Automated State Analyses: Proposed Applications to Neonatal Neurointensive Care

Quantitative electroencephalogram in term neonates under different sleep states

Electroencephalogram (EEG) can be used to assess depth of consciousness, but interpreting EEG can be challenging, especially in neonates whose EEG undergo rapid changes during the perinatal course. EEG can be processed into quantitative EEG (QEEG), but limited data exist on the range of QEEG for normal term neonates during wakefulness and sleep, baseline information that would be useful to determine changes during sedation or anesthesia. We aimed to determine the range of QEEG in neonates during awake, active sleep and quiet sleep states, and identified the ones best at discriminating between the three states. Normal neonatal EEG from 37 to 46 weeks were analyzed and classified as awake, quiet sleep, or active sleep. After processing and artifact removal, total power, power ratio, coherence, entropy, and spectral edge frequency (SEF) 50 and 90 were calculated. Descriptive statistics were used to summarize the QEEG in each of the three states. Receiver operating characteristic (ROC) curves were used to assess discriminatory ability of QEEG. 30 neonates were analyzed. QEEG were different between awake vs asleep states, but similar between active vs quiet sleep states. Entropy beta, delta2 power %, coherence delta2, and SEF50 were best at discriminating awake vs active sleep. Entropy beta had the highest AUC-ROC ≥ 0.84. Entropy beta, entropy delta1, theta power %, and SEF50 were best at discriminating awake vs quiet sleep. All had AUC-ROC ≥ 0.78. In active sleep vs quiet sleep, theta power % had highest AUC-ROC > 0.69, lower than the other comparisons. We determined the QEEG range in healthy neonates in different states of consciousness. Entropy beta and SEF50 were best at discriminating between awake and sleep states. QEEG were not as good at discriminating between quiet and active sleep. In the future, QEEG with high discriminatory power can be combined to further improve ability to differentiate between states of consciousness.

Automatic neonatal sleep stage classification: A comparative study

Sleep is an essential feature of living beings. For neonates, it is vital for their mental and physical development. Sleep stage cycling is an important parameter to assess neonatal brain and physical development. Therefore, it is crucial to administer newborn's sleep in the neonatal intensive care unit (NICU). Currently, Polysomnography (PSG) is used as a gold standard method for classifying neonatal sleep patterns, but it is expensive and requires a lot of human involvement. Over the last two decades, multiple researchers are working on automatic sleep stage classification algorithms using electroencephalography (EEG), electrocardiography (ECG), and video. In this study, we present a comprehensive review of existing algorithms for neonatal sleep, their limitations and future recommendations. Additionally, a brief comparison of the extracted features, classification algorithms and evaluation parameters is reported in the proposed study.

"The First Thousand Days" Define a Fetal/Neonatal Neurology Program

Gene-environment interactions begin at conception to influence maternal/placental/fetal triads, neonates, and children with short- and long-term effects on brain development. Life-long developmental neuroplasticity more likely results during critical/sensitive periods of brain maturation over these first 1,000 days. A fetal/neonatal program (FNNP) applying this perspective better identifies trimester-specific mechanisms affecting the maternal/placental/fetal (MPF) triad, expressed as brain malformations and destructive lesions. Maladaptive MPF triad interactions impair progenitor neuronal/glial populations within transient embryonic/fetal brain structures by processes such as maternal immune activation. Destructive fetal brain lesions later in pregnancy result from ischemic placental syndromes associated with the great obstetrical syndromes. Trimester-specific MPF triad diseases may negatively impact labor and delivery outcomes. Neonatal neurocritical care addresses the symptomatic minority who express the great neonatal neurological syndromes: encephalopathy, seizures, stroke, and encephalopathy of prematurity. The asymptomatic majority present with neurologic disorders before 2 years of age without prior detection. The developmental principle of ontogenetic adaptation helps guide the diagnostic process during the first 1,000 days to identify more phenotypes using systems-biology analyses. This strategy will foster innovative interdisciplinary diagnostic/therapeutic pathways, educational curricula, and research agenda among multiple FNNP. Effective early-life diagnostic/therapeutic programs will help reduce neurologic disease burden across the lifespan and successive generations.

Entropy-Based Quantitative Electroencephalogram Analysis for Diagnosing Attention-Deficit Hyperactivity Disorder in Girls

Diagnosis of attention-deficit hyperactivity disorder (ADHD) is currently based on core symptoms or checklists; however, the inevitability of practitioner subjectivity leads to over- and underdiagnosis. Although the Federal Drug Administration has approved an elevated theta/beta ratio (TBR) of the electroencephalogram (EEG) band as a tool for assisting ADHD diagnosis, several studies have reported no significant differences of the TBR between ADHD and control subjects. This study detailed the development of a method based on approximate entropy (ApEn) analysis of EEG to compare ADHD and control groups. Differences between ADHD presentation in boys and girls indicate the necessity of separate investigations. This study enrolled 30 girls with ADHD and 30 age-matched controls. The results revealed significantly higher ApEn values in most brain areas in the control group than in the ADHD group. Compared with TBR-related feature descriptors, ApEn-related feature descriptors can produce the higher average true positive rate (0.846), average true negative rate (0.814), average accuracy (0.817), and average area under the receiver operating characteristic curve value (0.862). Therefore, compared with TBR, ApEn possessed the better potential for differentiating between girls with ADHD and controls.

Review of sleep-EEG in preterm and term neonates

Neonatal sleep is a crucial state that involves endogenous driven brain activity, important for neuronal survival and guidance of brain networks. Sequential EEG-sleep analysis in preterm infants provides insights into functional brain integrity and can document deviations of the biologically pre-programmed process of sleep ontogenesis during the neonatal period. Visual assessment of neonatal sleep-EEG, with integration of both cerebral and non-cerebral measures to better define neonatal state, is still considered the gold standard. Electrographic patterns evolve over time and are gradually time locked with behavioural characteristics which allow classification of quiet sleep and active sleep periods during the last 10weeks of gestation. Near term age, the neonate expresses a short ultradian sleep cycle, with two distinct active and quiet sleep, as well as brief periods of transitional or indeterminate sleep. Qualitative assessment of neonatal sleep is however challenged by biological and environmental variables that influence the expression of EEG-sleep patterns and sleep organization. Developing normative EEG-sleep data with the aid of automated analytic methods, can further improve our understanding of extra-uterine brain development and state organization under stressful or pathological conditions. Based on those developmental biomarkers of normal and abnormal brain function, research can be conducted to support and optimise sleep in the NICU, with the ultimate goal to improve therapeutic interventions and neurodevelopmental outcome.

Unobtrusive sleep state measurements in preterm infants - A review

Sleep is important for the development of preterm infants. During sleep, neural connections are formed and the development of brain regions is triggered. In general, various rudimentary sleep states can be identified in the preterm infant, namely active sleep (AS), quiet sleep (QS) and intermediate sleep (IS). As the infant develops, sleep states change in length and organization, with these changes as important indicators of brain development. As a result, several methods have been deployed to distinguish between the different preterm infant sleep states, among which polysomnography (PSG) is the most frequently used. However, this method is limited by the use of adhesive electrodes or patches that are attached to the body by numerous cables that can disturb sleep. Given the importance of sleep, this review explores more unobtrusive methods that can identify sleep states without disturbing the infant. To this end, after a brief introduction to preterm sleep states, an analysis of the physiological characteristics associated with the different sleep states is provided and various methods of measuring these physiological characteristics are explored. Finally, the advantages and disadvantages of each of these methods are evaluated and recommendations for neonatal sleep monitoring proposed.

Nurses' Awareness of Preterm Neonates' Sleep in the NICU

Introduction:

Fetus and neonate spend most of their time sleeping inside and outside the womb. Sleep is considered a crucial action of neonatal period similar to breathing and nutrition. It plays a key role in brain development. Today, it is shown that sleep plays a predominant role in body temperature regulation, energy saving and neuronal detoxification. Sleep is the most important behavioral state of neonates, particularly in preterm ones. Noise, light, invasive treatment and caring activities are among disturbing factors in the neonatal intensive care unit (NICU) that leave negative impacts on brain development through disturbing the sleep process.

Materials and Methods:

This descriptive study assessed all NICU nurses of Ali-ibn-Abitaleb hospital using the census sampling method. Demographic data was collected through a questionnaire with 10 questions about active sleep (AS) cycles, also referred to as REM, methods for inducing AS and AS specifications in neonates. The questionnaire was distributed between the nurses. After completion, data was analyzed using SPSS 16 and descriptive statistics method.

Findings:

According to analyses, 24%, 20%, 48% and 92% of nurses gave correct answers to questions about AS cycle, AS in neonates, the role of sleep in saving energy and ideal noise level, respectively.

Conclusion:

According to results, nurses had a low level of knowledge towards neonatal sleep. All nurses need to know the importance of sleep in preterm neonates. The main role of inducing sleep is to protect the development of the neonates’ brain in the NICU. Those nurses who spend a remarkable portion of their time for caring neonates in the NICU play a significant role in neonatal sleep care.

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. The present study was set out to test whether IHS is stable enough for clinical use, and whether it could be an objective feature of EEG normality. We analyzed 31 neonatal EEG recordings that had been clinically classified as normal (n = 14) or abnormal (n = 17) using holistic, conventional visual criteria including amplitude, focal differences, qualitative synchrony, and focal abnormalities. We selected 20-min epochs of discontinuous background pattern. ASI values were computed separately for different channel pair combinations and window lengths to define them for the optimal ASI intraindividual stability. Finally, ROC curves were computed to find trade-offs related to compromised data lengths, a common challenge in neonatal EEG studies. Using the average of four consecutive 2.5-min epochs in the centro-occipital bipolar derivations gave ASI estimates that very accurately distinguished babies clinically classified as normal vs. abnormal. It was even possible to draw a cut-off limit (ASI~3.6) which correctly classified the EEGs in 97% of all cases. Finally, we showed that compromising the length of EEG segments from 20 to 5 min leads to increased variability in ASI-based classification. Our findings support the prior literature that IHS is an important feature of normal neonatal brain function. We show that ASI may provide diagnostic value even at individual level, which strongly supports its use in prospective clinical studies on neonatal EEG as well as in the feature set of upcoming EEG classifiers.

Sleep EEG composition in the first three months of life in monozygotic and dizygotic twins

We investigated genetic influence on sleep electroencephalogram (EEG) composition by a classical twin study of monozygotic (MZ) and dizygotic (DZ) twins in the first 3 months of life. Polysomnographic (PSG) recordings were obtained in 10 MZ and 20 DZ twin pairs in the 37th, 46th, and 52nd week of postmenstrual age (PMA). The EEG power spectra were generated on the basis of fast Fourier transformation (FFT). Genetic influence on active sleep/rapid eye movement (AS/REM)] and quiet sleep/non rapid eye movement (QS/NREM) sleep composition was estimated by calculating within pair concordance and the intraclass correlation coefficients (ICCs) for delta (0.5-3.5 Hz), theta (4-7.5 Hz), alpha (8-11.5 Hz), sigma (12-14 Hz), and beta (14.5-20 Hz) at central derivation. MZ twins show higher ICCs than DZ twins for alpha, sigma, and beta spectral powers during QS/NREM sleep in the 37th, 46th, and 52nd week PMA. However, there was no significant difference (P > .05) between the 2 types of twins in absolute differences of EEG spectral power of the alpha, beta, and sigma frequency ranges in the 37th, 46th, and 52nd week PMA. The greatest mean absolute difference within MZ and DZ twin pairs and also between MZ and DZ twin groups was identified in the delta frequency range. Our findings gave an indication of genetic influence on alpha, sigma, and beta frequency ranges in the QS/NREM sleep stage.

An interdisciplinary fetal/neonatal neurology program

A fetal/neonatal neurology program should encompass interdisciplinary service, educational and research objectives, merging curricula concerning maternal, placental, fetal and neonatal contributions to brain health and disease. This approach is anchored by research in early life programming that demonstrates that prenatal and postnatal factors influence long-term neurologic health. This concept also supports the design of neuroprotective interventions during critical periods of brain development when brain circuitries more optimally adapt to maturational challenges. Preventive, rescue and repair protocols will transform pediatric medical practices, to promote improved childhood outcomes. Inclusion of life-course science and research will identify medical and socioeconomic factors that favorably or adversely affect quality of life into adulthood. Greater awareness of the convergence of developmental origins of brain health and disease and developmental aging theories will influence public health policies, to encourage financial support for programs that will improve the quality of life for the child and adult.

Maturational changes in automated EEG spectral power analysis in preterm infants

Our study aimed at automated power spectral analysis of the EEG in preterm infants to identify changes of spectral measures with maturation. Weekly (10-20 montage) 4-h EEG recordings were performed in 18 preterm infants with GA <32 wk and normal neurological follow-up at 2 y, resulting in 79 recordings studied from 27(+4) to 36(+3) wk of postmenstrual age (PMA, GA + postnatal age). Automated spectral analysis was performed on 4-h EEG recordings. The frequency spectrum was divided in delta 1 (0.5-1 Hz), delta 2 (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) band. Absolute and relative power of each frequency band and spectral edge frequency were calculated. Maturational changes in spectral measures were observed most clearly in the centrotemporal channels. With advancing PMA, absolute powers of delta 1 to 2 and theta decreased. With advancing PMA, relative power of delta 1 decreased and relative powers of alpha and beta increased, respectively. In conclusion, with maturation, spectral analysis of the EEG showed a significant shift from the lower to the higher frequencies. Computer analysis of EEG will allow an objective and reproducible analysis for long-term prognosis and/or stratification of clinical treatment.

Physiologic brain dysmaturity in late preterm infants

Neonatal EEG sleep was used to determine whether differences are expressed between healthy late preterm and full-term (FT) groups. Twenty-seven 24-channel multihour studies were recorded at similar postmenstrual ages (PMA) and analyzed for eight asymptomatic late preterm infants (LPT) compared with 19 healthy FT infants as a preliminary analysis, followed by a comparison of a subset of eight FT infants, matched for gender, race, and PMA. Z scores were performed on data sets from each group pair comparing each of seven EEG/Sleep measures for entire recordings, active sleep (AS) and quiet sleep (QS) segments and artifact-free intervals. Six of seven measures showed differences between the eight LPT and eight matched FT cohort pair comparisons of >0.3; rapid eye movements, arousals during QS, spectral correlations between homologous centrotemporal regions during QS, spectral beta/alpha power ratios during AS and QS, a spectral measure of respiratory regularity during QS, and sleep cycle length. Quantitative neurophysiologic analyses define differences in brain maturation between LPT and FT infants at similar PMA. Altered EEG/Sleep behaviors in the LPT are biomarkers of developmental neuroplasticity involving interconnected neuronal networks adapting to conditions of prematurity for this largest segment of the preterm neonatal population.

Multivariate analysis of full-term neonatal polysomnographic data

INTRODUCTION

Polysomnography (PSG) is one of the most important noninvasive methods for studying maturation of the child brain. Sleep in infants is significantly different from sleep in adults. This paper addresses the problem of computer analysis of neonatal polygraphic signals.

METHODS

We applied methods designed for differentiating three important neonatal behavioral states: quiet sleep, active sleep, and wakefulness. The proportion of these states is a significant indicator of the maturity of the newborn brain in clinical practice. In this study, we used data provided by the Institute for Care of Mother and Child, Prague (12 newborn infants of similar postconceptional age). The data were scored by an experienced physician to four states (wake, quiet sleep, active sleep, movement artifact). For accurate classification, it was necessary to determine the most informative features. We used a method based on power spectral density (PSD) applied to each EEG channel. We also used features derived from electrooculogram (EOG), electromyogram (EMG), ECG, and respiration [pneumogram (PNG)] signals. The most informative feature was the measure of regularity of respiration from the PNG signal. We designed an algorithm for interpreting these characteristics. This algorithm was based on Markov models.

RESULTS

The results of automatic detection of sleep states were compared to the "sleep profiles" determined visually. We evaluated both the success rate and the true positive rate of the classification, and statistically significant agreement of the two scorings was found. Two variants, for learning and for testing, were applied, namely learning from the data of all 12 newborns and tenfold cross-validation, and learning from the data of 11 newborns and testing on the data from the 12th newborn. We utilized information obtained from several biological signals (EEG, ECG, PNG, EMG, EOG) for our final classification. We reached the final success rate of 82.5%. The true positive rate was 81.8% and the false positive rate was 6.1%.

DISCUSSION

The most important step in the whole process is feature extraction and feature selection. In this process, we used visualization as an additional tool that helped us to decide which features to select. Proper selection of features may significantly influence the success rate of the classification. We made a visual comparison of the computed features with the manual scoring provided by the expert. A hidden Markov model was used for classification. The advantage of this model is that it determines the future behavior of the process by its present state. In this way, it preserves information about temporal development.

The development of potentially better practices to support the neurodevelopment of infants in the NICU

OBJECTIVE

To review the existing evidence used to identify potentially better care practices that support newborn brain development.

STUDY DESIGN

Literature review.

RESULT

Sixteen potentially better practices are identified and grouped into two operational clinical bundles based upon timing for recommended implementation.

CONCLUSION

Existing evidence supports the implementation of selected care practices that potentially may support newborn brain development.

Ontogeny of EEG-sleep from neonatal through infancy periods

Serial neonatal and infant electroencephalographic (EEG)-polysomnographic studies document the ontogeny of cerebral and noncerebral physiologic behaviors based on visual inspection or computer analyses. EEG patterns and their relationship to other physiologic signals serve as templates for normal brain organization and maturation, subserving multiple interconnected neuronal networks. Interpretation of serial EEG-sleep patterns also helps track the continuity of brain functions from intrauterine to extrauterine time periods. Recognition of the ontogeny of behavioral and electrographic patterns provides insight into the developmental neurophysiological expression of neural plasticity. Sleep ontogenesis from neonatal and infancy periods documents expected patterns of postnatal brain maturation, which allows for alterations from genetically programmed neuronal processes under stressful and/or pathological conditions. Automated analyses of cerebral and noncerebral signals provide time- and frequency-dependent computational phenotypes of brain organization and maturation in healthy or diseased states. Research pertaining to the developmental origins of health and disease can use these computational phenotypes to design longitudinal studies for the assessment of gene-environment interactions. Computational strategies may ultimately improve our diagnostic skills to identify special-needs children and to track the neurorehabilitative care of the high-risk fetus, neonate, and infant.

Quantitative topographic differentiation of the neonatal EEG

OBJECTIVE

To test the discriminatory topographic potential of a new method of the automatic EEG analysis in neonates. A quantitative description of the neonatal EEG can contribute to the objective assessment of the functional state of the brain, and may improve the precision of diagnosing cerebral dysfunctions manifested by 'disorganization', 'dysrhythmia' or 'dysmaturity'.

METHODS

21 healthy, full-term newborns were examined polygraphically during sleep (EEG-8 referential derivations, respiration, ECG, EOG, EMG). From each EEG record, two 5-min samples (one from the middle of quiet sleep, the other from the middle of active sleep) were subject to subsequent automatic analysis and were described by 13 variables: spectral features and features describing shape and variability of the signal. The data from individual infants were averaged and the number of variables was reduced by factor analysis.

RESULTS

All factors identified by factor analysis were statistically significantly influenced by the location of derivation. A large number of statistically significant differences were also established when comparing the effects of individual derivations on each of the 13 measured variables. Both spectral features and features describing shape and variability of the signal are largely accountable for the topographic differentiation of the neonatal EEG.

CONCLUSIONS

The presented method of the automatic EEG analysis is capable to assess the topographic characteristics of the neonatal EEG, and it is adequately sensitive and describes the neonatal electroencephalogram with sufficient precision.

SIGNIFICANCE

The discriminatory capability of the used method represents a promise for their application in the clinical practice.

Effects of antenatal magnesium sulfate and corticosteroid therapy on sleep states of preterm infants

This exploratory longitudinal study was designed to compare the neonatal illness severity, sleep-wake, and respiratory sleep behaviors of preterm infants whose mothers received prenatal corticosteroids and/or magnesium sulfate (MgSO4) with those of infants whose mothers did not receive these medications. The 134 infants were divided into four groups: those whose mothers received MgSO4 only, those who received steroids only, those who received both MgSO4 and steroids, and those who received neither. The groups did not differ on infant characteristics or illness severity. Infants exposed to MgSO4 had more active sleep without rapid eye movement, indicating poorly organized active sleep. The MgSO4 -only group had higher quiet sleep regularity scores and fewer state changes. These findings suggest that fetal exposure to MgSO4 may subtly affect the central nervous system.

Automated State Analyses: Proposed Applications to Neonatal Neurointensive Care

The two principal challenges of neonatal physiologic monitoring device are: (1) the development of computational strategies that consider the rudimentary forms of neonatal sleep state especially for preterm infants and (2) any physiologic monitoring device for clinical applications in a neonatal intensive care setting must be small, portable, and user-friendly. Our multicenter neonatal sleep consortium has acquired more than 1,100 multihour EEG-sleep recordings on over 370 neonates, ranging from 24 to 44 weeks gestation. Each recording was visually-scored for state, arousals, movements, and rapid eye movements, which were used as templates when applying spectral analyses. The authors have defined a brain dysmaturity index to represent functional brain reorganization as the prenate matures to a full-term age; delayed or accelerated physiologic behaviors have been described for the preterm cohort when compared to the full-term group at the same postmenstrual age. Seven EEG-sleep measures comprise this index: quiet sleep percentage, sleep cycle length, rapid eye movements, arousals, spectral beta EEG energies, spectral EEG correlations, and a spectral measure of respiratory regularity. Linear and nonlinear computational algorithms are being developed to automate the computation of the dysmaturity index and to identify new feature types that correlate with dysmaturity. Automated neonatal sleep monitoring system can potentially improve neonatal neurointensive care by facilitating analyses of pervasive neonatal brain disorders expressed primarily as altered sleep state organization, and help predict altered developmental trajectories of children at higher risk for neurologic sequelae.