Search

• 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]

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]

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]

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]

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]

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]

Early intermittent cortical activity is thought to play a crucial role in the growth of neuronal network development, and large scale brain networks are known to provide the basis for higher brain functions. Yet, the early development of the large scale synchrony in cortical activations is unknown. Here, we tested the hypothesis that the early intermittent cortical activations seen in the human scalp EEG show a clear developmental course during the last trimester of pregnancy, the period of intensive growth of cortico-cortical connections. We recorded scalp EEG from altogether 22 premature infants at post-menstrual age between 30 and 44 weeks, and the early cortical synchrony was quantified using recently introduced activation synchrony index (ASI). The developmental correlations of ASI were computed for individual EEG signals as well as anatomically and mathematically defined spatial subgroups. We report two main findings. First, we observed a robust and statistically significant increase in ASI in all cortical areas. Second, there were significant spatial gradients in the synchrony in fronto-occipital and left-to-right directions. These findings provide evidence that early cortical activity is increasingly synchronized across the neocortex. The ASI-based metrics introduced in our work allow direct translational comparison to in vivo animal models, as well as hold promise for implementation as a functional developmental biomarker in future research on human neonates. [ABSTRACT FROM AUTHOR]

Objective To examine sleep–wake cycle (SWC) composition of healthy term infants in the immediate postnatal period using EEG, and investigate factors that might influence it. Methods Multichannel video-EEG was recorded for a median of 61.9 min (IQR: 60.0–69.3). The absolute and relative scores of sleep states were calculated for each infant’s recording. Parametric/non-parametric statistical tests and multiple linear regression analysis were used to investigate the influence of perinatal factors on SWC composition. Results Eighty healthy term infants aged 1–36 h were studied. A well-developed SWC was evident as early as within the first 6 h after birth. The mean (SD) percentage of active sleep (AS) was 52.1% (12.9) and quiet sleep (QS) was 38.6% (12.5). AS was longer and QS shorter in infants delivered by elective caesarean section (CS) compared to infants delivered by vaginal delivery or emergency CS. Conclusions This is the first large cohort EEG study that has quantified neonatal sleep. SWC is clearly present immediately after birth, it is dominated by AS, and is influenced by mode of delivery. Significance This knowledge of the early neonatal EEG/SWC can be used as reference data for EEG studies of neurologically compromised infants. [ABSTRACT FROM AUTHOR]

Objective: This work presents a novel automated system to classify the severity of hypoxic-ischemic encephalopathy (HIE) in neonates using EEG. Methods: A cross disciplinary method is applied that uses the sequences of short-term features of EEG to grade an hour long recording. Novel post-processing techniques are proposed based on majority voting and probabilistic methods. The proposed system is validated with one-hour-long EEG recordings from 54 full term neonates. Results: An overall accuracy of 87% is achieved. The developed grading system has improved both the accuracy and the confidence/quality of the produced decision. With a new label 'unknown' assigned to the recordings with lower confidence levels an accuracy of 96% is attained. Conclusion: The statistical long-term model based features extracted from the sequences of short-term features has improved the overall accuracy of grading the HIE injury in neonatal EEG. Significance: The proposed automated HIE grading system can provide significant assistance to healthcare professionals in assessing the severity of HIE. This represents a practical and user friendly implementation which acts as a decision support system in the clinical environment. Its integration with other EEG analysis algorithms may improve neonatal neurocritical care. [ABSTRACT FROM AUTHOR]

Objective: To assess how the recording montage in the neonatal EEG influences the detection of cortical source signals and their phase interactions. Methods: Scalp EEG was simulated by forward modeling 20-200 simultaneously active sources covering the cortical surface of a realistic neonatal head model. We assessed systematically how the number of scalp electrodes (11-85), analysis montage, or the size of cortical sources affect the detection of cortical phase synchrony. Statistical metrics were developed for quantifying the resolution and reliability of the montages. Results: The findings converge to show that an increase in the number of recording electrodes leads to a systematic improvement in the detection of true cortical phase synchrony. While there is always a ceiling effect with respect to discernible cortical details, we show that the average and Laplacian montages exhibit superior specificity and sensitivity as compared to other conventional montages. Conclusions: Reliability in assessing true neonatal cortical synchrony is directly related to the choice of EEG recording and analysis configurations. Because of the high conductivity of the neonatal skull, the conventional neonatal EEG recordings are spatially far too sparse for pertinent studies, and this loss of information cannot be recovered by re-montaging during analysis. Significance: Future neonatalEEG studies will need prospective planning of recording configuration to allow analysis of spatial details required by each study question. Our findings also advice about the level of details in brain synchrony that can be studied with existing datasets or by using conventional EEG recordings. [ABSTRACT FROM AUTHOR]

Objective: The objective of this study was to validate the performance of a seizure detection algorithm (SDA) developed by our group, on previously unseen, prolonged, unedited EEG recordings from 70 babies from 2 centres. Methods: EEGs of 70 babies (35 seizure, 35 non-seizure) were annotated for seizures by experts as the gold standard. The SDA was tested on the EEGs at a range of sensitivity settings. Annotations from the expert and SDA were compared using event and epoch based metrics. The effect of seizure duration on SDA performance was also analysed. Results: Between sensitivity settings of 0.5 and 0.3, the algorithm achieved seizure detection rates of 52.6-75.0%, with false detection (FD) rates of 0.04-0.36 FD/h for event based analysis, which was deemed to be acceptable in a clinical environment. Time based comparison of expert and SDA annotations using Cohen's Kappa Index revealed a best performing SDA threshold of 0.4 (Kappa 0.630). The SDA showed improved detection performance with longer seizures. Conclusion: The SDA achieved promising performance and warrants further testing in a live clinical evaluation. Significance: The SDA has the potential to improve seizure detection and provide a robust tool for comparing treatment regimens. [ABSTRACT FROM AUTHOR]

Objective To define how fontanels affect scalp EEG potentials in neonates. Method Realistic finite element method head models were generated with and without fontanels. The electrical activity of the whole cortex was simulated using distributed 54,620 concurrently active cortical dipoles with a uniform random distribution of current densities (0–40 μA/cm 2 ). The overall effects of fontanels on scalp potentials were calculated from finite element forward solution in the vicinity the fontanel region by relative difference measure ( RDM ∗ ) and magnification factor ( MAG ), and the skull conductivity was systematically varied from 0.003 to 0.3 S/m. Results The neonatal scalp EEG topographies are comparable in models with and without fontanels, with highest amplitudes directly above the anterior fontanel. Quantitatively, comparison of these models elicits negligible differences ( RDM ∗ ∼2% and MAG ∼1.0). However, fontanel contribution to scalp potential was shown to increase sharply with decreases in skull conductivity. Conclusions These results suggest that fontanels may affect neonatal scalp EEG much less than traditionally assumed, and the effect is strongly dependent on skull conductivity. Significance Most neonatal EEG studies can be adequately performed without assuming distortions by fontanels. [ABSTRACT FROM AUTHOR]

Background Sleep organization in neonates is an established predictor of neurological outcome and can be evaluated through the concordance between EEG and behavioral parameters. Aims To evaluate the correlation between sleep stages and behavioral states in neonates. Study design Longitudinal study performed in a birth-cohort of preterm low birth weight neonates. Subjects Twenty five neonates, 15 preterm (gestational age between 27 and 33 weeks) and low birth weight (800-1500 g) and 10 full-term neonates that served as controls. Measures All neonates were submitted to video-electroencephalography of, at least, 60 minute duration. The preterm during the first 15 days of life and, subsequently, at 38-42 weeks of conceptional age. The full-term between the 1st and 2nd days of life. The characterization of sleep stages by EEG parameters and behavioral states (based on Prechtl scale) was performed independently by previously trained researchers. Results Active sleep (AS) was the predominant sleep stage in the three groups. Preterm neonates had an increase in concordance between state 1 and quiet sleep (QS) from the 1st to the 2nd EEG (p < 0.001), however in both observations it remained inferior when compared to state 2 and AS (p < 0.001). Concordance between AS and state 2 was similar (p = 0.567). Conclusions Concordance between EEG and behavior is lower in QS in preterm and full-term neonates when compared to AS. Extra-uterine development of preterm neonates seems to accelerate concordance in QS. Prechtl behavior scale proved to be useful in preterm as percentage of concordance was similar in AS in the groups studied. [ABSTRACT FROM AUTHOR]

Summary Objective To evaluate whether wearing a wool cap, a routine practice used to prevent heat loss in premature infants, affects interpretation of electroencephalogram spectral analysis. Methods Eighteen premature infants (median gestational age 28 weeks, range 23–32) without neurological complications were randomized to two channel (C3, C4 referred to Cz) digital electroencephalogram recordings with (90 min) and without (90 min) wearing wool cap, at 4 days of life. Electroencephalogram was analyzed automatically by measurement of burst suppression ratio and asymmetry index and by Fast Fourier Transform to calculate total absolute spectral power; relative spectral power in the δ (0.5–3.5 Hz), θ (4–7.5 Hz), α (8–12.5 Hz), and β (13–30 Hz) frequency bands; spectral edge frequency; and mean dominant frequency. Results The use of wool cap had no effect on all electroencephalogram parameters considered. Gestational age showed an effect on relative spectral power of all considered bands, spectral edge frequency and mean dominant frequency, while no effect was seen on burst suppression ratio and asymmetry index. Neonates born at gestational weeks lower than 28 had significantly higher relative power in the δ band and lower relative power in the α and β bands. Conclusions Heat loss prevention using wool cap does not affect interpretation of spectral electroencephalogram. Spectral values in our group of very premature infants without neurological complications correspond to normal data reported in the literature. Maturation changes consist of reduction of relative power of the δ band, spectral edge frequency and mean dominant frequency. [ABSTRACT FROM AUTHOR]

Highlights: [•] Preterm neonates when matched for conceptional age to term controls have less mature EEG patterns. [•] Sleep organization of preterm neonates is influenced by extra uterine development. [•] Background abnormalities recorded in neonatal EEG might predict cerebral palsy. [ABSTRACT FROM AUTHOR]

Highlights: [•] Concerns of possible electrode warming have precluded EEG–fMRI co-registrations in human neonates. [•] Our safety study, using a wide range of fMRI sequences, shows no unacceptable rise in electrode temperatures during fMRI and structural T1 sequences. [•] The present findings with a real-size phantom open up a possibility to carry out the first EEG–fMRI recordings in human neonates. [ABSTRACT FROM AUTHOR]

Abstract: Background: Animal experiments have suggested that the quality of the early intermittent brain activity is important for shaping neuronal connectivity during developmental phase that corresponds to early prematurity. This is a pilot study aiming to assess whether spontaneous activity transients (SAT) in the early preterm babies are affected by drugs that are routinely used in neonatal intensive care. Methods: We collected retrospectively seventeen EEG recordings (15 babies, conceptional age 26–33weeks, no brain lesions) that were divided into groups according to drug administration at the time of EEG: phenobarbital, fentanyl, theophylline, and controls. SATs were extracted from the EEG for further analysis with several advanced time-series analysis paradigms. Results: The visual appearance of SATs was unaffected by drugs. Phenobarbital reduced the total power of the SAT events. Both fentanyl and phenobarbital reduced the length of SATs, and enhanced the oscillations at higher frequencies. Theophylline reduced the oscillatory activity at middle frequencies during SAT, but enhanced oscillations at higher frequencies during time-period prior to SAT. Conclusions: Our findings suggest, that (i) all drugs examined affect brain activity in ways that are not seen in the visual EEG interpretation, and that (ii) both acute and long term (i.e. developmental) effects of these drugs on brain may warrant more attention as a part of optimizing preterm neurological care. [Copyright &y& Elsevier]

Abstract: Automated methods of neonatal EEG seizure detection attempt to highlight the evolving, stereotypical, pseudo-periodic, nature of EEG seizure while rejecting the nonstationary, modulated, coloured stochastic background in the presence of various EEG artefacts. An important aspect of neonatal seizure detection is, therefore, the accurate representation and detection of pseudo-periodicity in the neonatal EEG. This paper describes a method of detecting pseudo-periodic components associated with neonatal EEG seizure based on a novel signal representation; the nonstationary frequency marginal (NFM). The NFM can be considered as an alternative time-frequency distribution (TFD) frequency marginal. This method integrates the TFD along data-dependent, time-frequency paths that are automatically extracted from the TFD using an edge linking procedure and has the advantage of reducing the dimension of a TFD. The reduction in dimension simplifies the process of estimating a decision statistic designed for the detection of the pseudo-periodicity associated with neonatal EEG seizure. The use of the NFM resulted in a significant detection improvement compared to existing stationary and nonstationary methods. The decision statistic estimated using the NFM was then combined with a measurement of EEG amplitude and nominal pre- and post-processing stages to form a seizure detection algorithm. This algorithm was tested on a neonatal EEG database of 18 neonates, 826h in length with 1389 seizures, and achieved comparable performance to existing second generation algorithms (a median receiver operating characteristic area of 0.902; IQR 0.835–0.943 across 18 neonates). [Copyright &y& Elsevier]

Abstract: Objective: To test the hypothesis that quantitative EEG (qEEG) measures are associated with a grading of HIE based on the visual interpretation of neonatal EEG (EEG/HIE). Methods: Continuous multichannel video-EEG data were recorded for up to 72h. One-hour EEG segments from each recording were visually analysed and graded by two electroencephalographers (EEGers) blinded to clinical data. Several qEEG measures were calculated for each EEG segment. Kruskal–Wallis testing with post hoc analysis and multiple linear regression were used to assess the hypothesis. Results: Fifty-four full-term infants with HIE were studied. The relative delta power, skewness, kurtosis, amplitude, and discontinuity were significantly different across four EEG/HIE grades (p <0.05). A linear combination of these qEEG measures could predict the EEG/HIE grade assigned by the EEGers with an accuracy of 89%. Conclusion: Quantitative analysis of background EEG activity has shown that measures based on the amplitude, frequency content and continuity of the EEG are associated with a visual interpretation of the EEG performed by experienced EEGers. Significance: Identifying qEEG measures that can separate between EEG/HIE grades is an important first step towards creating a classifier for automated detection of EEG/HIE grades. [Copyright &y& Elsevier]

Abstract: Objective: This study discusses an appropriate framework to measure system performance for the task of neonatal seizure detection using EEG. The framework is used to present an extended overview of a multi-channel patient-independent neonatal seizure detection system based on the Support Vector Machine (SVM) classifier. Methods: The appropriate framework for performance assessment of neonatal seizure detectors is discussed in terms of metrics, experimental setups, and testing protocols. The neonatal seizure detection system is evaluated in this framework. Several epoch-based and event-based metrics are calculated and curves of performance are reported. A new metric to measure the average duration of a false detection is proposed to accompany the event-based metrics. A machine learning algorithm (SVM) is used as a classifier to discriminate between seizure and non-seizure EEG epochs. Two post-processing steps proposed to increase temporal precision and robustness of the system are investigated and their influence on various metrics is shown. The resulting system is validated on a large clinical dataset of 267h. Results: In this paper, it is shown how a complete set of metrics and a specific testing protocol are necessary to extensively describe neonatal seizure detection systems, objectively assess their performance and enable comparison with existing alternatives. The developed system currently represents the best published performance to date with an ROC area of 96.3%. The sensitivity and specificity were ∼90% at the equal error rate point. The system was able to achieve an average good detection rate of ∼89% at a cost of 1 false detection per hour with an average false detection duration of 2.7min. Conclusions: It is shown that to accurately assess the performance of EEG-based neonatal seizure detectors and to facilitate comparison with existing alternatives, several metrics should be reported and a specific testing protocol should be followed. It is also shown that reporting only event-based metrics can be misleading as they do not always reflect the true performance of the system. Significance: This is the first study to present a thorough method for performance assessment of EEG-based seizure detection systems. The evaluated SVM-based seizure detection system can greatly assist clinical staff, in a neonatal intensive care unit, to interpret the EEG. [Copyright &y& Elsevier]

Abstract: Objective: The study presents a multi-channel patient-independent neonatal seizure detection system based on the Support Vector Machine (SVM) classifier. Methods: A machine learning algorithm (SVM) is used as a classifier to discriminate between seizure and non-seizure EEG epochs. Two post-processing steps are proposed to increase both the temporal precision and the robustness of the system. The resulting system is validated on a large clinical dataset of 267h of EEG data from 17 full-term newborns with seizures. Results: The performance of the system using event-based metrics is reported. The system showed the best up-to-date performance of a neonatal seizure detection system. The system was able to achieve an average good detection rate of ∼89% with one false seizure detection per hour, ∼96% with two false detections per hour, or ∼100% with four false detections per hour. An analysis of errors revealed sources of misclassification in terms of both missed seizures and false detections. Conclusions: The results obtained with the proposed SVM-based seizure detection system allow for its practical application in neonatal intensive care units. Significance: The proposed SVM-based seizure detection system can greatly assist clinical staff, in a neonatal intensive care unit, to interpret the EEG. The system allows control of the final decision by choosing different confidence levels which makes it flexible for clinical needs. The obtained results may provide a reference for future seizure detection systems. [ABSTRACT FROM AUTHOR]

Abstract: Background and objective: In clinical practice, the association between sleep and epilepsy has been increasingly observed in adults and during childhood. The aim of this article is to verify the relationship between sleep and epilepsy in the neonatal period in order to identify evidence and mechanisms to explain how epilepsy or neonatal seizures might disrupt sleep and how sleep might influence seizure occurrence and epilepsy during this age span. Methods: Literature review with search of PubMed database using the key words neonatal seizures and sleep. Results and conclusion: The complex processes of cortical maturation are closely related to the regulation of sleep–wakefulness cycles. Sleep regulation in the context of neonatal seizures is frequently abnormal, and these alterations may be a result of the maladaptative plasticity of neuronal networks. Furthermore, in this situation altered connectivity might also be associated with other expressions of neurological dysfunction such as cognitive and behavioral problems. EEG background abnormalities and higher frequency of discharges are often associated with disrupted sleep organization. The outcome of newborns with seizures where sleep organization is undifferentiated seems to be more unfavorable. [Copyright &y& Elsevier]

Abstract: Objective: To describe the characteristics of activity bursts in the early preterm EEG, to assess inter-rater agreement of burst detection by visual inspection, and to determine the performance of an automated burst detector that uses non-linear energy operator (NLEO). Methods: EEG recordings from extremely preterm (n =12) and very preterm (n =6) infants were analysed. Three neurophysiologists independently marked bursts in the EEG, the characteristics of bursts were analyzed and inter-rater agreement determined. Unanimous detections were used as the gold standard in estimating the performance of an automated burst detector. In addition, some details of this automated detector were revised in an attempt to improve performance. Results: Overall, inter-rater agreement was 86% for extremely preterm infants and 81% for very preterm infants. In visual markings, bursts had variable lengths (∼1–10s) and increased amplitudes (and power) throughout the frequency spectrum. Accuracy of the original detection algorithm was 87% and 79% and accuracy of the revised algorithm 93% and 87% for extremely preterm and very preterm babies, respectively. Conclusion: Visual detection of bursts from the early preterm EEG is comparable albeit not identical between raters. The original automated detector underestimates the amount of burst occurrence, but can be readily improved to yield results comparable to visual detection. Further clinical studies are warranted to assess the optimal descriptors of burst detection for monitoring and prognostication. Significance: Validation of a burst detector offers an evidence-based platform for further development of brain monitors in very preterm babies. [Copyright &y& Elsevier]

Abstract: We compared frequency and power of neonatal EEG delta brush rapid oscillatory activity (ROA) using multiple band frequency analysis (MBFA) in three groups; pre-term (PT, post-conceptional age 33–35.6weeks, n =5); full-term (FT, 39.4–40.6weeks, n =5) and pre-term or full-term with phenobarbital exposure (PB, n =5). Mean number of delta brushes analyzed was 29.4 (range 26–47) in PT, 20.8 (14–33) in FT and 20 (7–37) in PB. Mean frequency±standard deviation (s.d.) was 16.9±2.1Hz (range 15–20Hz) in PT, 17.3±1.9Hz (15–20Hz) in FT and 16.1±1.6Hz (14–19Hz) in PB. Mean power±s.d. was 22.9±6.2μV2 (range 16–39μV2) in PT, 11.9±4.1μV2 (7–19μV2) in FT and 17.1±6.2μV2 (9–26μV2) in PB. Power was significantly higher in PT than FT (p <0.005). Power after merging PB into respective PT (PT′, n =8) and FT (FT′, n =7) groups, remained significantly higher in PT′ (mean±s.d. 21.8±7.4μV2) than FT′ (11.4±3.6μV2) (p <0.05). We characterise ROA in delta brushes in maturing neonates using MBFA, which may provide additional information for assessing future seizure recurrence and epilepsy risk. [Copyright &y& Elsevier]

Abstract: Objective: To compare the seizure pattern detection rate of single-channel and multichannel amplitude-integrated EEG (aEEG), using conventional EEG (cEEG) as a gold standard, in full-term neonates with hypoxic–ischemic encephalopathy. The optimal electrode derivation for seizure detection with single-channel aEEG was also investigated. Methods: Twelve infants with cEEG seizure patterns (⩾10s) were investigated. cEEG signals were transformed into aEEG signals. Seizure patterns and the number of patients identified with ⩾1 seizure patterns were calculated for single- and multichannel aEEG. Results: On cEEG, 121 seizure patterns with a mean duration of 58s were identified, 68% of which occurred over the centrotemporal region. The sensitivity of aEEG for the detection of seizure patterns was 30% (C.I.: 0.22–0.38) for single-channel aEEG and 39% (C.I.: 0.31–0.48) for multichannel aEEG. Multichannel aEEG identified all patients with ⩾1 seizure pattern (C.I.: 0.75–1.00), whereas single-channel aEEG (with C4–C3 as the optimal electrode derivation) identified all but one of the patients (C.I.: 0.66–0.99). Conclusions: Seizure pattern detection rate is slightly better with multichannel aEEG compared with single-channel (C4–C3) aEEG. Multichannel aEEG identified correctly all patients with ⩾1 seizure pattern in this small selection of patients. Significance: Single-channel aEEG may detect most patients (in a selected group) with severe neonatal seizures patterns; patient identification can be improved using multichannel recordings. [Copyright &y& Elsevier]

Abstract: Objective: To characterise and quantify the EEG during sleep in healthy newborns in the early newborn period. Methods: Continuous multi-channel video-EEG data was recorded for up to 2hours in normal newborns within 12hours of birth. The total amount of active (AS) and quiet sleep (QS) was calculated in the first hour of recording. The EEG signal was quantitatively analysed for symmetry and synchrony. Spectral edge frequency (SEF), spectral entropy (H) and relative delta power (δ R) were calculated for a ten-minute segment of AS and QS in each recording. Paired t-test and Wilcoxon rank sum test were used for data analysis. Results: Thirty normal newborn babies were studied, 10 within 6hours of birth and 20 between 6 and 12hours. All babies showed continuous symmetrical and synchronous EEG activity and well-developed sleep–wake cycling (SWC) with the median percentage of AS – 48.5% and QS – 36.6%. Quantitative EEG analysis of sleep epochs showed that SEF and H were significantly higher (p <0.0001) and δ R was significantly lower (p <0.0001) in AS than in QS. Conclusion: The normal newborn EEG shows symmetrical and synchronous continuous activity and well-developed SWC as early as within the first 6hours of birth. Quantitative analysis of the EEG in the early postnatal period reveals differences in SEF, H and δ R for AS and QS periods. Significance: These findings may have implications for quantitative analysis of the newborn EEG, including the EEG of babies with hypoxic ischaemic encephalopathy. [Copyright &y& Elsevier]

Summary: Purpose: Hemimegalencephaly is an epileptic encephalopathy which presents during the neonatal period. Our aims are to analyze interictal fast oscillations and EEG patterns in neonates and infants with hemimegalencephaly. Methods: We collected scalp EEGs and applied multiple band frequency analysis (MBFA) to analyze frequency and power of interictal fast oscillations (FOs). Results: We studied 18 scalp EEGs in 7 patients with catastrophic epilepsy secondary to hemimegalencephaly, between 3 days and 24 months of age. Maximum frequency of FOs (22–57Hz; mean, 42Hz) on the hemimegalencephalic side was significantly higher than those (8–27Hz; mean, 18Hz) in the unaffected side (p <0.05). Differences in maximum FOs remained within 1–8Hz (mean, 3Hz) across consecutive EEGs. We found four EEG patterns: (1) suppression burst pattern (7 EEGs, 6 patients), (2) continuous triphasic complex pattern (5 EEGs, 3 patients), (3) continuous high amplitude slow waves with spikes (3 EEGs, 2 patients) and (4) frequent spike and slow waves (3 EEGs, 2 patients). Five patients with multiple EEG recordings showed changing EEG patterns. Conclusion: We confirmed the interictal epileptogenic FOs in neonatal EEGs of patients with hemimegalencephaly. The frequency of epileptogenic FOs remains stable from the neonates through increasing age while the patterns of EEG changed during brain maturation. [Copyright &y& Elsevier]

Abstract: Objective: To perform a detailed, contemporary temporal–spatial characterization of neonatal seizures (NS) and to compare conventional EEG (CEEG) to single-channel EEG for NS detection. Methods: Digitally recorded CEEGs were reviewed for NS characteristics (quantity, duration, location of onset, peak-to-peak amplitude). The presence and characteristics of each NS were simultaneously noted in a single, derived EEG channel (C3 →C4). Results: Eight hundred fifty-one seizures from 125 CEEGs recorded were analyzed. Mean seizure rate was 7.0NS/h (range: 0.5–21). Mean seizure burden (percent time CEEG showed NS at any location) was 24.8% (range: 0.7–86.9). Seizure rate was only moderately correlated with seizure burden (Spearman coefficient=0.58). Eighty-one percent of NS originated from central–temporal or midline vertex electrodes. Seventy-eight percent of NS appeared in the C3 →C4 channel. Conclusions: Accurate measurement of NS burden requires detailed temporal–spatial characterization. The theoretical ceiling of sensitivity for NS detection in the single EEG channel C3 →C4 is high. However, further processing the raw EEG in limited electrode arrays may reduce the sensitivity of NS detection. Significance: CEEG is the gold standard for NS detection. However, reduced montage EEG techniques are increasingly available. This detailed contemporary temporal–spatial characterization of NS evaluates the potential limitations of reduced montage techniques. [Copyright &y& Elsevier]

Abstract: Objective: To test the discriminatory topographic potential of a new method of the automatic EEG analysis in neonates. A quantitative description of the neonatal EEG can contribute to the objective assessment of the functional state of the brain, and may improve the precision of diagnosing cerebral dysfunctions manifested by ‘disorganization’, ‘dysrhythmia’ or ‘dysmaturity’. Methods: 21 healthy, full-term newborns were examined polygraphically during sleep (EEG-8 referential derivations, respiration, ECG, EOG, EMG). From each EEG record, two 5-min samples (one from the middle of quiet sleep, the other from the middle of active sleep) were subject to subsequent automatic analysis and were described by 13 variables: spectral features and features describing shape and variability of the signal. The data from individual infants were averaged and the number of variables was reduced by factor analysis. Results: All factors identified by factor analysis were statistically significantly influenced by the location of derivation. A large number of statistically significant differences were also established when comparing the effects of individual derivations on each of the 13 measured variables. Both spectral features and features describing shape and variability of the signal are largely accountable for the topographic differentiation of the neonatal EEG. Conclusions: The presented method of the automatic EEG analysis is capable to assess the topographic characteristics of the neonatal EEG, and it is adequately sensitive and describes the neonatal electroencephalogram with sufficient precision. Significance: The discriminatory capability of the used method represents a promise for their application in the clinical practice. [Copyright &y& Elsevier]

Summary: Purpose:: To assess the advantages of the 2001 ILAE proposed diagnostic scheme for classification of newborns with epileptic seizures over the 1989 ILAE classification. Method:: Clinical features, seizure semiology and duration, neurological evaluation, ictal and interictal EEG, brain imaging and outcome at the end of the neonatal period (44 weeks post-conceptional age) of 94 newborns with EEG confirmed seizures were analyzed. The 2001 ILAE classification was applied where possible. Results:: Twenty patients died before the end of the neonatal period. In the remaining patients, applying axis 3, we classified 54 newborns as having epileptic seizures that do not require a diagnosis of epilepsy; 14 as symptomatic partial epilepsy; 5 within the neonatal epileptic syndromes and one as benign neonatal seizures (NS). Axis 4 in all newborns provided a valuable tool in order to better define the individual patient. Conclusions:: Compared to the 1989 ILAE classification, which allowed a syndromic diagnosis in only 6/94 patients, the remaining being classified as Epilepsies and Syndromes undetermined whether focal or generalized, the 2001 ILAE diagnostic scheme, applied at the end of the neonatal period, offers a variety of approaches to classification, allowing an early distinction between epilepsy and single or isolated clusters of seizures, with therapeutic and prognostic implications. [Copyright &y& Elsevier]

Abstract: While enormous resources have been recently invested into the development of a variety of neuroimaging techniques, the bandwidth of the clinical EEG, originally set by trivial technical limitations, has remained practically unaltered for over 50 years. An increasing amount of evidence shows that salient EEG signals are observed beyond the bandwidth of the routine clinical EEG, which is typically around 0.5–50Hz. Physiological and pathological EEG activity ranges at least from 0.01Hz to several hundred Hz, as demonstrated in recordings of spontaneous activity in the immature human brain, as well as during epileptic seizures, or various kinds of cognitive tasks and states in the adult brain. In the present paper, we will review several arguments leading to the conclusion that elimination of the lower (infraslow) or higher (ultrafast) bands of the EEG frequency spectrum in routine EEG leads to situations where salient and physiologically meaningful features of brain activity are ignored. Recording the full, physiologically relevant range of frequencies is readily attained with commercially available direct-current (DC) coupled amplifiers, which have a wide dynamic range and a high sampling rate. Such amplifiers, combined with appropriate DC-stable electrode–skin interface, provide a genuine full-band EEG (FbEEG). FbEEG is mandatory for a faithful, non-distorted and non-attenuated recording, and it does not have trade-offs that would favor any frequency band at the expense of another. With the currently available electrode, amplifier and data acquisition technology, FbEEG is likely to become the standard approach for a wide range of applications in both basic science and in the clinic. [Copyright &y& Elsevier]

Objective: Clinical significance of neonatal positive temporal sharp waves (PTS) is controversial. The aim of this work is to study (1) PTS incidence in preterm infants with or without major ultrasound lesion (MUL) per gestational age (GA), and (2) the relationship between PTS in both sleep states and other electroencephalographic (EEG) findings with poor prognoses.Methods: 97 preterm infants of <27–36 weeks GA, and 12 full-term healthy infants were presented. Prospective study included (1) neurodevelopmental assessment at 40–42 weeks conceptional age (CA), (2) serial neurosonography, and (3) EEG recording at postnatal week 1, 2, 4 and at 40–42 weeks CA.Results: In 50 neonates without MUL, peak PTS was at 31–32 weeks GA. In 47 neonates with MUL, PTS increased significantly from week 2 after birth, descending at the 4th. Neonates of <33 weeks GA with MUL showed significantly increased PTS at term. A significant relationship was found between PTS and other EEG abnormalities with poor neurologic prognoses. PTS incidence varied with sleep states, being predominant in indeterminate sleep in neonates with MUL.Conclusions: PTS increased significantly in infants with MUL, mainly at week 2 of postnatal life, persisting high until term CA, and correlated with other abnormal EEG findings.Significance: PTS are highly sensitive to MUL. [Copyright &y& Elsevier]

Objective: The time courses of quadratic phase-coupling (QPC) of electroencephalographic burst and interburst patterns of the ‘trace alternant’ (TA) in full-term newborns have been quantified.Methods: Using the Gabor expansion, a fast Fourier transformation based method, biamplitude, bicoherence and phase-bicoherence time courses of both burst and interburst patterns have been determined (common average reference EEG recordings). With a frequency resolution of 0.25 Hz and a frequency grid of 1–1.5⇔3.5–4.5 Hz (region-of-interest), a number of 15 frequency pairs result. These pairs have been investigated.Results: The burst and the interburst patterns are characterized by temporally and topographically different QPC profiles. All differences are dominant at the electrode Fp1 followed by Fp2. There is a significant difference (combined multiple and global test strategy) in the QPC characteristics between both patterns within the time period from 0.75 to 1.5 s after the pattern onset at electrode Fp1. The maximal QPC in burst patterns (especially at Fp1) can be observed during this time period. In contrast to this finding, maximal QPC in interburst patterns (at Fp1) are reached immediately after the onset and at 3 s. Summarising all findings, a QPC-rhythm of 0.1 Hz during TA can be assumed.Conclusions: It can be assumed that the QPC rhythm of the TA is generated by a pattern-spanning time-variant phase-locking process and there are indications for a possible correspondence between the QPC rhythm and vegetative rhythms.Significance: This study showed that advanced, time-variant analysis methods quantifying QPC rhythms are able to add new scientific information to the understanding of nature, characteristics and significance of TA in the neonatal EEG. [Copyright &y& Elsevier]

Objective: Periodic breathing is a respiratory pattern typical of preterm infants, but its clinical significance has not been clarified yet. The present study was designed to investigate whether the presence of periodic breathing is specifically associated to low post-conceptional ages, preterm birth, or common clinical disorders related to preterm birth.Methods: The study included 271 consecutive infants submitted to neonatal polysomnography, of whom 138 were born before 37 complete gestational weeks (preterm) and 133 were full-term. The main outcome measure was periodic breathing. A multivariate analysis was performed to test the specific impact of preterm birth, respiratory distress syndrome and hypoxic-ischemic encephalopathy on the occurrence of periodic breathing, with adjustment for potential confounding factors such as the post-conceptional age by the time of the polysomnography.Results: Periodic breathing was twice more frequent in infants born before term (83/138 [60%]) than in full-term babies (41/133 [31%], χ2=22.3, P<0.0001). The presence of periodic breathing was not significantly associated to either hypoxic-ischemic encephalopathy or respiratory distress syndrome. After a multivariate analysis, only preterm birth remained in the regression model as a specific risk factor for periodic breathing (adjusted odds ratio=5.62, P<0.0001).Conclusions: Periodic breathing is a respiratory pattern independently associated with preterm birth, and this association cannot be imputed to the coexistence of either respiratory distress syndrome or hypoxic-ischemic encephalopathy among preterm infants.Significance: The finding of periodic breathing in neonatal polysomnography correlates with preterm birth but not with its associated clinical disorders. [Copyright &y& Elsevier]

Objective: To investigate the effects of perinatal dioxin exposure indicated by dioxins in breast milk on neonatal electroencephalography (EEG) power in the quiet sleep stage, and associations with neurodevelopmental outcomes at 2 years of age.Study Design: Fifty-one mother-newborn pairs were enrolled for neonatal EEG analysis in the quiet sleep stage from a birth cohort recruited at a prefecture hospital in Bien Hoa city, Vietnam. Relative EEG power in intra-burst-intervals and high-voltage-bursts in the trace alternant pattern were computed from EEG data during the quiet sleep stage. Forty-three mother-child pairs participated in a 2-year follow-up survey to examine neurodevelopment using the Bayley-III scale and gaze behavior exhibited by fixation duration on the face of a child talking in videos. The general linear model and regression linear model were used for data analysis after adjusting for confounding factors.Results: Perinatal dioxin exposure, particularly 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) exposure, influenced relative EEG power values mainly in the intra-burst-interval part of the trace alternant pattern in the quiet sleep stage. In intra-burst-intervals, decreased frontal delta power and increased frontal and parietal alpha power values in the left hemisphere and temporal beta power values in the right hemisphere were associated with increased TCDD exposure, with significant dose-response relationships. Almost none of the relative power values in these brain regions were associated with Bayley III scores, but relative delta power values were significantly associated with face fixation duration in left frontal and parietal regions at 2 years of age.Conclusion: Perinatal dioxin exposure influences neuronal activity in the quiet sleep stage, leading to poor communication ability indicated by gaze behavior in early childhood. [ABSTRACT FROM AUTHOR]

• A high-performance, ultra low-noise and very high-precision neonatal EEG recording instrument. • Novel tunable filter single-OPAMP, single-multiplexer topology. • A portable, wearable but also modular design, allowing for multi-purpose use (neonatal and adult). This paper describes the design considerations and performance evaluation of a novel high-performance instrumentation, termed the neoEEG Board, able to record neonatal EEG signals with high precision. This prototype EEG recording modality is proposed as an alternative to the currently utilised in NICU state-of-the-art device, the CFM™. Among other characteristics, the neoEEG Board is modular, portable, wearable, wireless and battery operated. It offers 8 channels per module, each providing independent spectral analysis from 0.5 to 100 Hz, with a novel topology for variable bandwidth. The proposed instrument's noise figure has been measured and found to be equal to 3 nV / Hz , allowing it to resolve very weak signals down to 6 nV p - p . In addition it provides high CMRR (>130 dB), wide gain range (74 dB to 95.5 dB), DC-offset tolerance up to ±35 mV , sampling rate per channel up to 32 kHz and supports wireless data transmission up to 2 Mbits / s. [ABSTRACT FROM AUTHOR]

Summary Objectives The selection of quantitative EEG markers according to their ability to grade the severity of cerebral disability after perinatal hypoxia and to predict long term prognosis. These markers will be associated in an automated analysis tool that can be used at the patient's bedside to facilitate visual analysis. Methods Full term neonatal EEG were recorded before the 6th hour of life after perinatal hypoxia. They were visually analysed and graded from 0 to 3 (French classification) by two neurophysiologists blinded to clinical data. Several quantitative EEG markers were calculated on different derivations: the minimal and maximal Amplitude Index (min and max AI), the Burst Suppression Rate (BSR), the Spectral Absolute Power and Relative Delta Power (AP and RDP), the Spectral Edge Frequency (SEF). Results Ninety EEG were selected between 2013 and 2017 for the quantitative analysis. The mean BSR on C3C4 discriminated the grades 3 from grades 1 and 2 (Youden index: 0.88) and the median min AI on C3T3 discriminated the grades 1 from grades 2 (Youden index 0.6). The mean BSR on C3C4 discriminated good outcome from poor outcome at 1 year (Air Under the Curve: AUC 0.89) and 2 years (AUC 0.87). Conclusion After perinatal hypoxia, quantitative EEG markers based on the analysis of the amplitude and the continuity of the signal are associated with the visual EEG grades of severity and with the evolution at 1 and 2 years. They can be used to develop an automated classifier to facilitate the visual interpretation, especially to evaluate the indication of therapeutic hypothermia (grades 2 and 3) and long-term prognosis. [ABSTRACT FROM AUTHOR]

Background: The severity of the initial encephalopathy in neonatal hypoxic-ischemic encephalopathy correlates with seizure burden. Early electroencephalograph (EEG) background activity reflects the severity of encephalopathy. Thus, we hypothesized that early EEG background would be predictive of subsequent seizures in neonatal hypoxic-ischemic encephalopathy.Methods: This study included infants undergoing therapeutic hypothermia at St. Louis Children's Hospital between January 2009 and April 2013. Two pediatric epilepsy specialists independently characterized EEG background qualitatively using amplitude-integrated EEG trends. Total EEG power in the 1-20 Hz frequency band was calculated for quantitative EEG background assessment. Seizures were identified on conventional full montage EEG. Statistical analysis was performed using logistic regression.Results: Seventy-eight of the 93 eligible infants had artifact-free EEG data; 23 of 78 infants (29%) developed seizures, and of these, 11 developed status epilepticus. The best predictors of subsequent seizures during the first hour of EEG recording were a flat tracing pattern on amplitude-integrated EEG (sensitivity 26%, specificity 98%, likelihood ratio 13, positive predictive value 85%) and the total EEG power less than 10 μV2 (sensitivity 52%, specificity 98%, likelihood ratio 30, positive predictive value 92%).Conclusions: Early EEG biomarkers predict subsequent seizures in infants with hypoxic-ischemic encephalopathy. Compared with the qualitative amplitude-integrated EEG background, total EEG power improves our ability to identify high-risk infants from the first hour of EEG recording. Infants with a total EEG power of less than 10 μV2 have a 90% risk of subsequent seizures. Quantitative EEG measures could stratify cohorts while evaluating novel neuroprotective strategies in neonatal hypoxic-ischemic encephalopathy. [ABSTRACT FROM AUTHOR]