Decomposition of a multiscale entropy tensor for sleep stage identification in preterm infants

Established sleep cycling is one of the main hallmarks of early brain development in preterm infants, therefore, automated classification of the sleep stages in preterm infants can be used to assess the neonate's cerebral maturation. Tensor algebra is a powerful tool to analyze multidimensional data and has proven successful in many applications. In this paper, a novel unsupervised algorithm to identify neonatal sleep stages based on the decomposition of a multiscale entropy tensor is presented. The method relies on the difference in electroencephalography(EEG) complexity between the neonatal sleep stages and is evaluated on a dataset of 97 EEG recordings. An average sensitivity, specificity, accuracy and area under the receiver operating characteristic curve of 0.80, 0.79, 0.79 and 0.87 was obtained if the rank of the tensor decomposition is selected based on the age of the infant. © 2019 by the authors

Measurement of neurovascular coupling in neonates

Neurovascular coupling refers to the mechanism that links the transient neural activity to the subsequent change in cerebral blood flow, which is regulated by both chemical signals and mechanical effects. Recent studies suggest that neurovascular coupling in neonates and preterm born infants is different compared to adults. The hemodynamic response after a stimulus is later and less pronounced and the stimulus might even result in a negative (hypoxic) signal. In addition, studies both in animals and neonates confirm the presence of a short hypoxic period after a stimulus in preterm infants. In clinical practice, different methodologies exist to study neurovascular coupling. The combination of functional magnetic resonance imaging or functional near-infrared spectroscopy (brain hemodynamics) with EEG (brain function) is most commonly used in neonates. Especially near-infrared spectroscopy is of interest, since it is a non-invasive method that can be integrated easily in clinical care and is able to provide results concerning longer periods of time. Therefore, near-infrared spectroscopy can be used to develop a continuous non-invasive measurement system, that could be used to study neonates in different clinical settings, or neonates with different pathologies. The main challenge for the development of a continuous marker for neurovascular coupling is how the coupling between the signals can be described. In practice, a wide range of signal interaction measures exist. Moreover, biomedical signals often operate on different time scales. In a more general setting, other variables also have to be taken into account, such as oxygen saturation, carbon dioxide and blood pressure in order to describe neurovascular coupling in a concise manner. Recently, new mathematical techniques were developed to give an answer to these questions. This review discusses these recent developments. © 2019 Hendrikx, Smits, Lavanga, De Wel, Thewissen, Jansen, Caicedo, Van Huffel and Naulaers. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms

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

Detection of cerebral autoregulation by near-infrared spectroscopy in neonates: performance analysis of measurement methods

Cerebral Autoregulation, in clinical practice, is assessed by means of correlation or coherence analysis between mean arterial blood pressure (MABP) and cerebral blood flow (CBF). However, even though there is evidence linking cerebral autoregulation assessment with clinical outcome in preterm infants, available methods lack precision for clinical use. Classical methods, used for cerebral autoregulation, are influenced by the choice of parameters such as the length of the epoch under analysis and the choice of suitable frequency bands. The influence of these parameters, in the derived measurements for cerebral autoregulation, has not yet been evaluated. In this study, cerebral autoregulation was assessed using correlation, coherence, a modified version of coherence and transfer function gain, and phase. The influence of the extra-parameters on the final scores was evaluated by means of sensitivity analysis. The methods were applied to a database of 18 neonates with measurements of MABP and tissue oxygenation index (TOI). TOI reflects changes in CBF and was measured by means of near-infrared spectroscopy

Measurement of Neurovascular Coupling in Neonates

Neurovascular coupling refers to the mechanism that links the transient neural activity to the subsequent change in cerebral blood flow, which is regulated by both chemical signals and mechanical effects. Recent studies suggest that neurovascular coupling in neonates and preterm born infants is different compared to adults. The hemodynamic response after a stimulus is later and less pronounced and the stimulus might even result in a negative (hypoxic) signal. In addition, studies both in animals and neonates confirm the presence of a short hypoxic period after a stimulus in preterm infants. In clinical practice, different methodologies exist to study neurovascular coupling. The combination of functional magnetic resonance imaging or functional near-infrared spectroscopy (brain hemodynamics) with EEG (brain function) is most commonly used in neonates. Especially near-infrared spectroscopy is of interest, since it is a non-invasive method that can be integrated easily in clinical care and is able to provide results concerning longer periods of time. Therefore, near-infrared spectroscopy can be used to develop a continuous non-invasive measurement system, that could be used to study neonates in different clinical settings, or neonates with different pathologies. The main challenge for the development of a continuous marker for neurovascular coupling is how the coupling between the signals can be described. In practice, a wide range of signal interaction measures exist. Moreover, biomedical signals often operate on different time scales. In a more general setting, other variables also have to be taken into account, such as oxygen saturation, carbon dioxide and blood pressure in order to describe neurovascular coupling in a concise manner. Recently, new mathematical techniques were developed to give an answer to these questions. This review discusses these recent developments

Neonatal Seizure Detection Using Deep Convolutional Neural Networks

Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method

Objective differentiation of neonatal EEG background grades using detrended fluctuation analysis

A quantitative and objective assessment of background electroencephalograph (EEG) in sick neonates remains an everyday clinical challenge. We studied whether long range temporal correlations quantified by detrended fluctuation analysis (DFA) could be used in the neonatal EEG to distinguish different grades of abnormality in the background EEG activity. Long-term EEG records of 34 neonates were collected after perinatal asphyxia, and their background was scored in 1 h epochs (8 h in each neonate) as mild, moderate or severe. We applied DFA on 15 min long, non-overlapping EEG epochs (n = 1088) filtered from 3 to 8 Hz. Our formal feasibility study suggested that DFA exponent can be reliably assessed in only part of the EEG epochs, and in only relatively short time scales (10-60 s), while it becomes ambiguous if longer time scales are considered. This prompted further exploration whether paradigm used for quantifying multifractal DFA (MF-DFA) could be applied in a more efficient way, and whether metrics from MF-DFA paradigm could yield useful benchmark with existing clinical EEG gradings. Comparison of MF-DFA metrics showed a significant difference between three visually assessed background EEG grades. MF-DFA parameters were also significantly correlated to interburst intervals quantified with our previously developed automated detector. Finally, we piloted a monitoring application of MF-DFA metrics and showed their evolution during patient recovery from asphyxia. Our exploratory study showed that neonatal EEG can be quantified using multifractal metrics, which might offer a suitable parameter to quantify the grade of EEG background, or to monitor changes in brain state that take place during long-term brain monitoring.Peer reviewe

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

Peer reviewe

Neonatal factors predictive for respiratory and gastro-intestinal morbidity after esophageal atresia repair

Background: Esophageal atresia is a major congenital foregut anomaly. Affected patients often suffer from respiratory and gastro-intestinal morbidity. The objective of this study is to identify possible neonatal predictive factors contributing to a long-term complicated clinical course in patients after repair of esophageal atresia. Methods: A total of 93 patients born between 1993 and 2013, with esophageal atresia and surviving the neonatal period were included in this retrospective study. A complicated clinical course was defined as the occurrence of 1 of these complications: severe gastroesophageal reflux, esophageal stricture requiring dilatations, need for tube feeding for >100 days, severe tracheomalacia, severe chronic respiratory disease and death. We used linear models with a binomial distribution to determine risk factors for gastro-intestinal or respiratory complicated evolution and a backward stepwise elimination procedure to reduce models until only significant variables remained in the model. Multinomial logistic regression was used to assess risk factors for different evolutions of complication. Model parameter estimates were used to calculate odds ratios for significant risk factors. Results: Fifty-seven patients (61%) had a complicated clinical course in the first year of life and 47 (51%) had a complicated evolution during years 1e6. In the first year, prematurity was a significant factor for complicated gastro-intestinal (OR 2.84) and respiratory evolution (OR 2.93). After 1 year, gastro-intestinal morbidity in childhood was associated with VACTERL association (OR 12.2) and a complicated first year (OR 36.1). Respiratory morbidity was associated with congenital heart disease (OR 12.9) and a complicated first year (OR 86.9). Multinomial logistic regression showed that premat