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.

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.

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.

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.

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.

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.

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.

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.

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.

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.