Resting state electroencephalographic brain activity in neonates can predict age and is indicative of neurodevelopmental outcome

Objective: Electroencephalography (EEG) can be used to estimate neonates\u27 biological brain age. Discrepancies between postmenstrual age and brain age, termed the brain age gap, can potentially quantify maturational deviation. Existing brain age EEG models are not well suited to clinical cot-side use for estimating neonates\u27 brain age gap due to their dependency on relatively large data and pre-processing requirements. Methods: We trained a deep learning model on resting state EEG data from preterm neonates with normal neurodevelopmental Bayley Scale of Infant and Toddler Development (BSID) outcomes, using substantially reduced data requirements. We subsequently tested this model in two independent datasets from two clinical sites. Results: In both test datasets, using only 20 min of resting-state EEG activity from a single channel, the model generated accurate age predictions: mean absolute error = 1.03 weeks (p-value = 0.0001) and 0.98 weeks (p-value = 0.0001). In one test dataset, where 9-month follow-up BSID outcomes were available, the average neonatal brain age gap in the severe abnormal outcome group was significantly larger than that of the normal outcome group: difference in mean brain age gap = 0.50 weeks (p-value = 0.04). Conclusions: These findings demonstrate that the deep learning model generalises to independent datasets from two clinical sites, and that the model\u27s brain age gap magnitudes differ between neonates with normal and severe abnormal follow-up neurodevelopmental outcomes. Significance: The magnitude of neonates\u27 brain age gap, estimated using only 20 min of resting state EEG data from a single channel, can encode information of clinical neurodevelopmental value

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

Peer reviewe

Resting state electroencephalographic brain activity in neonates can predict age and is indicative of neurodevelopmental outcome

Objective: Electroencephalography (EEG) can be used to estimate neonates’ biological brain age. Discrepancies between postmenstrual age and brain age, termed the brain age gap, can potentially quantify maturational deviation. Existing brain age EEG models are not well suited to clinical cot-side use for estimating neonates’ brain age gap due to their dependency on relatively large data and pre-processing requirements. Methods: We trained a deep learning model on resting state EEG data from preterm neonates with normal neurodevelopmental Bayley Scale of Infant and Toddler Development (BSID) outcomes, using substantially reduced data requirements. We subsequently tested this model in two independent datasets from two clinical sites. Results: In both test datasets, using only 20 min of resting-state EEG activity from a single channel, the model generated accurate age predictions: mean absolute error = 1.03 weeks (p-value = 0.0001) and 0.98 weeks (p-value = 0.0001). In one test dataset, where 9-month follow-up BSID outcomes were available, the average neonatal brain age gap in the severe abnormal outcome group was significantly larger than that of the normal outcome group: difference in mean brain age gap = 0.50 weeks (p-value = 0.04). Conclusions: These findings demonstrate that the deep learning model generalises to independent datasets from two clinical sites, and that the model's brain age gap magnitudes differ between neonates with normal and severe abnormal follow-up neurodevelopmental outcomes. Significance: The magnitude of neonates’ brain age gap, estimated using only 20 min of resting state EEG data from a single channel, can encode information of clinical neurodevelopmental value

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