need for the automated detection and removal of artefacts in neonatal EEG, especially of distinct and predominant artefacts such as flat line segments (mainly caused by instrumental error where contact between electrodes and head box is lost) and large amplitude fluctuations (related to neonatal movements). Method: A threshold-based algorithm for the automated detection and removal of flat line segments and large amplitude fluctuations in neonatal EEG of infants at term-equivalent age is developed. The algorithm applies thresholds to the absolute second difference, absolute amplitude, absolute first difference and the ratio between the frequency content above 50 Hz and the frequency content across all frequencies. Results: The algorithm reaches a median accuracy of 0.91, a median hit rate of 0.91 and a median false discovery rate of 0.37. Also, a significant improvement (≈10%) in the performance of a four-stage sleep classifier is observed after artefact removal with the proposed algorithm as compared to before its application. Significance: An automated artefact removal method contributes to the pipeline of automated EEG analysis. The proposed algorithm has shown to have good performance and to be effective in neonatal EEG applications. [ABSTRACT FROM AUTHOR]

Background Artefact removal in neonatal electroencephalography (EEG) by visual inspection generally depends on the expertise of the operator, is time consuming and is not a consistent pre-processing step to the pipeline for the automated EEG analysis. Therefore, there is the need for the automated detection and removal of artefacts in neonatal EEG, especially of distinct and predominant artefacts such as flat line segments (mainly caused by instrumental error where contact between electrodes and head box is lost) and large amplitude fluctuations (related to neonatal movements). Method A threshold-based algorithm for the automated detection and removal of flat line segments and large amplitude fluctuations in neonatal EEG of infants at term-equivalent age is developed. The algorithm applies thresholds to the absolute second difference, absolute amplitude, absolute first difference and the ratio between the frequency content above 50 Hz and the frequency content across all frequencies. Results The algorithm reaches a median accuracy of 0.91, a median hit rate of 0.91 and a median false discovery rate of 0.37. Also, a significant improvement (≈10%) in the performance of a four-stage sleep classifier is observed after artefact removal with the proposed algorithm as compared to before its application. Significance An automated artefact removal method contributes to the pipeline of automated EEG analysis. The proposed algorithm has shown to have good performance and to be effective in neonatal EEG applications.

Newborn seizures are frequent in preterm newborns and indicate brain lesions in many cases. The objective of this observational study was to investigate the long-term outcome of very-low-birth-weight (VLBW) and low-birth-weight (LBW) preterm infants with neonatal seizures. We examined 54 preterm infants (40 VLBW and 14 LBW cases) born between 2008 and 2011 with clinical seizures during the neonatal period confirmed by interictal or ictal electroencephalography recordings in a retrospective single-center study. Neurodevelopmental follow-up included an expert neurological examination and cognitive testing (Kaufman Assessment Battery for Children) at a mean age of six years. The (mean ± standard deviation) gestational ages of the VLBW and LBW infants were 27.2 ± 1.9 weeks and 33.4 ± 1.7 weeks, respectively, and the postnatal age at seizure onset was 13 ± 11 days in VLBW infants and 9 ± 8 days in LBW infants, with a wide range of one to 62 days. LBW infants more frequently developed non-motor seizures (50.0%) than VLBW infants did (25.0%), and higher-grade intracranial hemorrhage was the predominant etiology in the VLBW group (18.0%), while the etiology in the LBW group was more heterogeneous and included central nervous system malformations and genetic syndromes. At the mean age of 6.2 ± 2.0, years, 25/54 patients were assessed and 44.4% of the VLBW group and 71.4% of the LBW group showed intellectual impairment. Infantile cerebral palsy was present in 22% of VLBW and 42.9% of LBW infants, respectively. The present analysis of long-term neurodevelopmental outcomes of preterm neonates who experienced seizures shows that the risk for intellectual impairment is not limited only to VLBW infants but may significantly affect LBW infants as well. The etiological spectrum differs in relation to gestational age. [Display omitted] • Etiology of neonatal seizures is different in LBW and VLBW preterm infants. • Postnatal age at seizure onset is later in the preterm infant compared to the full-term infant. • Subtle seizures are common in LBW infants and carry a risk of cognitive impairment at school age. [ABSTRACT FROM AUTHOR]

Our objective was to evaluate the potential additional value of electroencephalogram (EEG) and evoked potentials in neonates with hypoxic-ischemic encephalopathy to predict their disability at 1 and 2 years old. 30 full-term infants after perinatal asphyxia who underwent therapeutic hypothermia were evaluated at 1 year and 2 years for disability using International Classification of Functioning, Disability and Health classification. Scores for EEG, sensory evoked potentials and brainstem auditory evoked potentials were evaluated after withdrawal of therapeutic hypothermia that lasted 72 h. A regression approach was investigated to build models allowing to distinguish neonates according to their disability at 1 and 2 years. Two models were built, the first by considering the clinical data and EEG before and after therapeutic hypothermia and the second by incorporating evoked potentials recording. Adding EEG and evoked potentials data after rewarming improved dramatically the accuracy of the model considering outcome at 1 and 2 years. We propose to record systematically EEG and evoked potentials following rewarming to predict the outcome of neonates with hypoxic ischemic encephalopathy. Combination of altered evoked potentials with no improvement of EEG after rewarming appeared to be a robust criterion for a poor outcome. • Outcome of neonates with HIE remains uncertain. • Adding EEG and EP data after rewarming to clinical data improved the accuracy of a prediction model. • EEG and EP should be performed after withdrawal of TH to predict the outcome of neonates with HIE. [ABSTRACT FROM AUTHOR]

Neonates spend most of their life sleeping. During sleep, their brain experiences fast changes in its functional organization. Microstate analysis permits to capture the rapid dynamical changes occurring in the functional organization of the brain by representing the changing spatio-temporal features of the electroencephalogram (EEG) as a sequence of short-lasting scalp topographies—the microstates. In this study, we modeled the ongoing neonatal EEG into sequences of a limited number of microstates and investigated whether the extracted microstate features are altered in REM and NREM sleep (usually known as active and quiet sleep states—AS and QS—in the newborn) and depend on the EEG frequency band. 19-channel EEG recordings from 60 full-term healthy infants were analyzed using a modified version of the k-means clustering algorithm. The results show that ~ 70% of the variance in the datasets can be described using 7 dominant microstate templates. The mean duration and mean occurrence of the dominant microstates were significantly different in the two sleep states. Microstate syntax analysis demonstrated that the microstate sequences characterizing AS and QS had specific non-casual structures that differed in the two sleep states. Microstate analysis of the neonatal EEG in specific frequency bands showed a clear dependence of the explained variance on frequency. Overall, our findings demonstrate that (1) the spatio-temporal dynamics of the neonatal EEG can be described by non-casual sequences of a limited number of microstate templates; (2) the brain dynamics described by these microstate templates depends on frequency; (3) the features of the microstate sequences can well differentiate the physiological conditions characterizing AS and QS. [ABSTRACT FROM AUTHOR]

EEG is the gold standard for seizure detection in the newborn infant, but EEG interpretation in the preterm group is particularly challenging; trained experts are scarce and the task of interpreting EEG in real-time is arduous. Preterm infants are reported to have a higher incidence of seizures compared to term infants. Preterm EEG morphology differs from that of term infants, which implies that seizure detection algorithms trained on term EEG may not be appropriate. The task of developing preterm specific algorithms becomes extra-challenging given the limited amount of annotated preterm EEG data available. This paper explores novel deep learning (DL) architectures for the task of neonatal seizure detection in preterm infants. The study tests and compares several approaches to address the problem: training on data from full-term infants; training on data from preterm infants; training on age-specific preterm data and transfer learning. The system performance is assessed on a large database of continuous EEG recordings of 575 h in duration. It is shown that the accuracy of a validated term-trained EEG seizure detection algorithm, based on a support vector machine classifier, when tested on preterm infants falls well short of the performance achieved for full-term infants. An AUC of 88.3% was obtained when tested on preterm EEG as compared to 96.6% obtained when tested on term EEG. When re-trained on preterm EEG, the performance marginally increases to 89.7%. An alternative DL approach shows a more stable trend when tested on the preterm cohort, starting with an AUC of 93.3% for the term-trained algorithm and reaching 95.0% by transfer learning from the term model using available preterm data. The proposed DL approach avoids time-consuming explicit feature engineering and leverages the existence of the term seizure detection model, resulting in accurate predictions with a minimum amount of annotated preterm data. [ABSTRACT FROM AUTHOR]

Neonatal brain monitoring in the neonatal intensive care units (NICU) requires a continuous review of the spontaneous cortical activity, i.e., the electroencephalograph (EEG) background activity. This needs development of bedside methods for an automated assessment of the EEG background activity. In this paper, we present development of the key components of a neonatal EEG background classifier, starting from the visual background scoring to classifier design, and finally to possible bedside visualization of the classifier results. A dataset with 13,200 5-minute EEG epochs (8–16 channels) from 27 infants with birth asphyxia was used for classifier training after scoring by two independent experts. We tested three classifier designs based on 98 computational features, and their performance was assessed with respect to scoring system, pre- and post-processing of labels and outputs, choice of channels, and visualization in monitor displays. The optimal solution achieved an overall classification accuracy of 97% with a range across subjects of 81–100%. We identified a set of 23 features that make the classifier highly robust to the choice of channels and missing data due to artefact rejection. Our results showed that an automated bedside classifier of EEG background is achievable, and we publish the full classifier algorithm to allow further clinical replication and validation studies.

To identify relevant quantitative parameters for early classification of neonatal hypoxic-ischemic encephalopathy (HIE) severity from conventional EEGs. Ninety EEGs, recorded in full-term infants within 6 h of life after perinatal hypoxia, were visually classified according to the French EEG classification into three groups of increasing HIE severity. Physiologically significant EEG features (signal amplitude, continuity and frequency content) were automatically quantified using different parameters. The EEG parameters selection was based on their ability to reproduce the visual EEG classification. Post hoc analysis based on clinical outcome was performed. Six EEG parameters were selected, with overall EEG classification performances between 61% and 70%. All parameters differed significantly between group 3 (severe) and groups 1 (normal-mildly abnormal) and 2 (moderate) EEGs (p < 0.001). Amplitude and discontinuity parameters were different between the 3 groups (p < 0.01) and were also the best predictors of clinical outcome. Conversely, pH and lactate did not differ between groups. This study provides quantitative EEG parameters that are complementary to visual analysis as early markers of neonatal HIE severity. These parameters could be combined in a multiparametric algorithm to improve their classification performance. The absence of relationship between pH lactate and HIE severity reinforces the central role of early neonatal EEG. [ABSTRACT FROM AUTHOR]

Early nutritional compromise after preterm birth is shown to affect long-term neurodevelopment, however, there has been a lack of early functional measures of nutritional effects. Recent progress in computational electroencephalography (EEG) analysis has provided means to measure the early maturation of cortical activity. Our study aimed to explore whether computational metrics of early sequential EEG recordings could reflect early nutritional care measured by energy and macronutrient intake in the first week of life. A higher energy or macronutrient intake was assumed to associate with improved development of the cortical activity. We analyzed multichannel EEG recorded at 32 weeks (32.4 ± 0.7) and 36 weeks (36.6 ± 0.9) of postmenstrual age in a cohort of 28 preterm infants born before 32 weeks of postmenstrual age (range: 24.3–32 weeks). We computed several quantitative EEG measures from epochs of quiet sleep (QS): (i) spectral power; (ii) continuity; (iii) interhemispheric synchrony, as well as (iv) the recently developed estimate of maturational age. Parenteral nutritional intake from day 1 to day 7 was monitored and clinical factors collected. Lower calories and carbohydrates were found to correlate with a higher reduction of spectral amplitude in the delta band. Lower protein amount associated with higher discontinuity. Both higher proteins and lipids intake correlated with a more developmental increase in interhemispheric synchrony as well as with better progress in the estimate of EEG maturational age (EMA). Our study shows that early nutritional balance after preterm birth may influence subsequent maturation of brain activity in a way that can be observed with several intuitively reasoned and transparent computational EEG metrics. Such measures could become early functional biomarkers that hold promise for benchmarking in the future development of therapeutic interventions.

Early nutritional compromise after preterm birth is shown to affect long-term neurodevelopment, however, there has been a lack of early functional measures of nutritional effects. Recent progress in computational electroencephalography (EEG) analysis has provided means to measure the early maturation of cortical activity. Our study aimed to explore whether computational metrics of early sequential EEG recordings could reflect early nutritional care measured by energy and macronutrient intake in the first week of life. A higher energy or macronutrient intake was assumed to associate with improved development of the cortical activity. We analyzed multichannel EEG recorded at 32 weeks (32.4 ± 0.7) and 36 weeks (36.6 ± 0.9) of postmenstrual age in a cohort of 28 preterm infants born before 32 weeks of postmenstrual age (range: 24.3–32 weeks). We computed several quantitative EEG measures from epochs of quiet sleep (QS): (i) spectral power; (ii) continuity; (iii) interhemispheric synchrony, as well as (iv) the recently developed estimate of maturational age. Parenteral nutritional intake from day 1 to day 7 was monitored and clinical factors collected. Lower calories and carbohydrates were found to correlate with a higher reduction of spectral amplitude in the delta band. Lower protein amount associated with higher discontinuity. Both higher proteins and lipids intake correlated with a more developmental increase in interhemispheric synchrony as well as with better progress in the estimate of EEG maturational age (EMA). Our study shows that early nutritional balance after preterm birth may influence subsequent maturation of brain activity in a way that can be observed with several intuitively reasoned and transparent computational EEG metrics. Such measures could become early functional biomarkers that hold promise for benchmarking in the future development of therapeutic interventions. [ABSTRACT FROM AUTHOR]

Abstract We previously reported the adverse effects of perinatal dioxin exposure on child neurodevelopment around a former US military airbase in Vietnam. In the present study, we investigated the effects of maternal dioxin exposure on fetal brain development, which may predict neurodevelopmental outcomes in early childhood. A total of 55 newborns with mothers from dioxin-contaminated areas were recruited in the prefecture hospital in Bien Hoa, Vietnam. Dioxins in maternal breast milk collected 1 month after birth were used as a maternal exposure marker. Relative powers and coherence were computed from neonatal electroencephalogram (EEG) records during active sleep stages. Relationships between the EEG parameters and dioxin exposure markers were analyzed using linear regression and a general linear model after adjusting for gestational age, body length, and head circumference of infants at birth. Using data from 47 infants whose neurodevelopment was examined in a 2-year follow-up study, associations between EEG parameters and neurodevelopment were analyzed after adjusting for confounding factors. On the right frontal and parietal regions, relative delta powers were significantly decreased, and relative alpha and beta powers were significantly increased with increasing dioxin exposure. Increases in delta power and decreases in alpha power on the right frontal and parietal regions were associated with an increase in language scores at 2 years of age. Furthermore, intra- and inter-hemispheric coherence in theta and alpha bands were positively and inversely correlated with dioxin exposure, respectively, and increased intra-coherence in the right hemisphere was associated with lower language scores. These findings suggest that prenatal dioxin exposure affects neuronal activity and functional connectivity between brain regions, and may lead to poor language development. Graphical abstract Unlabelled Image Highlights • Prenatal dioxin exposure affects neuronal activity estimated from neonatal EEG. • Relative power and coherence of neonatal EEG are good markers for brain development. • Alteration of neonatal EEG might lead to poor language development at 2 years of age. [ABSTRACT FROM AUTHOR]

Summary: The aim of this study was to evaluate whether specific etiologies of neonatal seizures have distinct ictal electroclinical features. A systematic review of English articles using the PubMed database since 2004 (last update 9/26/16). Search terms included text words and Medical Subject Headings (MeSH) terms related to neonatal seizures. Eligible articles included reports of neonates with seizures with a full description of seizure semiology and electroclinical findings. Independent extraction of data was performed by 2 authors using predefined data fields, including study quality indicators. Data were collected for every individual patient described in the articles. The dataset was analyzed with the Fisher exact test. The initial search led to 8507 titles; using filters, 2910 titles and abstracts were identified, with 177 full texts selected to be read. Fifty‐seven studies were included in the analysis with 151 neonates (37.7 male and 62.9% term). Genetic etiologies (51%) and sequential seizures (41.1%) predominated in this sample and hypoxic‐ischemic encephalopathy (HIE) accounted for only 4%. The low prevalence of HIE observed was probably due to a publication bias. A significant association was found between etiology and seizure type: hemorrhage with autonomic seizures (P = 0.003), central nervous system (CNS) infection and stroke with clonic seizures (P = 0.042, P < 0.001, respectively), metabolic/vitamin‐related disorders, and inborn errors of metabolism with myoclonic seizures (P < 0.001). There were also specific electroencephalography (EEG) patterns seen with certain etiologies: vascular disorders and electrolyte imbalance with focal ictal discharges (P < 0.001, P = 0.049 respectively), vitamin‐related disorders with multifocal (P = 0.003), and all categories of genetic disorders with burst‐suppression (P < 0.001). Clonic and autonomic seizures were more frequently present with focal EEG abnormalities (P = 0.001 and P < 0.001), whereas tonic and myoclonic seizures present with burst‐suppression (P = 0.001, P = 0.005). In conclusion, our data suggest that specific associations of etiologies of neonatal seizures with distinct clinical features and EEG patterns might help in the decision to establish appropriate treatment. [ABSTRACT FROM AUTHOR]

• Neonatal cortical activity organizes into transient network states. • These states have specific spectral and connectivity patterns. • Vigilance states and brain injury affect the dynamics of these transient network states. • Quiet sleep has more brain-wide neural synchronization compared to active sleep. • Brain injury is associated with lowered dynamics in the cortical networks. Early neurodevelopment is critically dependent on the structure and dynamics of spontaneous neuronal activity; however, the natural organization of newborn cortical networks is poorly understood. Recent adult studies suggest that spontaneous cortical activity exhibits discrete network states with physiological correlates. Here, we studied newborn cortical activity during sleep using hidden Markov modeling to determine the presence of such discrete neonatal cortical states (NCS) in 107 newborn infants, with 47 of them presenting with a perinatal brain injury. Our results show that neonatal cortical activity organizes into four discrete NCSs that are present in both cardinal sleep states of a newborn infant, active and quiet sleep, respectively. These NCSs exhibit state-specific spectral and functional network characteristics. The sleep states exhibit different NCS dynamics, with quiet sleep presenting higher fronto-temporal activity and a stronger brain-wide neuronal coupling. Brain injury was associated with prolonged lifetimes of the transient NCSs, suggesting lowered dynamics, or flexibility, in the cortical networks. Taken together, the findings suggest that spontaneously occurring transient network states are already present at birth, with significant physiological and pathological correlates; this NCS analysis framework can be fully automatized, and it holds promise for offering an objective, global level measure of early brain function for benchmarking neurodevelopmental or clinical research. [ABSTRACT FROM AUTHOR]

Background: To analyze the findings in the background EEG activity of infants who suffered perinatal stroke.Methods: Eleven neonates born 2009-2014 diagnosed of ischemic stroke by MRI (three of them with multistroke) underwent continuous video-EEG monitoring. Visual and spectral (power spectrum and coherence) analyses of the background EEG was performed in three moments: 1) Onset of EEG recording (prior to initiate seizure treatment), 2) Post-ictal epoch (1-2 h after the last seizure), and 3) one-two days after seizure control. All children aged 2-6 years underwent neurodevelopmental assessment.Results: Discontinuity, asymmetry, asynchrony, transients, and relative power spectrum in δ and θ frequency bands increased significantly (p < 0.05) in the post-ictal epoch with respect to onset of EEG recording. After seizure control, discontinuity, asynchrony, and θ power spectrum no longer had significant differences with those found at onset of EEG recording. Significant differences between the ischemic and unaffected hemispheres were found in transients and in β coherence (p = 0.002; p = 0.001, respectively) exclusively in the post-ictal epoch. Seizure burden and time-to-control ranged 5-38 min and 0.5-40 h respectively. Currently, only one child is affected by spastic monoparesis. The intelligence quotients ranged 96-123.Conclusions: The background EEG can undergo significant changes in the post-ictal epoch due to the seizure activity triggered by the perinatal stroke. Most of these EEG changes involve all brain activity and not exclusively the ischemic hemisphere. Many of these modifications in the EEG background reverse following the seizure control. Video-EEG monitoring allows accurate/immediate diagnosis and rapid/intensive treatment of the stroke-associated seizures. [ABSTRACT FROM AUTHOR]

Objectives To measure changes in the visual interpretation of the EEG by the human expert for neonatal seizure detection when reducing the number of recording electrodes. Methods EEGs were recorded from 45 infants admitted to the neonatal intensive care unit (NICU). Three experts annotated seizures in EEG montages derived from 19, 8 and 4 electrodes. Differences between annotations were assessed by comparing intra-montage with inter-montage agreement (K). Results Three experts annotated 4464 seizures across all infants and montages. The inter-expert agreement was not significantly altered by the number of electrodes in the montage ( p  = 0.685, n  = 43). Reducing the number of EEG electrodes altered the seizure annotation for all experts. Agreement between the 19-electrode montage ( K 19,19  = 0.832) was significantly higher than the agreement between 19 and 8-electrode montages (d K  = 0.114; p  < 0.001, n  = 42) or 19 and 4-electrode montages (d K  = 0.113, p  < 0.001, n  = 43). Seizure burden and number were significantly underestimated by the 4 and 8-electrode montage ( p  < 0.001). No significant difference in agreement was found between 8 and 4-electrode montages (d K  = 0.002; p  = 0.07, n  = 42). Conclusions Reducing the number of EEG electrodes from 19 electrodes resulted in slight but significant changes in seizure detection. Significance Four-electrode montages for routine EEG monitoring are comparable to eight electrodes for seizure detection in the NICU. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) is an important clinical tool for monitoring neurological health. However, the required equipment, expertise, and patient preparation inhibits its use outside of tertiary care. Non-experts struggle to obtain high-quality EEG due to its low amplitude and artefact susceptibility. Wet electrodes are currently used, which require abrasive/conductive gels to reduce skin-electrode impedance. Advances in dry electrodes, which do not require gels, have simplified this process. However, the assessment of dry electrodes on neonates is limited due to health and safety barriers. This study presents a simulation framework for assessing the quality of EEG systems using a neonatal EEG database, without the use of human participants. The framework is used to evaluate a low-cost EEG acquisition system and compare performance of wet and dry (Micro Transdermal Interface Platforms (MicroTIPs), g.tec-g.SAHARA) electrodes using accurately acquired impedance models. A separate experiment assessing the electrodes on adult participants was conducted to verify the simulation framework’s efficacy. Dry electrodes have higher impedance than wet electrodes, causing a reduction in signal quality. However, MicroTIPs perform comparably to wet electrodes at the frontal region and g.tec-g.SAHARA performs well at the occipital region. Using the simulation framework, a 25dB signal-to-noise ratio (SNR) was obtained for the low-cost EEG system. The tests on adults closely matched the simulated results.

Automated analysis of the electroencephalographic (EEG) data for the brain monitoring of preterm infants has gained attention in the last decades. In this study, we analyze the complexity of neonatal EEG, quantified using multiscale entropy. The aim of the current work is to investigate how EEG complexity evolves during electrocortical maturation and whether complexity features can be used to classify sleep stages. First, we developed a regression model that estimates the postmenstrual age (PMA) using a combination of complexity features. Then, these features are used to build a sleep stage classifier. The analysis is performed on a database consisting of 97 EEG recordings from 26 prematurely born infants, recorded between 27 and 42 weeks PMA. The results of the regression analysis revealed a significant positive correlation between the EEG complexity and the infant's age. Moreover, the PMA of the neonate could be estimated with a root mean squared error of 1.88 weeks. The sleep stage classifier was able to discriminate quiet sleep from nonquiet sleep with an area under the curve (AUC) of 90%. These results suggest that the complexity of the brain dynamics is a highly useful index for brain maturation quantification and neonatal sleep stage classification. [ABSTRACT FROM AUTHOR]

Objective To develop a method for automated neonatal sleep state classification based on EEG that can be applied over a wide range of age. Methods We collected 231 EEG recordings from 67 infants between 24 and 45 weeks of postmenstrual age. Ten minute epochs of 8 channel polysomnography ( N = 323) from active and quiet sleep were used as a training dataset. We extracted a set of 57 EEG features from the time, frequency, and spatial domains. A greedy algorithm was used to define a reduced feature set to be used in a support vector machine classifier. Results Performance tests showed that our algorithm was able to classify quiet and active sleep epochs with 85% accuracy, 83% sensitivity, and 87% specificity. The performance was not substantially lowered by reducing the epoch length or EEG channel number. The classifier output was used to construct a novel trend, the sleep state probability index, that improves the visualisation of brain state fluctuations. Conclusions A robust EEG-based sleep state classifier was developed. It performs consistently well across a large span of postmenstrual ages. Significance This method enables the visualisation of sleep state in preterm infants which can assist clinical management in the neonatal intensive care unit. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To describe the neurological outcome of newborns with seizures. METHOD: Cohort study with newborns prospectively followed. Perinatal characteristics and etiological screening were related to outcome in a regression model. RESULTS: During the study 3659 newborns were admitted and 2.7% were diagnosed as having seizures. Hypoxic ischemic encephalopathy (51%) was the etiology more frequently associated to seizures and also to postneonatal epilepsy (53%). In the follow up 25 died during the acute neonatal illness and 9 during the first years of life, 19 were diagnosed as having post neonatal epilepsy, 35 had developmental delay and 11 an association among this two comorbidities. A significant association between abnormal postnatal EEG and neuroimaging to developmental delay (p=0.014, p=0.026) was observed. The group of newborns that had seizures presented an increased risk of developing epilepsy compared to newborns from the same cohort without seizures (19.3/100 vs. 1.8/100, pOBJETIVO: Avaliar o prognóstico neurológico de neonatos com crises convulsivas. MÉTODO: Estudo prospectivo, realizado em coorte de neonatos provenientes de hospital terciário. As características clínicas perinatais e os resultados de exames complementares foram correlacionados com prognóstico através de modelo de regressão logística. RESULTADOS: Durante o estudo 3659 neonatos foram internados, sendo que 101 apresentaram crises convulsivas (2,7%). A encefalopatia hipóxico-isquêmica foi a etiologia mais frequentemente associada às crises (51%). O seguimento evidenciou 25 óbitos no período neonatal e 9 durante os primeiros anos de vida, 19 lactentes desenvolveram epilepsia, 35 atraso no desenvolvimento e 11 associação entre os dois desfechos. O modelo de regressão logística aplicado mostrou associação significativa entre EEG pós neonatal anormal e neuroimagem anormal com atraso no desenvolvimento (p=0,014, p=0,026). Os neonatos em estudo, quando comparados aos demais da mesma coorte, que não apresentaram crises convulsivas tiveram maior probabilidade de desenvolver epilepsia (19,3/100 vs. 1,8/100, p