Measuring Cot-Side the Effects of Parenteral Nutrition on Preterm Cortical Function.

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

Cortical responses to tactile stimuli in preterm infants.

The conventional assessment of preterm somatosensory functions using averaged cortical responses to electrical stimulation ignores the characteristic components of preterm somatosensory evoked responses (SERs). Our study aimed to systematically evaluate the occurrence and development of SERs after tactile stimulus in preterm infants. We analysed SERs performed during 45 electroencephalograms (EEGs) from 29 infants at the mean post-menstrual age of 30.7 weeks. Altogether 2,087 SERs were identified visually at single-trial level from unfiltered signals capturing also their slowest components. We observed salient SERs with a high-amplitude slow component at a high success rate after hand (95%) and foot (83%) stimuli. There was a clear developmental change in both the slow wave and the higher-frequency components of the SERs. Infants with intraventricular haemorrhage (IVH; eleven infants) had initially normal SERs, but those with bilateral IVH later showed a developmental decrease in the ipsilateral SER occurrence after 30 weeks of post-menstrual age. Our study shows that tactile stimulus applied at bedside elicits salient SERs with a large slow component and an overriding fast oscillation, which are specific to the preterm period. Prior experimental research indicates that such SERs allow studying both subplate and cortical functions. Our present findings further suggest that they might offer a window to the emergence of neurodevelopmental sequelae after major structural brain lesions and, hence, an additional tool for both research and clinical neurophysiological evaluation of infants before term age.

Automated classification of neonatal sleep states using EEG.

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 45weeks 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.

Improving Reliability of Monitoring Background EEG Dynamics in Asphyxiated Infants.

The goal of this study is to develop an automated algorithm to quantify background electroencephalography (EEG) dynamics in term neonates with hypoxic ischemic encephalopathy. The recorded EEG signal is adaptively segmented and the segments with low amplitudes are detected. Next, depending on the spatial distribution of the low-amplitude segments, the first part of the algorithm detects (dynamic) interburst intervals (dIBIs) and performs well on the relatively artifact-free EEG periods and well-defined burst-suppression EEG periods. However, on testing the algorithm on EEG recordings of more than 48 h per neonate, a significant number of misclassified and dubious detections were encountered. Therefore, as the next step, we applied machine learning classifiers to differentiate between definite dIBI detections and misclassified ones. The developed algorithm achieved a true positive detection rate of 98%, 97%, 88%, and 95% for four duration-related dIBI groups that we subsequently defined. We benchmarked our algorithm with an expert diagnostic interpretation of EEG periods (1 h long) and demonstrated its effectiveness in clinical practice. We show that the detection algorithm effectively discriminates challenging cases encountered within mild and moderate background abnormalities. The dIBI detection algorithm improves identification of neonates with good clinical outcome as compared to the classification based on the classical burst-suppression interburst interval.

Objective differentiation of neonatal EEG background grades using detrended fluctuation analysis.

A quantitative and objective assessment of background electroencephalograph (EEG) in sick neonates remains an everyday clinical challenge. We studied whether long range temporal correlations quantified by detrended fluctuation analysis (DFA) could be used in the neonatal EEG to distinguish different grades of abnormality in the background EEG activity. Long-term EEG records of 34 neonates were collected after perinatal asphyxia, and their background was scored in 1 h epochs (8 h in each neonate) as mild, moderate or severe. We applied DFA on 15 min long, non-overlapping EEG epochs (n = 1088) filtered from 3 to 8 Hz. Our formal feasibility study suggested that DFA exponent can be reliably assessed in only part of the EEG epochs, and in only relatively short time scales (10-60 s), while it becomes ambiguous if longer time scales are considered. This prompted further exploration whether paradigm used for quantifying multifractal DFA (MF-DFA) could be applied in a more efficient way, and whether metrics from MF-DFA paradigm could yield useful benchmark with existing clinical EEG gradings. Comparison of MF-DFA metrics showed a significant difference between three visually assessed background EEG grades. MF-DFA parameters were also significantly correlated to interburst intervals quantified with our previously developed automated detector. Finally, we piloted a monitoring application of MF-DFA metrics and showed their evolution during patient recovery from asphyxia. Our exploratory study showed that neonatal EEG can be quantified using multifractal metrics, which might offer a suitable parameter to quantify the grade of EEG background, or to monitor changes in brain state that take place during long-term brain monitoring.

Data-driven metric representing the maturation of preterm EEG.

Essential information about early brain maturation can be retrieved from the preterm human electroencephalogram (EEG). This study proposes a new set of quantitative features that correlate with early maturation. We exploit the known early trend in EEG content from intermittent to continuous activity, which changes the line length content of the EEG. The developmental shift can be captured in the line length histogram, which we use to obtain 28 features; 20 histogram bins and 8 other statistical measurements. Using the mutual information, we select 6 features with high correlation to the infant's age. This subset appears promising to detect deviances from normal brain maturation. The presented data-driven index holds promise for developing into a computational EEG index of maturation that is highly needed for overall assessment in the Neonatal Intensive Care Units.

Holistic approach for automated background EEG assessment in asphyxiated full-term infants.

OBJECTIVE: To develop an automated algorithm to quantify background EEG abnormalities in full-term neonates with hypoxic ischemic encephalopathy. APPROACH: The algorithm classifies 1 h of continuous neonatal EEG (cEEG) into a mild, moderate or severe background abnormality grade. These classes are well established in the literature and a clinical neurophysiologist labeled 272 1 h cEEG epochs selected from 34 neonates. The algorithm is based on adaptive EEG segmentation and mapping of the segments into the so-called segments' feature space. Three features are suggested and further processing is obtained using a discretized three-dimensional distribution of the segments' features represented as a 3-way data tensor. Further classification has been achieved using recently developed tensor decomposition/classification methods that reduce the size of the model and extract a significant and discriminative set of features. MAIN RESULTS: Effective parameterization of cEEG data has been achieved resulting in high classification accuracy (89%) to grade background EEG abnormalities. SIGNIFICANCE: For the first time, the algorithm for the background EEG assessment has been validated on an extensive dataset which contained major artifacts and epileptic seizures. The demonstrated high robustness, while processing real-case EEGs, suggests that the algorithm can be used as an assistive tool to monitor the severity of hypoxic insults in newborns.

Line length as a robust method to detect high-activity events: automated burst detection in premature EEG recordings.

OBJECTIVE: EEG is a valuable tool for evaluation of brain maturation in preterm babies. Preterm EEG constitutes of high voltage burst activities and more suppressed episodes, called interburst intervals (IBIs). Evolution of background characteristics provides information on brain maturation and helps in prediction of neurological outcome. The aim is to develop a method for automated burst detection. METHODS: Thirteen polysomnography recordings were used, collected at preterm postmenstrual age of 31.4 (26.1-34.4)weeks. We developed a burst detection algorithm based on the feature line length and compared it with manual scorings of clinical experts and other published methods. RESULTS: The line length-based algorithm is robust (84.27% accuracy, 84.00% sensitivity, 85.70% specificity). It is not critically dependent on the number of measurement channels, because two channels still provide 82% accuracy. Furthermore, it approximates well clinically relevant features, such as median IBI duration 5.45 (4.00-7.11)s, maximum IBI duration 14.02 (8.73-18.80)s and burst percentage 48.89 (35.45-60.12)%, with a median deviation of respectively 0.65s, 1.96s and 6.55%. CONCLUSION: Automated assessment of long-term preterm EEG is possible and its use will optimize EEG interpretation in the NICU. SIGNIFICANCE: This study takes a first step towards fully automatic analysis of the preterm brain.

Interhemispheric synchrony in the neonatal EEG revisited: activation synchrony index as a promising classifier.

A key feature of normal neonatal EEG at term age is interhemispheric synchrony (IHS), which refers to the temporal co-incidence of bursting across hemispheres during trace alternant EEG activity. The assessment of IHS in both clinical and scientific work relies on visual, qualitative EEG assessment without clearly quantifiable definitions. A quantitative measure, activation synchrony index (ASI), was recently shown to perform well as compared to visual assessments. The present study was set out to test whether IHS is stable enough for clinical use, and whether it could be an objective feature of EEG normality. We analyzed 31 neonatal EEG recordings that had been clinically classified as normal (n = 14) or abnormal (n = 17) using holistic, conventional visual criteria including amplitude, focal differences, qualitative synchrony, and focal abnormalities. We selected 20-min epochs of discontinuous background pattern. ASI values were computed separately for different channel pair combinations and window lengths to define them for the optimal ASI intraindividual stability. Finally, ROC curves were computed to find trade-offs related to compromised data lengths, a common challenge in neonatal EEG studies. Using the average of four consecutive 2.5-min epochs in the centro-occipital bipolar derivations gave ASI estimates that very accurately distinguished babies clinically classified as normal vs. abnormal. It was even possible to draw a cut-off limit (ASI~3.6) which correctly classified the EEGs in 97% of all cases. Finally, we showed that compromising the length of EEG segments from 20 to 5 min leads to increased variability in ASI-based classification. Our findings support the prior literature that IHS is an important feature of normal neonatal brain function. We show that ASI may provide diagnostic value even at individual level, which strongly supports its use in prospective clinical studies on neonatal EEG as well as in the feature set of upcoming EEG classifiers.

Automated EEG inter-burst interval detection in neonates with mild to moderate postasphyxial encephalopathy.

EEG inter-burst interval (IBI) and its evolution is a robust parameter for grading hypoxic encephalopathy and prognostication in newborns with perinatal asphyxia. We present a reliable algorithm for the automatic detection of IBIs. This automated approach is based on adaptive segmentation of EEG, classification of segments and use of temporal profiles to describe the global distribution of EEG activity. A pediatric neurologist has blindly scored data from 8 newborns with perinatal postasphyxial encephalopathy varying from mild to severe. 15 minutes of EEG have been scored per patient, thus totaling 2 hours of EEG that was used for validation. The algorithm shows good detection accuracy and provides insight into challenging cases that are difficult to detect.