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

Peer reviewe

Automated EEG background analysis to identify neonates with hypoxic-ischemic encephalopathy treated with hypothermia at risk for adverse outcome: A pilot study

Background: To improve the objective assessment of continuous video-EEG (cEEG) monitoring of neonatal brain function, the aim was to relate automated derived amplitude and duration parameters of the suppressed periods in the EEG background (dynamic Interburst Interval= dIBIs) after neonatal hypoxic-ischemic encephalopathy (HIE) to favourable or adverse neurodevelopmental outcome. Methods: Nineteen neonates (gestational age 36-41 weeks) with HIE underwent therapeutic hypothermia and had cEEG-monitoring. EEGs were retrospectively analyzed with a previously developed algorithm to detect the dynamic Interburst Intervals. Median duration and amplitude of the dIBIs were calculated at 1h-intervals. Sensitivity and specificity of automated EEG background grading for favorable and adverse outcomes were assessed at 6h-intervals. Results: Dynamic IBI values reached the best prognostic value between 18 and 24h (AUC of 0.93). EEGs with dIBI amplitude ≥15 μV and duration 10s were specific for adverse outcome (89-100%) at 18-24h (n = 10). Extremely low voltage and invariant EEG patterns were indicative of adverse outcome at all time points. Conclusions: Automated analysis of the suppressed periods in EEG of neonates with HIE undergoing TH provides objective and early prognostic information. This objective tool can be used in a multimodal strategy for outcome assessment. Implementation of this method can facilitate clinical practice, improve risk stratification and aid therapeutic decision-making. A multicenter trial with a quantifiable outcome measure is warranted to confirm the predictive value of this method in a more heterogeneous dataset

Automated EEG analysis to quantify brain function in preterm and term neonates

This PhD project aims to define specific EEG maturational features in premature infants and to develop an objective scoring system for predicting neurodevelopmental outcome at 2 years of corrected age. Differentiation with transient EEG changes willl give insight in causal factors and timing of brain injury in premature en term infants, which allows to improve neuroprotective measures. Development and implementation of algorithms may contribute to reliable interpretation by non- EEG experts and will enable the implementation of multichannel EEG as a standard investigation in neonatal intensive care units. This clinical study will focus on the quantification, interpretation and classification of (ab)normal maturational EEG features in premature infants. The first part of this study is aimed at the automation of EEG analysis. For automatic quantification and algorithm development of clinically relevant patterns in the background EEG of premature babies, we will collaborate with engineers of KUL ESAT-SISTA. The second part is aimed at the identification of quantitative measures which are sensitive and specific for predicting neurodevelopmental outcome; therefore we will analyze EEG data of both prerterm and term infants. We will do an assessment of brain function by EEG in neonates who experience acute interference with cerebral integrity (peripartal asphyxia, seizures, flow metabolism coupling). On the other hand, we will measure brain maturation in premature infants by consecutive measurements.status: publishe

Supporting Data for Manuscript on Automated Quiet Sleep Detection for Preterm Babies

The supporting data was created in MATLAB (R2014a onwards) and is largely provided in the software's default .mat format and would be most easily accessible using this same software package. The data was used to produce the results published in the Manuscript titled 'An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assessing Brain Maturation'. The data was produced between October 2016 - February 2017 inclusive. Four .zip folders are provided containing the data-set. A supporting 'readme.txt' file detailing the format of the data is also included

Supporting Data for Manuscript on Automated Quiet Sleep Detection for Preterm Babies

The supporting data was created in MATLAB (R2014a onwards) and is largely provided in the software's default .mat format and would be most easily accessible using this same software package. The data was used to produce the results published in the Manuscript titled 'An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assessing Brain Maturation'. The data was produced between October 2016 - February 2017 inclusive. Four .zip folders are provided containing the data-set. A supporting 'readme.txt' file detailing the format of the data is also included

Applying a data-driven approach to quantify EEG maturational deviations in preterms with normal and abnormal neurodevelopmental outcomes

Premature babies are subjected to environmental stresses that can affect brain maturation and cause abnormal neurodevelopmental outcome later in life. Better understanding this link is crucial to developing a clinical tool for early outcome estimation. We defined maturational trajectories between the Electroencephalography (EEG)-derived 'brain-age' and postmenstrual age (the age since the last menstrual cycle of the mother) from longitudinal recordings during the baby's stay in the Neonatal Intensive Care Unit. Data consisted of 224 recordings (65 patients) separated for normal and abnormal outcome at 9-24 months follow-up. Trajectory deviations were compared between outcome groups using the root mean squared error (RMSE) and maximum trajectory deviation (δmax). 113 features were extracted (per sleep state) to train a data-driven model that estimates brain-age, with the most prominent features identified as potential maturational and outcome-sensitive biomarkers. RMSE and δmax showed significant differences between outcome groups (cluster-based permutation test, p < 0.05). RMSE had a median (IQR) of 0.75 (0.60-1.35) weeks for normal outcome and 1.35 (1.15-1.55) for abnormal outcome, while δmax had a median of 0.90 (0.70-1.70) and 1.90 (1.20-2.90) weeks, respectively. Abnormal outcome trajectories were associated with clinically defined dysmature and disorganised EEG patterns, cementing the link between early maturational trajectories and neurodevelopmental outcome.status: publishe

A Bayesian parametric model for quantifying brain maturation from sleep-EEG in the vulnerable newborn baby.

Newborn babies, particularly preterms, can exhibit early deviations in sleep maturation as seen by Electroencephalogram (EEG) recordings. This may be indicative of cognitive problems by school-age. The current 'clinically-driven' approach uses separate algorithms to first extract sleep states and then predict EEG 'brain-age'. Maturational deviations are identified when the brain-age no longer matches the Postmenstrual Age (PMA, the age since the last menstrual cycle of the mother). However, the PMA range where existing sleep staging algorithms perform optimally, is limited, which subsequently limits the PMA range for brain-age prediction. We introduce a Bayesian Parametric Model (BPM) as a single end-to-end solution to directly estimate brain-age, modelling for sleep state maturation without requiring a separately optimized sleep staging algorithm. Comparison of this model with a traditional multi-stage approach, yields a similar Krippendorff's $\alpha = 0.92$ (a performance measure ranging from 0 (chance agreement) to 1 (perfect agreement)) with the BPM performing better at younger ages <30 weeks PMA. The BPM's potential to detect maturational deviations is also explored on a few preterm babies who were abnormal at 9 months follow-up.status: publishe

Métricas de rendimiento ponderadas para la detección automática de convulsiones neonatales utilizando datos de EEG de múltiples puntuaciones

In neonatal intensive care units, there is a need for around the clock monitoring of electroencephalogram (EEG), especially for recognizing seizures. An automated seizure detector with an acceptable performance can partly fill this need. In order to develop a detector, an extensive dataset labeled by experts is needed. However, accurately defining neonatal seizures on EEG is a challenge, especially when seizure discharges do not meet exact definitions of repetitiveness or evolution in amplitude and frequency. When several readers score seizures independently, disagreement can be high. Commonly used metrics such as good detection rate (GDR) and false alarm rate (FAR) derived from data scored by multiple raters have their limitations. Therefore, new metrics are needed to measure the performance with respect to the different labels. In this paper, instead of defining the labels by consensus or majority voting, popular metrics including GDR, FAR, positive predictive value, sensitivity, specificity, and selectivity are modified such that they can take different scores into account. To this end, 353 hours of EEG data containing seizures from 81 neonates were visually scored by a clinical neurophysiologist, and then processed by an automated seizure detector. The scored seizures were mixed with false detections of an automated seizure detector and were relabeled by three independent EEG readers. Then, all labels were used in the proposed performance metrics and the result was compared with the majority voting technique and showed higher accuracy and robustness for the proposed metrics. Results were confirmed using a bootstrapping test