Prediction of Survival After Pediatric Cardiac Arrest Using Quantitative EEG and Machine Learning Techniques.

Background And Objectives: Early neuroprognostication in children with reduced consciousness after cardiac arrest (CA) is a major clinical challenge. EEG is frequently used for neuroprognostication in adults, but has not been sufficiently validated for this indication in children. Using machine learning techniques, we studied the predictive value of quantitative EEG (qEEG) features for survival 12 months after CA, based on EEG recordings obtained 24 hours after CA in children.

Depression of cortical neuronal activity after a low-dose fentanyl in preterm infants.

Aim: Opioids might be harmful to the developing brain and dosing accuracy is important. We aimed at investigating fentanyl effects on cortical activity in infants using computational re-analysis of bedside recorded EEG signals.

Methods: Fifteen infants born at median 26.

Early automated classification of neonatal hypoxic-ischemic encephalopathy - An aid to the decision to use therapeutic hypothermia.

Objective: 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.

Methods: 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.

A machine learning artefact detection method for single-channel infant event-related potential studies.

. Automated detection of artefact in stimulus-evoked electroencephalographic (EEG) data recorded in neonates will improve the reproducibility and speed of analysis in clinical research compared with manual identification of artefact. Some studies use very short, single-channel epochs of EEG data with little recorded EEG per infant-for example because the clinical vulnerability of the infants limits access for recording.

Sensory event-related potential morphology predicts age in premature infants.

Objective: We investigated whether sensory-evoked cortical potentials could be used to estimate the age of an infant. Such a model could be used to identify infants who deviate from normal neurodevelopment.

Methods: Infants aged between 28- and 40-weeks post-menstrual age (PMA) (166 recording sessions in 96 infants) received trains of visual and tactile stimuli.

A Bedside Method for Measuring Effects of a Sedative Drug on Cerebral Function in Newborn Infants.

Background: Data on the cerebral effects of analgesic and sedative drugs are needed for the development of safe and effective treatments during neonatal intensive care. Electroencephalography (EEG) is an objective, but interpreter-dependent method for monitoring cortical activity. Quantitative computerized analyses might reveal EEG changes otherwise not detectable.

Early predictors of neurodevelopment after perinatal arterial ischemic stroke: a systematic review and meta-analysis.

Background And Aims: Perinatal arterial ischemic stroke (PAIS) often has lifelong neurodevelopmental consequences. We aimed to review early predictors (<4 months of age) of long-term outcome.

Methods: We carried out a systematic literature search (PubMed and Embase), and included articles describing term-born infants with PAIS that underwent a diagnostic procedure within four months of age, and had any reported outcome parameter ≥12 months of age.

Effects of Daily Peat Smoke Exposure on Present and Next Generations.

This study aimed to follow the neurotoxic effect of peat smoke on adult outbred rats and its influence on central nervous system (CNS) parameters in first-generation offspring. Under experimental conditions, exposure to peat smoke was carried out on adult male Wistar rats for 24 h. After the end of the exposure, an open field test (OFT), electroencephalography (EEG), and histological analysis of the testes and brains of smoke-exposed males were performed, after which they were mated with intact females to obtain F1 offspring.

Multiscale entropy as a metric of brain maturation in a large cohort of typically developing children born preterm using longitudinal high-density EEG in the first two years of life.

We aimed to analyze whether complexity of brain electrical activity (EEG) measured by multiscale entropy (MSE) increases with brain maturation during the first two years of life. We also aimed to investigate whether this complexity shows regional differences across the brain, and whether changes in complexity are influenced by extrauterine life experience duration.We measured MSE of EEG signals recorded longitudinally using a high-density setup (64 or 128 electrodes) in 84 typically developing infants born preterm (<32 weeks' gestation) from term age to two years.

Automated detection and removal of flat line segments and large amplitude fluctuations in neonatal electroencephalography.

Background: Artefact removal in neonatal electroencephalography (EEG) by visual inspection generally depends on the expertise of the operator, is time consuming and is not a consistent pre-processing step to the pipeline for the automated EEG analysis. Therefore, there is the need for the automated detection and removal of artefacts in neonatal EEG, especially of distinct and predominant artefacts such as flat line segments (mainly caused by instrumental error where contact between electrodes and head box is lost) and large amplitude fluctuations (related to neonatal movements).

Method: A threshold-based algorithm for the automated detection and removal of flat line segments and large amplitude fluctuations in neonatal EEG of infants at term-equivalent age is developed.

A method for AI assisted human interpretation of neonatal EEG.

The study proposes a novel method to empower healthcare professionals to interact and leverage AI decision support in an intuitive manner using auditory senses. The method's suitability is assessed through acoustic detection of the presence of neonatal seizures in electroencephalography (EEG). Neurophysiologists use EEG recordings to identify seizures visually.

The impact of premature extrauterine exposure on infants' stimulus-evoked brain activity across multiple sensory systems.

Prematurity can result in widespread neurodevelopmental impairment, with the impact of premature extrauterine exposure on brain function detectable in infancy. A range of neurodynamic and haemodynamic functional brain measures have previously been employed to study the neurodevelopmental impact of prematurity, with methodological and analytical heterogeneity across studies obscuring how multiple sensory systems are affected. Here, we outline a standardised template analysis approach to measure evoked response magnitudes for visual, tactile, and noxious stimulation in individual infants (n = 15) using EEG.

An EEG analysis framework through AI and sonification on low power IoT edge devices.

This study explores the feasibility of implementation of an analysis framework of neonatal EEG, including ML, sonification and intuitive visualization, on a low power IoT edge device. Electroencephalography (EEG) analysis is a very important tool to detect brain disorders. Neonatal seizure detection is a known, challenging problem.

Case Report: High-Gamma Oscillations on an Ictal Electroencephalogram in a Newborn Patient With Hypoxic-Ischemic Encephalopathy.

Fast oscillations (FOs) >40 Hz in electroencephalograms (EEGs) are associated with ictogenesis and epileptogenesis in adults and children with epilepsy. However, only a few previous studies showed FOs in neonates. Reported frequencies of such neonatal FOs were in the low-gamma (<60 Hz) band and, therefore, they were not high compared to those in pediatric patients.

A Rare Cause of Epilepsy: Ulegyria Revisited in a Series of 10 Patients.

Ulegyria results from perinatal hypoxic-ischemic brain injury in term infants. The specific mushroom-shaped configuration of ulegyria results from small atrophic circumvolutions at the bottom of a sulcus underlying an intact gyral apex. Clinically, ulegyria is generally associated with epilepsy.

The Kynurenic Acid Analog SZR72 Enhances Neuronal Activity after Asphyxia but Is Not Neuroprotective in a Translational Model of Neonatal Hypoxic Ischemic Encephalopathy.

Hypoxic-ischemic encephalopathy (HIE) remains to be a major cause of long-term neurodevelopmental deficits in term neonates. Hypothermia offers partial neuroprotection warranting research for additional therapies. Kynurenic acid (KYNA), an endogenous product of tryptophan metabolism, was previously shown to be beneficial in rat HIE models.

Pilot study of a single-channel EEG seizure detection algorithm using machine learning.

Objective: Seizures are one of the most common emergencies in the neonatal intensive care unit (NICU). They are identified through visual inspection of electroencephalography (EEG) reports and treated by neurophysiologic experts. To support clinical seizure detection, several feature-based automatic neonatal seizure detection algorithms have been proposed.

Behavioral-state development and sleep-state differentiation during early ontogenesis.

Sleep is a key process in neurodevelopment and essential for the maturation of fundamental brain functions. Premature birth can disturb the initial steps of sleep maturation, which may contribute to the impairment of neurodevelopment. It is thus fundamental to understand the maturation of the various sleep states and the quality of cerebral function in each vigilance state, as well as the development of sleep cyclicity, in at-risk neonatal infants, particularly those born premature.

Inhaled H or CO Do Not Augment the Neuroprotective Effect of Therapeutic Hypothermia in a Severe Neonatal Hypoxic-Ischemic Encephalopathy Piglet Model.

Hypoxic-ischemic encephalopathy (HIE) is still a major cause of neonatal death and disability as therapeutic hypothermia (TH) alone cannot afford sufficient neuroprotection. The present study investigated whether ventilation with molecular hydrogen (2.1% H) or graded restoration of normocapnia with CO for 4 h after asphyxia would augment the neuroprotective effect of TH in a subacute (48 h) HIE piglet model.

Reliability and accuracy of EEG interpretation for estimating age in preterm infants.

Objectives: To determine the accuracy of, and agreement among, EEG and aEEG readers' estimation of maturity and a novel computational measure of functional brain age (FBA) in preterm infants.

Methods: Seven experts estimated the postmenstrual ages (PMA) in a cohort of recordings from preterm infants using cloud-based review software. The FBA was calculated using a machine learning-based algorithm.

Machine-learning-based diagnostics of EEG pathology.

Machine learning (ML) methods have the potential to automate clinical EEG analysis. They can be categorized into feature-based (with handcrafted features), and end-to-end approaches (with learned features). Previous studies on EEG pathology decoding have typically analyzed a limited number of features, decoders, or both.

Predictive Value of Early Amplitude Integrated EEG in Extremely Premature Infants.

Amplitude integrated EEG (aEEG) is increasingly utilized in preterm infants. The aim of the study was to evaluate whether semiquantitative visual assessment of aEEG background during the first 72 hours of life is associated with long-term outcome in a group of premature infants born less than 28 weeks' gestation. Infants were prospectively enrolled and monitored in the first 72 hours after birth.