An automated bedside measure for monitoring neonatal cortical activity: a supervised deep learning-based electroencephalogram classifier with external cohort validation

Assessing Early Severity of Hypoxic-Ischemic Encephalopathy: The Role of Electroencephalogram Background in Addition to Sarnat Exam

OBJECTIVE

To assess the relationship between the Sarnat exam, early electroencephalogram (EEG) background, and death or neurodevelopmental impairment (NDI) at age 2 years among neonates with moderate to severe hypoxic-ischemic encephalopathy treated with therapeutic hypothermia.

STUDY DESIGN

Neonates enrolled in the High-dose Erythropoietin for Asphyxia and Encephalopathy trial with EEG (n = 463) or amplitude-integrated electroencephalogram (n = 15) reports available on the first day after birth were included in this cohort study. A Sarnat exam was performed between 1 and 6 hours after birth, and neonates were classified into 3 groups of increasing severity based on the number of severe features (none, 1-2, or 3+). EEG background continuity was extracted from reports and categorized as normal, excessively discontinuous, or severely abnormal. The primary outcome was severe NDI or death at age 2.

RESULTS

Among 478 neonates with hypoxic-ischemic encephalopathy, EEG background continuity was normal in 186 (39%), excessively discontinuous in 171 (36%), and severely abnormal in 121 (25%). For each additional severe feature on the Sarnat exam, the risk of abnormal EEG background increased by 16% (relative risk 1.16 [95% CI 1.09-1.23]). Both the Sarnat exam and EEG background severity were associated with an increased risk of severe NDI or death. After adjusting for Sarnat exam severity, severe EEG background remained associated with severe NDI and death (relative risk 5.7 [95% CI 3.7-8.9]).

CONCLUSIONS

The early EEG background provides additional information beyond the Sarnat exam and could be an additional early marker when assessing the severity of HIE.

Automatic detection and characterization of maturational neurobiomarkers identified as nested oscillations in premature newborns using high-density electroencephalography

Neural development leads to the evolution of electroencephalographic (EEG) characteristics during the third trimester of gestation. Theta activity in coalescence with slow waves (TA-SW) and delta brushes (DB) are key clinical neurobiomarkers in the evaluation of neurodevelopment in infants prior to full-term gestation. Both neurobiomarkers exhibit nested oscillations, a key feature of intrinsic spontaneous oscillatory activity, allowing the investigation of neural interaction development in the underlying circuits. In the present study, we propose an automatic approach for the detection and characterization of neurobiomarkers that (1) leverages high-density EEG (HD-EEG), (2) incorporates temporal dynamics and spatial distributions, and (3) evaluates the characteristics of nested oscillations. This method evaluates both slow and rapid neural activity, along with their cross-frequency coupling. Our results are in good agreement with those of clinical experts, achieving ROC performances and overall accuracies of 91 %/84 % and 83 %/75 % for TA-SW/DB events, respectively. Following detection and validation, we characterized and compared these two neurobiomarkers. Correlation-based spatial clustering showed that DB patterns were more symmetric and diffuse, whereas TA-SW patterns were more localized in the right and left temporal areas. Comparisons revealed (1) greater variability in spatial patterns for DB than for TA-SW, and that (2) while slow-wave coupling to fast oscillations showed similar characteristics for both neurobiomarkers, differences emerged in the amplitude and descending slope of the underlying slow waves. These findings suggested potential differences in the mechanisms underlying their generation, particularly in the modulation of slow oscillations. This approach represents a promising avenue for the quantitative evaluation of EEG signatures pertinent to early neural development in premature neonates.

Co-developing sleep-wake and sensory foundations for cognition in the human fetus and newborn

In older children and adults, cognition builds upon waking sensory experience which is consolidated during sleep. In the fetus and newborn, sensory input is instead largely experienced during sleep. The nature of these sensory inputs differs within sleep, between active and quiet sleep, as well as versus wakefulness. Here, sleep-wake organisation in the fetus and newborn is reviewed, and then its interaction with sensory inputs discussed with a focus on somatosensory and auditory modalities. Next, these ideas are applied to how neurological insults affect early development, using fetal growth restriction as a test case. Finally, the argument is made that taking account of sleep-wake state during perinatal functional neuroimaging can better index sensorimotor, language, and cognitive brain activities, potentially improving its diagnostic and prognostic value. To sum up, sensory and sleep-wake functions go hand in hand during early human development. Perturbation of these twinned functions by neurological insults may mediate later neurodevelopmental deficits. Perinatal neuroimaging has the potential to track these trajectories, feasibly identifying opportunities to therapeutically intervene.

Quantitative EEG features during the first day correlate to clinical outcome in perinatal asphyxia

OBJECTIVE

To assess whether computational electroencephalogram (EEG) measures during the first day of life correlate to clinical outcomes in infants with perinatal asphyxia with or without hypoxic-ischemic encephalopathy (HIE).

METHODS

We analyzed four-channel EEG monitoring data from 91 newborn infants after perinatal asphyxia. Altogether 42 automatically computed amplitude- and synchrony-related EEG features were extracted as 2-hourly average at very early (6 h) and early (24 h) postnatal age; they were correlated to the severity of HIE in all infants, and to four clinical outcomes available in a subcohort of 40 newborns: time to full oral feeding (nasogastric tube NGT), neonatal brain MRI, Hammersmith Infant Neurological Examination (HINE) at three months, and Griffiths Scales at two years.

RESULTS

At 6 h, altogether 14 (33%) EEG features correlated significantly to the HIE grade ([r]= 0.39-0.61, p < 0.05), and one feature correlated to NGT ([r]= 0.50). At 24 h, altogether 13 (31%) EEG features correlated significantly to the HIE grade ([r]= 0.39-0.56), six features correlated to NGT ([r]= 0.36-0.49) and HINE ([r]= 0.39-0.61), while no features correlated to MRI or Griffiths Scales.

CONCLUSIONS

Our results show that the automatically computed measures of early cortical activity may provide outcome biomarkers for clinical and research purposes.

IMPACT

The early EEG background and its recovery after perinatal asphyxia reflect initial severity of encephalopathy and its clinical recovery, respectively. Computational EEG features from the early hours of life show robust correlations to HIE grades and to early clinical outcomes. Computational EEG features may have potential to be used as cortical activity biomarkers in early hours after perinatal asphyxia.

Detection of neurologic changes in critically ill infants using deep learning on video data: a retrospective single center cohort study

Background

Infant alertness and neurologic changes can reflect life-threatening pathology but are assessed by physical exam, which can be intermittent and subjective. Reliable, continuous methods are needed. We hypothesized that our computer vision method to track movement, pose artificial intelligence (AI), could predict neurologic changes in the neonatal intensive care unit (NICU).

Methods

We collected video data linked to electroencephalograms (video-EEG) from infants with corrected age less than 1 year at Mount Sinai Hospital in New York City, a level four urban NICU between February 1, 2021 and December 31, 2022. We trained a deep learning pose recognition algorithm on video feeds, labeling 14 anatomic landmarks in 25 frames/infant. We then trained classifiers on anatomic landmarks to predict cerebral dysfunction, diagnosed from EEG readings by an epileptologist, and sedation, defined by the administration of sedative medications.

Findings

We built the largest video-EEG dataset to date (282,301 video minutes, 115 infants) sampled from a diverse patient population. Infant pose was accurately predicted in cross-validation, held-out frames, and held-out infants with respective receiver operating characteristic area under the curves (ROC-AUCs) 0.94, 0.83, 0.89. Median movement increased with age and, after accounting for age, was lower with sedative medications and in infants with cerebral dysfunction (all P < 5 × 10-3, 10,000 permutations). Sedation prediction had high performance on cross-validation, held-out intervals, and held-out infants (ROC-AUCs 0.90, 0.91, 0.87), as did prediction of cerebral dysfunction (ROC-AUCs 0.91, 0.90, 0.76).

Interpretation

We show that pose AI can be applied in an ICU setting and that an EEG diagnosis, cerebral dysfunction, can be predicted from video data alone. Deep learning with pose AI may offer a scalable, minimally invasive method for neuro-telemetry in the NICU.

Funding

Friedman Brain Institute Fascitelli Scholar Junior Faculty Grant and Thrasher Research Fund Early Career Award (F.R.). The Clinical and Translational Science Awards (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences. Office of Research Infrastructure of the National Institutes of Health under award number S10OD026880 and S10OD030463.

Automated assessment of EEG background for neurodevelopmental prediction in neonatal encephalopathy

OBJECTIVE

Assess the capacity of brain state of the newborn (BSN) to predict neurodevelopment outcomes in neonatal encephalopathy.

METHODS

Trends of BSN, a deep learning-based measure translating EEG background to a continuous trend, were studied from a three-channel montage long-term EEG monitoring from a prospective cohort of 92 infants with neonatal encephalopathy and neurodevelopmental outcomes assessed by Bayley Scales of Infant Development, 3rd edition (Bayley-III) at 18 months. Outcome prediction used categories "Severe impairment" (Bayley-III composite score ≤70 or death) or "Any impairment" (score ≤85 or death).

RESULTS

"Severe impairment" was predicted best for motor outcomes (24 h area under the curve (AUC) = 0.97), followed by cognitive (36 h AUC = 0.90), overall (24 h AUC = 0.84), and language (24 h AUC = 0.82). "Any impairment" was best predicted for motor outcomes (12 h AUC = 0.95), followed by cognitive (24 h AUC = 0.85), overall (12 h AUC = 0.75), and language (12 and 24 h AUC = 0.68). Optimal BSN cutoffs for outcome predictions evolved with the postnatal age. Low BSN scores reached a 100% positive prediction of poor outcomes at 24 h of age.

INTERPRETATION

BSN is an excellent predictor of adverse neurodevelopmental outcomes in survivors of neonatal encephalopathy after therapeutic hypothermia, even at 24 h of life. The trend provides a fully automated, objective, quantified, and reliable interpretation of EEG background. The high temporal resolution supports continuous bedside brain assessment and early prognostication during the initial dynamic recovery phase.

Focused review on artificial intelligence for disease detection in infants

Over the last years, studies using artificial intelligence (AI) for the detection and prediction of diseases have increased and also concentrated more and more on vulnerable groups of individuals, such as infants. The release of ChatGPT demonstrated the potential of large language models (LLMs) and heralded a new era of AI with manifold application possibilities. However, the impact of this new technology on medical research cannot be fully estimated yet. In this work, we therefore aimed to summarise the most recent pre-ChatGPT developments in the field of automated detection and prediction of diseases and disease status in infants, i.e., within the first 12 months of life. For this, we systematically searched the scientific databases PubMed and IEEE Xplore for original articles published within the last five years preceding the release of ChatGPT (2018-2022). The search revealed 927 articles; a final number of 154 articles was included for review. First of all, we examined research activity over time. Then, we analysed the articles from 2022 for medical conditions, data types, tasks, AI approaches, and reported model performance. A clear trend of increasing research activity over time could be observed. The most recently published articles focused on medical conditions of twelve different ICD-11 categories; "certain conditions originating in the perinatal period" was the most frequently addressed disease category. AI models were trained with a variety of data types, among which clinical and demographic information and laboratory data were most frequently exploited. The most frequently performed tasks aimed to detect present diseases, followed by the prediction of diseases and disease status at a later point in development. Deep neural networks turned out as the most popular AI approach, even though traditional methods, such as random forests and support vector machines, still play a role-presumably due to their explainability or better suitability when the amount of data is limited. Finally, the reported performances in many of the reviewed articles suggest that AI has the potential to assist in diagnostic procedures for infants in the near future. LLMs will boost developments in this field in the upcoming years.

Revisiting the functional monitoring of brain development in premature neonates. A new direction in clinical care and research

The first 1000 days of life are of paramount importance for neonatal development. Premature newborns are exposed early to the external environment, modifying the fetal exposome and leading to overexposure in some sensory domains and deprivation in others. The resulting neurodevelopmental effects may persist throughout the individual's lifetime. Several neonatal neuromonitoring techniques can be used to investigate neural mechanisms in early postnatal development. EEG is the most widely used, as it is easy to perform, even at the patient's bedside. It is not expensive and provides information with a high temporal resolution and relatively good spatial resolution when performed in high-density mode. Functional near-infrared spectroscopy (fNIRS), a technique for monitoring vascular network dynamics, can also be used at the patient's bedside. It is not expensive and has a good spatial resolution at the cortical surface. These two techniques can be combined for simultaneous monitoring of the neuronal and vascular networks in premature newborns, providing insight into neurodevelopment before term. However, the extent to which more general conclusions about fetal development can be drawn from findings for premature neonates remains unclear due to considerable differences in environmental and medical situations. Fetal MEG (fMEG, as an alternative to EEG for preterm infants) and fMRI (as an alternative to fNIRS for preterm infants) can also be used to investigate fetal neurodevelopment on a trimester-specific basis. These techniques should be used for validation purposes as they are the only tools available for evaluating neuronal dysfunction in the fetus at the time of the gene-environment interactions influencing transient neuronal progenitor populations in brain structures. But what do these techniques tell us about early neurodevelopment? We address this question here, from two points of view. We first discuss spontaneous neural activity and its electromagnetic and hemodynamic correlates. We then explore the effects of stimulating the immature developing brain with information from exogenous sources, reviewing the available evidence concerning the characteristics of electromagnetic and hemodynamic responses. Once the characteristics of the correlates of neural dynamics have been determined, it will be essential to evaluate their possible modulation in the context of disease and in at-risk populations. Evidence can be collected with various neuroimaging techniques targeting both spontaneous and exogenously driven neural activity. A multimodal approach combining the neuromonitoring of different functional compartments (neuronal and vascular) is required to improve our understanding of the normal functioning and dysfunction of the brain and to identify neurobiomarkers for predicting the neurodevelopmental outcome of premature neonate and fetus. Such an approach would provide a framework for exploring early neurodevelopment, paving the way for the development of tools for earlier diagnosis in these vulnerable populations, thereby facilitating preventive, rescue and reparative neurotherapeutic interventions.

Advances in Electroencephalographic Biomarkers of Neonatal Hypoxic Ischemic Encephalopathy

Electroencephalography (EEG) is a key objective biomarker of newborn brain function, delivering critical, cotside insights to aid the management of encephalopathy. Access to continuous EEG is limited, forcing reliance on subjective clinical assessments. In hypoxia ischaemia, the primary cause of encephalopathy, alterations in EEG patterns correlate with. injury severity and evolution. As HIE evolves, causing secondary neuronal death, EEG can track injury progression, informing neuroprotective strategies, seizure management and prognosis. Despite its value, challenges with interpretation and lack of on site expertise has limited its broader adoption. Technological advances, particularly in digital EEG and machine learning, are enhancing real-time analysis. This will allow EEG to expand its role in HIE diagnosis, management and outcome prediction.

Free access via computational cloud to deep learning-based EEG assessment in neonatal hypoxic-ischemic encephalopathy: revolutionary opportunities to overcome health disparities

In this issue of Pediatric Research, Kota et al. evaluate a novel monitoring visual trend using deep-learning - Brain State of the Newborn (BSN)- based EEG as a bedside marker for severity of the encephalopathy in 46 neonates with hypoxic-ischemic encephalopathy (HIE) compared with healthy infants. Early BSN distinguished between normal and abnormal outcome, and correlated with the Total Sarnat Score.

Prognostic value of quantitative EEG in early hours of life for neonatal encephalopathy and neurodevelopmental outcomes

BACKGROUND

The ability to determine severity of encephalopathy is crucial for early neuroprotective therapies and for predicting neurodevelopmental outcome. The objective of this study was to assess a novel brain state of newborn (BSN) trend to distinguish newborns with presence of hypoxic ischemic encephalopathy (HIE) within hours after birth and predict neurodevelopmental outcomes at 2 years of age.

METHOD

This is a prospective cohort study of newborns at 36 weeks' gestation or later with and without HIE at birth. The Total Sanart Score (TSS) was calculated based on a modified Sarnat exam within 6 h of life. BSN was calculated from electroencephalogram (EEG) measurements initiated after birth. The primary outcome at 2 year of age was a diagnosis of death or disability using the Bayley Scales of Infant Development III.

RESULTS

BSN differentiated between normal and abnormal neurodevelopmental outcomes throughout the entire recording period from 6 h of life. Additionally, infants with lower BSN values had higher odds of neurodevelopmental impairment and HIE. BSN distinguished between normal (n = 86) and HIE (n = 46) and showed a significant correlation with the concomitant TSS.

CONCLUSION

BSN is a sensitive real-time marker for monitoring dynamic progression of encephalopathy and predicting neurodevelopmental impairment.

IMPACT

This is a prospective cohort study to investigate the ability of brain state of newborn (BSN) trend to predict neurodevelopmental outcome within the first day of life and identify severity of encephalopathy. BSN predicts neurodevelopmental outcomes at 2 years of age and the severity of encephalopathy severity. It also correlates with the Total Sarnat Score from the modified Sarnat exam. BSN could serve as a promising bedside trend aiding in accurate assessment and identification of newborns who may benefit from additional neuroprotection therapies.

Quantitative EEG and prediction of outcome in neonatal encephalopathy: a review

Electroencephalogram (EEG) is an important biomarker for neonatal encephalopathy (NE) and has significant predictive value for brain injury and neurodevelopmental outcomes. Quantitative analysis of EEG involves the representation of complex EEG data in an objective, reproducible and scalable manner. Quantitative EEG (qEEG) can be derived from both a limited channel EEG (as available during amplitude integrated EEG) and multi-channel conventional EEG. It has the potential to enable bedside clinicians to monitor and evaluate details of cortical function without the necessity of continuous expert input. This is particularly useful in NE, a dynamic and evolving condition. In these infants, continuous, detailed evaluation of cortical function at the bedside is a valuable aide to management especially in the current era of therapeutic hypothermia and possible upcoming neuroprotective therapies. This review discusses the role of qEEG in newborns with NE and its use in informing monitoring and therapy, along with its ability to predict imaging changes and short and long-term neurodevelopmental outcomes. IMPACT: Quantitative representation of EEG data brings the evaluation of continuous brain function, from the neurophysiology lab to the NICU bedside and has a potential role as a biomarker for neonatal encephalopathy. Clinical and research applications of quantitative EEG in the newborn are rapidly evolving and a wider understanding of its utility is valuable. This overview summarizes the role of quantitative EEG at different timepoints, its relevance to management and its predictive value for short- and long-term outcomes in neonatal encephalopathy.

Clinical outcome prediction with an automated EEG trend, Brain State of the Newborn, after perinatal asphyxia

OBJECTIVE

To evaluate the utility of a fully automated deep learning -based quantitative measure of EEG background, Brain State of the Newborn (BSN), for early prediction of clinical outcome at four years of age.

METHODS

The EEG monitoring data from eighty consecutive newborns was analyzed using the automatically computed BSN trend. BSN levels during the first days of life (a of total 5427 hours) were compared to four clinical outcome categories: favorable, cerebral palsy (CP), CP with epilepsy, and death. The time dependent changes in BSN-based prediction for different outcomes were assessed by positive/negative predictive value (PPV/NPV) and by estimating the area under the receiver operating characteristic curve (AUC).

RESULTS

The BSN values were closely aligned with four visually determined EEG categories (p < 0·001), as well as with respect to clinical milestones of EEG recovery in perinatal Hypoxic Ischemic Encephalopathy (HIE; p < 0·003). Favorable outcome was related to a rapid recovery of the BSN trend, while worse outcomes related to a slow BSN recovery. Outcome predictions with BSN were accurate from 6 to 48 hours of age: For the favorable outcome, the AUC ranged from 95 to 99% (peak at 12 hours), and for the poor outcome the AUC ranged from 96 to 99% (peak at 12 hours). The optimal BSN levels for each PPV/NPV estimate changed substantially during the first 48 hours, ranging from 20 to 80.

CONCLUSIONS

We show that the BSN provides an automated, objective, and continuous measure of brain activity in newborns.

SIGNIFICANCE

The BSN trend discloses the dynamic nature that exists in both cerebral recovery and outcome prediction, supports individualized patient care, rapid stratification and early prognosis.

Deep Learning Aided Neuroimaging and Brain Regulation

Currently, deep learning aided medical imaging is becoming the hot spot of AI frontier application and the future development trend of precision neuroscience. This review aimed to render comprehensive and informative insights into the recent progress of deep learning and its applications in medical imaging for brain monitoring and regulation. The article starts by providing an overview of the current methods for brain imaging, highlighting their limitations and introducing the potential benefits of using deep learning techniques to overcome these limitations. Then, we further delve into the details of deep learning, explaining the basic concepts and providing examples of how it can be used in medical imaging. One of the key strengths is its thorough discussion of the different types of deep learning models that can be used in medical imaging including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial network (GAN) assisted magnetic resonance imaging (MRI), positron emission tomography (PET)/computed tomography (CT), electroencephalography (EEG)/magnetoencephalography (MEG), optical imaging, and other imaging modalities. Overall, our review on deep learning aided medical imaging for brain monitoring and regulation provides a referrable glance for the intersection of deep learning aided neuroimaging and brain regulation.

Charting infants' motor development at home using a wearable system: validation and comparison to physical growth charts

BACKGROUND

Early neurodevelopmental care and research are in urgent need of practical methods for quantitative assessment of early motor development. Here, performance of a wearable system in early motor assessment was validated and compared to developmental tracking of physical growth charts.

METHODS

Altogether 1358 h of spontaneous movement during 226 recording sessions in 116 infants (age 4-19 months) were analysed using a multisensor wearable system. A deep learning-based automatic pipeline quantified categories of infants' postures and movements at a time scale of seconds. Results from an archived cohort (dataset 1, N = 55 infants) recorded under partial supervision were compared to a validation cohort (dataset 2, N = 61) recorded at infants' homes by the parents. Aggregated recording-level measures including developmental age prediction (DAP) were used for comparison between cohorts. The motor growth was also compared with respective DAP estimates based on physical growth data (length, weight, and head circumference) obtained from a large cohort (N = 17,838 infants; age 4-18 months).

FINDINGS

Age-specific distributions of posture and movement categories were highly similar between infant cohorts. The DAP scores correlated tightly with age, explaining 97-99% (94-99% CI 95) of the variance at the group average level, and 80-82% (72-88%) of the variance in the individual recordings. Both the average motor and the physical growth measures showed a very strong fit to their respective developmental models (R² = 0.99). However, single measurements showed more modality-dependent variation that was lowest for motor (σ = 1.4 [1.3-1.5 CI 95] months), length (σ = 1.5 months), and combined physical (σ = 1.5 months) measurements, and it was clearly higher for the weight (σ = 1.9 months) and head circumference (σ = 1.9 months) measurements. Longitudinal tracking showed clear individual trajectories, and its accuracy was comparable between motor and physical measures with longer measurement intervals.

INTERPRETATION

A quantified, transparent and explainable assessment of infants' motor performance is possible with a fully automated analysis pipeline, and the results replicate across independent cohorts from out-of-hospital recordings. A holistic assessment of motor development provides an accuracy that is comparable with the conventional physical growth measures. A quantitative measure of infants' motor development may directly support individual diagnostics and care, as well as facilitate clinical research as an outcome measure in early intervention trials.

FUNDING

This work was supported by the Finnish Academy (314602, 335788, 335872, 332017, 343498), Finnish Pediatric Foundation (Lastentautiensäätiö), Aivosäätiö, Sigrid Jusélius Foundation, and HUS Children's Hospital/HUS diagnostic center research funds.