Neonatal Electroencephalogram Research Articles

Machine learning for forecasting initial seizure onset in neonatal hypoxic-ischemic encephalopathy.

This study was undertaken to develop a machine learning (ML) model to forecast initial seizure onset in neonatal hypoxic-ischemic encephalopathy (HIE) utilizing clinical and quantitative electroencephalogram (QEEG) features. We developed a gradient boosting ML model (Neo-GB) that utilizes clinical features and QEEG to forecast time-dependent seizure risk. Clinical variables included cord blood gas values, Apgar scores, gestational age at birth, postmenstrual age (PMA), postnatal age, and birth weight. QEEG features included statistical moments, spectral power, and recurrence quantification analysis (RQA) features. We trained and evaluated Neo-GB on a University of California, San Francisco (UCSF) neonatal HIE dataset, augmenting training with publicly available neonatal electroencephalogram (EEG) datasets from Cork University and Helsinki University Hospitals. We assessed the performance of Neo-GB at providing dynamic and static forecasts with diagnostic performance metrics and incident/dynamic area under the receiver operating characteristic curve (iAUC) analyses. Model explanations were performed to assess contributions of QEEG features and channels to model predictions. The UCSF dataset included 60 neonates with HIE (30 with seizures). In subject-level static forecasting at 30 min after EEG initiation, baseline Neo-GB without time-dependent features had an area under the receiver operating characteristic curve (AUROC) of .76 and Neo-GB with time-dependent features had an AUROC of .89. In time-dependent evaluation of the initial seizure onset within a 24-h seizure occurrence period, dynamic forecast with Neo-GB demonstrated median iAUC = .79 (interquartile range [IQR] .75-.82) and concordance index (C-index) = .82, whereas baseline static forecast at 30 min demonstrated median iAUC = .75 (IQR .72-.76) and C-index = .69. Model explanation analysis revealed that spectral power, PMA, RQA, and cord blood gas values made the strongest contributions in driving Neo-GB predictions. Within the most influential EEG channels, as the preictal period advanced toward eventual seizure, there was an upward trend in broadband spectral power. This study demonstrates an ML model that combines QEEG with clinical features to forecast time-dependent risk of initial seizure onset in neonatal HIE. Spectral power evolution is an early EEG marker of seizure risk in neonatal HIE.

Quantitative electroencephalogram in term neonates under different sleep states.

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 46weeks 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.

A multi-task and multi-channel convolutional neural network for semi-supervised neonatal artefact detection

Objective. Automated artefact detection in the neonatal electroencephalogram (EEG) is crucial for reliable automated EEG analysis, but limited availability of expert artefact annotations challenges the development of deep learning models for artefact detection. This paper proposes a semi-supervised deep learning approach for artefact detection in neonatal EEG that requires few labelled data by training a multi-task convolutional neural network (CNN). Approach. An unsupervised and a supervised objective were jointly optimised by combining an autoencoder and an artefact classifier in one multi-output model that processes multi-channel EEG inputs. The proposed semi-supervised multi-task training strategy was compared to a classical supervised strategy and other existing state-of-the-art models. The models were trained and tested separately on two different datasets, which contained partially annotated multi-channel neonatal EEG. Models were evaluated using the F1-statistic and the relevance of the method was investigated in the context of a functional brain age (FBA) prediction model. Main results. The proposed multi-task and multi-channel CNN methods outperformed state-of-the-art methods, reaching F1 scores of 86.2% and 95.7% on two separate datasets. The proposed semi-supervised multi-task training strategy was shown to be superior to a classical supervised training strategy when the amount of labels in the dataset was artificially reduced. Finally, we found that the error of a brain age prediction model correlated with the amount of automatically detected artefacts in the EEG segment. Significance. Our results show that the proposed semi-supervised multi-task training strategy can train CNNs successfully even when the amount of labels in the dataset is limited. Therefore, this method is a promising semi-supervised technique for developing deep learning models with scarcely labelled data. Moreover, a correlation between the error of FBA estimates and the amount of detected artefacts in the corresponding EEG segments indicates the relevance of artefact detection for robust automated EEG analysis.

Neonatal EEG graded for severity of background abnormalities in hypoxic-ischaemic encephalopathy

This report describes a set of neonatal electroencephalogram (EEG) recordings graded according to the severity of abnormalities in the background pattern. The dataset consists of 169 hours of multichannel EEG from 53 neonates recorded in a neonatal intensive care unit. All neonates received a diagnosis of hypoxic-ischaemic encephalopathy (HIE), the most common cause of brain injury in full term infants. For each neonate, multiple 1-hour epochs of good quality EEG were selected and then graded for background abnormalities. The grading system assesses EEG attributes such as amplitude, continuity, sleep–wake cycling, symmetry and synchrony, and abnormal waveforms. Background severity was then categorised into 4 grades: normal or mildly abnormal EEG, moderately abnormal EEG, majorly abnormal EEG, and inactive EEG. The data can be used as a reference set of multi-channel EEG for neonates with HIE, for EEG training purposes, or for developing and evaluating automated grading algorithms.

Background suppression of electrical activity is a potential biomarker of subsequent brain injury in a rat model of neonatal hypoxia-ischemia.

Electrographic seizures and abnormal background activity in the neonatal electroencephalogram (EEG) may differentiate between harmful versus benign brain insults. Using two animal models of neonatal seizures, electrical activity was recorded in freely behaving rats and examined quantitatively during successive time periods with field-potential recordings obtained shortly after the brain insult (i.e., 0-4 days). Single-channel, differential recordings with miniature wireless telemetry were used to analyze spontaneous electrographic seizures and background suppression of electrical activity after 1) hypoxia-ischemia (HI), which is a model of neonatal encephalopathy that causes acute seizures and a large brain lesion with possible development of epilepsy, 2) hypoxia alone (Ha), which causes severe acute seizures without an obvious lesion or subsequent epilepsy, and 3) sham control rats. Background EEG exhibited increases in power as a function of age in control animals. Although background electrical activity was depressed in all frequency bands immediately after HI, suppression in the β and γ bands was greatest and lasted longest. Spontaneous electrographic seizures were recorded, but only in a few HI-treated animals. Ha-treated rat pups were similar to sham controls, they had no subsequent spontaneous electrographic seizures after the treatment and background suppression was only briefly observed in one frequency band. Thus, the normal age-dependent maturation of electrical activity patterns in control animals was significantly disrupted after HI. Suppression of the background EEG observed here after HI-induced acute seizures and subsequent brain injury may be a noninvasive biomarker for detecting severe brain injuries and may help predict subsequent epilepsy.NEW & NOTEWORTHY Biomarkers of neonatal brain injury are needed. Hypoxia-ischemia (HI) in immature rat pups caused severe brain injury, which was associated with strongly suppressed background EEG. The suppression was most robust in the β and γ bands; it started immediately after the HI injury and persisted for days. Thus, background suppression may be a noninvasive biomarker for detecting severe brain injuries and may help predict subsequent epilepsy.

Expert consensus on grading management of electroencephalogram monitoring in neonates.

Neonatal electroencephalogram (EEG) monitoring guidelines have been published by American Clinical Neurophysiology Society, and the expert consensus on neonatal amplitude-integrated EEG (aEEG) has also been published in China. It is difficult to strictly follow the guidelines or consensus for EEG monitoring in different levels of neonatal units due to a lack of EEG monitoring equipment and professional interpreters. The Subspecialty Group of Neonatology, Society of Pediatrics, Chinese Medical Association, established an expert group composed of professionals in neonatology, pediatric neurology, and brain electrophysiology to review published guidelines and consensuses and the articles in related fields and propose grading management recommendations for EEG monitoring in different levels of neonatal units. Based on the characteristics of video EEG and aEEG, local medical resources, and disease features, the expert group recommends that video EEG and aEEG can complement each other and can be used in different levels of neonatal units. The consensus also gives recommendations for promoting collaboration between professionals in neonatology, pediatric neurology, and brain electrophysiology and implementing remote EEG monitoring.

Lateralized neonatal EEG coherence during sleep predicts language outcome.

Background:Enriched language exposure may benefit infants in the neonatal intensive care unit. We hypothesized that changes in neonatal EEG coherence during sleep, in response to maternal voice exposure, predict language development.Methods:Convalescent neonates underwent 12-hour polysomnography. A recording of the mother’s voice was randomized to continuous playback in the first or second 6 hours. We calculated the imaginary coherence (ICOH – a measure of functional connectivity) between EEG leads. Spearman correlations were computed between ICOH and 18-month Bayley-III Language scores.Results:Thirty-five neonates were included (N=18 33-to-<35 weeks gestation; N=17 ≥35 weeks). Predictive value of ICOH during neonatal non-rapid eye movement (NREM) sleep was left-lateralized and varied with gestational age and voice playback. ICOH in the left-hemispheric (C3-Cz; T3-Cz) channels across multiple EEG frequency bands was associated with 18-month language scores (rho= −0.34 to −0.48). The association was driven by neonates born at 33-34 weeks gestation, and a trend suggested a possible effect of maternal voice at some EEG frequencies. Right-hemisphere ICOH (C4-Cz; T4-Cz) was not associated with language outcome.Conclusion:Left-hemispheric EEG functional connectivity during neonatal NREM sleep shows early signs of physiologic asymmetry that may predict language development. We speculate that sleep analyses could have unique prognostic value.

An Assessment of the Relationship of Clinico-Etiological Profile with Electroencephalogram and Neuroimaging in Neonates with Seizures Admitted to Besat Hospital in Hamadan: A seven-year experience

An Assessment of the Relationship of Clinico-Etiological Profile with Electroencephalogram and Neuroimaging in Neonates with Seizures Admitted to Besat Hospital in Hamadan: A seven-year experience

Normal Neonatal Electroencephalogram at a Glance

Interpreting neonatal electroencephalogram (EEG) presents a challenge owing to rapid evolution of EEG patterns occurring during brain maturation in the neonatal period and rich variety of normal patterns of EEG activity, which is difficult to categorize completely. Furthermore, the description of some aspects during maturation varies in different studies. Neonatal EEG is unfamiliar to most neurologists, and its interpretation requires knowledge of the physiological markers of electrogenesis maturation. The purpose of this review was to provide health-care professionals in the neonatal intensive care unit with guidance on the more common normal maturational features of the neonatal EEG. A simplified layout with the essential elements of normal neonatal EEG is included.

Comparison of neonatal electroencephalogram changes in three month old neonates with seizure in Bu-Ali Hospital of Ardabil city, Iran 2017

Background: The prevalence of neonatal seizure in term neonates is 3 per 1000 births, but in preterm newborns is 50 per 1000 births. Babies who have seizures are at high risk of death or neurological disabilities. Seizure is often the first sign of neonatal dysfunction and may be effective in long-term prognosis. EEG is the only available method for the diagnosis of seizures in neonates. Therefore, authors compared EEG changes in the first EEG infant seizure with 3 months of age in newborns who referred to the Aristotelian hospital in Bouali Hospital.Methods: The present study is a cross-sectional descriptive analytical method. In this study, neonates referred to Ardabil Booali Hospital, EEG, were screened for seizure and EEG was monitored 3 months later and the results were evaluated. Finally, all the data were entered into the SPSS-24 statistical analysis program and authors analyzed the data according to the type of variables by statistical tests.Results: In this study, 50 neonates with seizure were enrolled in this study, 70% of which had an average age of 14.92 days. 80% of infants were born at the time of term. The average birth weight was 3.208 kg. 6.2% of infants had abnormal CT scan findings, with an IVH infant and one baby showing brain edema. In this study, only 14% of neonates with abnormal brain strain were observed in the neonatal period and near the seizure. However, after 3 months, 40% of infants experienced abnormal brain stroke findings. Among the changes in EEG with age (p=0.173), gestational age (p=0.616), gender (p=0.176), seizure (p=0.145), neonatal hypoglycemia (p=0.594), hypocalcaemia (p=0.607) no statistic was found.Conclusions: The results of this study showed that a small percentage of neonates had abnormal EEG in the neonate, but after 3 months of seizure, the larger percentage of them found abnormal EEG.

The Entropy-Based Time Domain Feature Extraction for Online Concept Drift Detection

Most of time series deriving from complex systems in real life is non-stationary, where the data distribution would be influenced by various internal/external factors such that the contexts are persistently changing. Therefore, the concept drift detection of time series has practical significance. In this paper, a novel method called online entropy-based time domain feature extraction (ETFE) for concept drift detection is proposed. Firstly, the empirical mode decomposition based on extrema symmetric extension is used to decompose time series, where features in various time scales can be adaptively extracted. Meanwhile, the end point effect caused by traditional empirical mode decomposition can be avoided. Secondly, by using the entropy calculation, the time-domain features are coarse-grained to quantify the structure and complexity of the time series, among which six kinds of entropy are used for discussion. Finally, a statistical process control method based on generalized likelihood ratio is used to monitor the change of the entropy, which can effectively track the mean and amplitude of the time series. Therefore, the early alarm of concept drift can be given. Synthetic data sets and neonatal electroencephalogram (EEG) recordings with seizures annotations data sets are used to validate the effectiveness and accuracy of the proposed method.

Adverse effects of maternal dioxin exposure on fetal brain development before birth assessed by neonatal electroencephalography (EEG) leading to poor neurodevelopment; a 2-year follow-up study

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.

Surrogate data test for nonlinearity of EEG signals: A newborn EEG burst suppression case study

This paper applies the surrogate data method to investigate the presence of nonlinearity in neonatal electroencephalogram (EEG) burst suppression (B/S) patterns in order to rationalize the use of nonlinear methods for automated detection of such patterns. To generate surrogate data, the statically transformed autoregressive process (STAP) algorithm is deployed, and, the correlation dimension (CD) and asymmetry due to time reversal (REV) are applied as discriminating statistics. The results of the surrogate data test demonstrate the nonlinearity characteristic of real neonatal EEG signals during both burst and suppression phases at the 0.05 significance level. The evidence of nonlinearity is found in 90% and 87% of bursts and suppressions respectively. Furthermore, the ability of nonlinear tools in detecting B/S patterns in multichannel neonatal EEG signals is investigated using receiver operating characteristic analysis. The experimental results show that the CD outperforms existing methods based on the nonlinear energy operator.

Measuring the Severity of Neonatal Seizures: Temporal-Spatial Burden.

The American Clinical Neurophysiology Society recommends measuring neonatal seizures' severity by their frequency (number of seizures-anywhere per hour), burden (percentage of time with seizures-anywhere), or on a region-by-region, temporal-spatial basis. This study compares two reduced-channel montages for temporal-spatial seizure burden analyses and examines the agreement of seizures' quantification among these three methodologies. A convenience sample of 10 neonatal electroencephalograms was annotated for the beginnings and ends of seizures, which appeared anywhere in the full neonatal montage, then repeated on a more precise, region-by-region basis using 2 reduced-channel montages A and B. Seizure severity was measured by seizures-anywhere frequency, seizures-anywhere burden, and temporal-spatial seizure burdens using montages A and B. The results were compared by measuring their correlation and by linear regression modeling. Seizures-anywhere frequency was correlated with seizures-anywhere burden (ρ = 0.77). However, a narrow range of seizures-anywhere frequencies corresponded with a broad range of seizures-anywhere burdens. Although there was high correlation between seizures-anywhere burdens and temporal-spatial seizure burdens (ρ = 0.92 montage A, ρ = 0.90 montage B), seizures-anywhere burdens were insensitive to variations in the spatial aspects of seizures, which were highly prevalent even in this small sample set. After adjusting for intrareader variability, the temporal-spatial seizure burdens measured by montages A and B were not significantly different (P = 0.56). The severity of neonatal seizures is poorly represented by simple measures such as seizures-anywhere frequencies or burdens. The use of temporal-spatial seizure burden measurements is supported in work where great precision in quantifying neonatal seizures is required.

Amplitude Integrated Electroencephalogram as a Prognostic Tool in Neonates with Hypoxic-Ischemic Encephalopathy: A Systematic Review.

IntroductionPerinatal management and prognostic value of clinical evaluation and diagnostic tools have changed with the generalization of therapeutic hypothermia (TH) in infants with hypoxic-ischemic encephalopathy (HIE)Aimto ascertain the prognostic value of amplitude integrated electroencephalogram (aEEG) in neonates with HIE considering hours of life and treatment with TH.MethodsA systematic review was performed. Inclusion criteria were studies including data of neonates with HIE, treated or not with TH, monitored with aEEG and with neurodevelopmental follow-up of at least 12 months. The period of bibliographic search was until February 2016. No language restrictions were initially applied. Consulted databases were MEDLINE, Scopus, CINHAL and the Spanish language databases GuiaSalud and Bravo. Article selection was performed by two independent reviewers. Quality for each individual paper selected was evaluated using QUADAS-2. Review Manager (RevMan) version 5.3 software was used. Forest plots were constructed to graphically show sensitivity and specificity for all included studies, separating patients treated or not with hypothermia. Summary statistics were estimated using bivariate models and random effects approaches with the R package MADA from summary ROC curves. Meta-regression was used to estimate heterogeneity and trends.Resultsfrom the 403 articles initially identified, 17 were finally included and critically reviewed. In infants not treated with hypothermia the maximum reliability of an abnormal aEEG background to predict death or moderate/severe disability was at 36 hours of life, when a positive post-test probability of 97.90% was achieved (95%CI 88.40 to 99.40%). Positive likelihood ratio (+LR) at these hours of life was 26.60 (95%CI 4.40 to 94.90) and negative likelihood ratio (-LR) was 0.23 (95%CI 0.10 to 0.44). A high predictive value was already present at 6 hours of life in this group of patients, with a positive post-test probability of 88.20% (95%CI 79.80 to 93%) and a +LR of 4.34 (95%CI 2.31 to 7.73). In patients treated with TH the maximum predictive reliability was achieved at 72 hours of life (post-test probability of 95.70%, 95%CI 84.40 to 98.50%). +LR at this age was 24.30 (95%CI 5.89 to 71.30) and–LR was 0.40 (95%CI 0.25 to 0.57). Predictive value of aEEG at 6 hours of life was low in these patients (59.10%, 95%CI 55.70 to 63%).ConclusionThis study confirms that aEEG´s background activity, as recorded during the first 72 hours after birth, has a strong predictive value in infants with HIE treated or not with TH. Predictive values of traces throughout the following 72 hours are a helpful guide when considering and counselling parents about the foreseeable long-term neurological outcome

Abstract 11740: Fetal and Neonatal Markers of Neurodevelopmental Outcome in Congenital Heart Disease

Objectives: Children with congenital heart disease (CHD) are at risk for abnormal neurodevelopment (ND). We evaluated associations between fetal Doppler and biometry measures, neonatal electroencephalogram (EEG) and 18-month ND. Methods: Fetuses with hypoplastic left heart syndrome (HLHS), transposition of the great arteries (TGA), and tetralogy of Fallot (TOF) had middle cerebral (MCA) and umbilical artery (UA) Doppler velocities, as well as biometry such as head (HC) and abdominal circumference (AC), prospectively recorded at 20-25 (F1), 26-32 (F2), and 33-39 (F3) wks gestational age (GA). Pulsatility indices (PI) with GA-derived z-scores and cerebral-to-placental resistance (CPR) ratios were calculated. Neonatal high-density EEG was preformed preoperatively and the Bayley Scales of Infant Development-III were assessed at 18-months. Factor analysis was used to reduce the number of EEG predictors used in regression analysis. Results: Among 56 CHD fetuses (N=19 HLHS, N=16 TGA, N=21 TOF) who underwent preoperative EEG, ND scores are available for 33 to date. Cardiac subtype was highly associated with EEG and was considered in all models. Cognition scores were predicted by CPR&lt; 1 ever (B=-15.7, P=0.002) and HC/AC at F2 (B=-130, P=0.013, R 2 =0.42). Language scores were predicted by UA PI z-score at F1 (B=-9.6, P=0.005, R 2 =0.27). Motor scores were predicted by UA PI z-score at F1 (B=-3.9, P=0.085), HLHS (B=-15, P&lt;0.001), EFW%ile (B=0.374, P=0.007), and delta band right parietal and right temporal log power in active sleep (B=3.9, P=0.045, R 2 =0.61). Conclusion: Lower umbilical artery pulsatility at 20-25 wks GA was associated with higher 18-month Language and Motor scores. A diagnosis of HLHS predicted poorer Motor scores. Increased EEG power in the parietal and temporal region of the right brain predicted higher Motor scores. A larger abdomen relative to head at 26-32 wks was associated with improved cognition while diminished cerebrovascular compared with placental resistance predicted poorer cognition, similar to what has been observed in the growth restricted fetus. Further investigation is needed to confirm these hypothesis-generating findings.

Neonatal seizure detection using atomic decomposition with a novel dictionary.

Atomic decomposition (AD) can be used to efficiently decompose an arbitrary signal. In this paper, we present a method to detect neonatal electroencephalogram (EEG) seizure based on AD via orthogonal matching pursuit using a novel, application-specific, dictionary. The dictionary consists of pseudoperiodic Duffing oscillator atoms which are designed to be coherent with the seizure epochs. The relative structural complexity (a measure of the rate of convergence of AD) is used as the sole feature for seizure detection. The proposed feature was tested on a large clinical dataset of 826 h of EEG data from 18 full-term newborns with 1389 seizures. The seizure detection system using the proposed dictionary was able to achieve a median receiver operator characteristic area of 0.91 (IQR 0.87-0.95) across 18 neonates.

Bispectral Index and Lower Margin Amplitude of the Amplitude-Integrated Electroencephalogram in Neonates

Background: The lower margin amplitude (LMA) of the amplitude-integrated electroencephalogram (aEEG) is suppressed in neonates during deep sedation, a feature that is attributed to the bispectral index (BIS) in adults. Objective: We compare the BIS and the LMA of the aEEG in neonates. Methods: Thirty neurologically healthy neonates between 37 and 44 weeks postmenstrual age were included in this study. Twenty patients received sedoanalgesic therapy for various reasons. BIS and aEEG recordings were performed simultaneously. The digital data were imported in the numerical software environment Matlab®. The LMA of the aEEG was computed on a 1-min time scale and synchronized with the BIS data. The correlation between the time-dependent variables BIS and LMA was estimated using the Spearman rank correlation index. Results: The median correlation between BIS and LMA was 0.3. Inclusion of recordings of high signal quality only into analysis improved the median correlation index to 0.6. Conclusions: We found a light-to-moderate correlation between BIS and LMA in our study cohort and a good correlation in the subgroup with high signal quality.

Evaluation of somatosensory cortical processing in extremely preterm infants at term with MEG and EEG

ObjectivePrior studies on extremely preterm infants have reported long-term prognostic value of absent secondary somatosensory cortex (SII) responses in magnetoencephalography (MEG) at term. The present work (i) further examines the potential added value of SII responses in neonatal neurological evaluation of preterm infants, and (ii) tests whether SII responses are detectable in routine neonatal electroencephalogram complemented with median nerve stimulation (EEG-SEP). MethodsAltogether 29 infants born <28 gestational weeks underwent MEG, MRI, and neonatal neurological examination at term age, and Hempel neurological examination at 2-years corrected age. Term-age EEG-SEP was available for seven infants. ResultsWhile in neonatal neurological examination severely abnormal finding predicted unfavorable outcome in 2/2 infants, outcome was unfavorable also in 3/9 (33%) moderately abnormal and in 5/18 (28%) mildly abnormal/normal infants. Of these eight infants four had unilaterally absent SII responses in MEG, compared with only two of the 24 infants with favorable outcome. Furthermore, SII responses (when present in MEG) were also usually detectable in EEG-SEP. ConclusionsComplementing clinical EEG recording with SEP holds promise for valuable extension of neonatal neurophysiological assessment. SignificanceMultimodal study of EEG and sensory evoked responses is informative, safe, and cheap, and it can be readily performed at bedside.

Maturational changes of neonatal electroencephalogram: A comparison between intra uterine and extra uterine development

ObjectivesTo evaluate electroencephalogram (EEG) patterns in very low birth weight premature newborns when reaching term-to-term neonates, thereby comparing extra uterine to intra uterine development. MethodsEEG recordings were obtained between 2 and 14days of life in 47 preterm (<37weeks) very low birth weight (⩽1500g) newborns and repeated when reaching term age (38–42weeks) in a subsample (n=22). EEG recordings were also obtained in term newborns. Clinical evaluation was performed at birth and at 6 and 12months. ResultsRelative to the first EEG, preterm newborns showed a reduction in delta brushes and interburst intervals and increased interhemispheric synchrony, suggesting an improvement in EEG maturation. Further organization in sleep patterns, like a reduction in total sleep time and phase shifts, were also observed. However, when reaching term, preterm newborns still had less mature EEG patterns, phase shifts and transitional sleep than matched controls. Background abnormalities and dysmaturity in preterm newborns during the first EEG predicted adverse neurological outcome later on. ConclusionsEEG patterns in preterm very low birth weight newborns might be influenced by extra uterine development. Specific EEG abnormalities are related to increased risk of neurological disorders. SignificanceExtra uterine development of very low birth weight delays the acquisition of maturational EEG patterns.