Atlas of Neonatal Electroencephalography (4th Edition)
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Return to Book Page. Atlas of Neonatal Electroencephalography by Eli M. Mizrahi ,. Richard A. Peter Kellaway. Thoroughly revised and updated by internationally recognized experts, the Third Edition of this popular and widely used atlas reflects twelve years of vital advances in electrodiagnosis of neurologic function in neonates. The authors have distilled the vast, complex literature on neonatal EEG to provide a practical, contemporary, superbly illustrated guide to performing EE Thoroughly revised and updated by internationally recognized experts, the Third Edition of this popular and widely used atlas reflects twelve years of vital advances in electrodiagnosis of neurologic function in neonates.
The authors have distilled the vast, complex literature on neonatal EEG to provide a practical, contemporary, superbly illustrated guide to performing EEG in neonates and interpreting both common and unusual patterns. This edition includes digital as well as analog EEG and features over brand-new, full-sized reproductions of EEG tracings. The authors demonstrate state-of-the-art improvements in recording technique and highlight recent advances in the understanding of normal and abnormal brain development.
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This book is not yet featured on Listopia. Community Reviews. Showing Rating details. All Languages. More filters. Minimally invasive, automated cot-side tools for monitoring early neurological development can be used to guide individual treatment and benchmark novel interventional studies. We develop an automated estimate of the EEG maturational age EMA for application to serial recordings in preterm infants.
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The EMA estimate was based on a combination of 23 computational features estimated from both the full EEG recording and a period of low EEG activity 46 features in total. The combination function support vector regression was trained using serial EEG recordings from 39 preterm infants with a gestational age less than 28 weeks and normal neurodevelopmental outcome at 12 months of age. The EMA is a surrogate measure of age that can accurately determine brain maturation in preterm infants. Premature birth is a significant health problem that affects every tenth live birth and results in half of all admissions to the neonatal intensive care unit NICU 1 , 2.
Mortality and morbidity is increased in neonates born prematurely with neurological deficits persisting over the longer term 3 , 4. These deficits result in a 10 to fold increase in annual healthcare costs 5. The premature brain is highly susceptible to disruption as it undergoes large scale, activity-dependent neuronal wiring during the last trimester 6 , 7.
Ensuring optimal brain development through dedicated neuro-critical care, therefore, requires effective monitoring of functional brain maturation 8. The major difficulty with the implementation of EEG monitoring is achieving interpretation by the human expert for long periods of time, on demand. This can be effectively overcome using automated analyses. An important aspect in interpreting the EEG of preterm infants is the use of post-menstrual age PMA as a contextual benchmark It can also provide support to maturational observations from the visual interpretation of the preterm EEG and be used as benchmark when translating the findings of animal models The EMA was compared to the clinically determined age PMA at the time of EEG recording and key practical aspects of its implementation were assessed in the context of wide scale brain monitoring and as an early outcome proxy for novel interventional studies.
Linear mixed effects modelling resulted in a correlation of 0. Repeated EEG measures longitudinal recordings were available in 34 infants. Several individual features showed a significant correlation with the PMA.
Atlas of Neonatal Electroencephalography, Fourth Edition
The result of feature selection during the development of the EEG maturational age. Note that, the features selected most often are not always the features with the highest individual correlation between EMA and PMA. The EMA used all features and applied to recordings that passed strict artefact detection. We developed an automated estimator of functional cortical maturation using cot-side EEG recordings from infants with normal neurodevelopmental outcome at 12 months of age.
Streamlining the EMA measurement by reducing the number of computational features or the number of EEG channels did not significantly affect its performance. The performance of the EMA is substantial given that there is an inherent error of at least plus or minus one week in the definition of PMA due to variability between conception and the last menstrual period These results align with previous findings that the rEEG and temporal information are correlated with maturation in the EEG 17 , Prior studies typically focus on individual features that maximise the correlation with PMA, however, we have shown that when combining features to improve maturational estimates the highest correlating features are not necessarily chosen by the feature selection algorithm.
The most notable difference was in the relative beta power RBP which has been shown to correlate with PMA in infants with a gestational age less than 32 weeks but was never selected see Fig.
Atlas Neonatal Electroencephalography Fourth Edition by Eli Mizrahi Richard Hrachovy - AbeBooks
This may be due to the dominance of other features representing similar aspects of the EEG, overly age specific correlations that do not necessarily hold over a wider range of PMA or differences in inter-uterine versus extra-uterine maturation It is interesting that amplitude was the most important maturational feature of the EEG; a measurement that may be considered as underappreciated as a maturational feature in the neonatal EEG literature, although recent work on the analysis of the amplitude integrated EEG aEEG supports the usefulness of amplitude for assessing maturation 20 — There are two factors which may challenge the wide scale implementation of EMA monitoring.
The electrode placement must be, however, carefully selected to optimize the susceptibility to artefacts and the information value relating to maturation 24 , The second is potential clinical confounders such as neurological condition, non-pharmacological treatments and medication. There is evidence to suggest that medications, particularly sedatives, affect the EEG and should be considered during interpretation These considerations are also highly relevant when incorporating a variety of neonatal EEG analysis algorithms into neuro-critical care 14 , 27 , Longitudinal monitoring is a powerful method for assessing human health.
In infants, existing methods use measures such as weight, head circumference and clinical assessment of motor function 29 , In recent years, analyses have progressed to include measures based functional MRI and DNA methylation in cord blood 31 , While it is always difficult to compare studies, the correlation between EMA and the PMA was higher than these methods.
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Firstly, it is minimally disruptive and relies on cot-side data that routinely accumulates in the NICU. Secondly, it is a direct measure of the functional development in the preterm brain, which is the target organ in most attempts to improve neonatal care. It also supplements the paradigm of assessing preterm EEG for acute and chronic abnormalities as an additional measurement over a longer time scale 12 , The EMA can accurately, and continuously, track the maturation of cortical function in preterm infants over their entire stay in the NICU.
The practical significance of this development will ultimately be measured in terms of clinical usefulness. This will be determined in prospective clinical trials which evaluate the added value of such a measure in the individualized neurological care of preterm infants. The EMA also offers an unprecedented opportunity to measure the effects of various treatments and therapies for preterm infants.
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The EMA as an early outcome measure holds promise for novel interventional studies by expediting their development cycle from several years of follow-up to near real-time assessment. The overall organisation of this study is shown in Fig.
All methods were carried out in accordance with relevant guidelines and regulations. A The flow diagram of the EMA algorithm. B The EEG dataset used in this study. The table shows additional demographics of the EEG cohort used in this study - data are summarised as median interquartile range , except gender which is given as a count.
Atlas of Neonatal Electroencephalography, Fourth Edition
C Evaluating the EMA within a leave-one-subject cross-validation. The diagonal lines denote errors of plus and minus 0, 1 and 2 weeks. Each infant had a cranial ultrasound, once a week, until 34 weeks PMA. Informed parental consent was obtained for all infants included in the study. The PMA of each infant was defined by the last menstrual period LMP of the mother and adjusted using ultrasound measurements in the first trimester if there was significant deviation between LMP and ultrasound analysis.
Out of an initial cohort of EEG recordings from 67 infants, 43 infants EEG recordings had normal neurodevelopmental outcome, and 16 infants were lost to follow up. The EEG signal was initially pre-processed with a band pass filter low cut-off frequency of 0. One hour epochs were chosen based on the reported duration of sleep states in preterm infants with the aim of capturing a significant proportion of a full sleep cycle in an epoch Several features of amplitude, spatial organization and temporal organization were calculated from the EEG using these annotations.
The computational features are listed in Fig.
Each feature was estimated per channel and the median value across channels was used. The EEG data was not preselected with any visual criteria, rather a simple automated artefact detection AD method was employed to exclude epochs with excessively low or high amplitude. Applying the EEG in a challenging environment such as the NICU results in recordings that are commonly contaminated by a large variety of artefacts.