Early Ultrasonic Monitoring of Brain Growth and Later Neurodevelopmental Outcome in Very Preterm Infants.

BACKGROUND AND PURPOSE: In infants born very preterm, monitoring of early brain growth could contribute to prediction of later neurodevelopment. Therefore, our aim was to investigate associations between 2 early cranial ultrasound markers (corpus callosum-fastigium and corpus callosum length) and neurodevelopmental outcome and the added value of both markers in the prediction of neurodevelopmental outcome based on neonatal risk factors and head circumference in very preterm infants. MATERIALS AND METHODS: This prospective observational study included 225 infants born at <30 weeks' gestational age, of whom 153 were without any brain injury on cranial ultrasound. Corpus callosum-fastigium and corpus callosum length and head circumference were measured at birth, 29 weeks' gestational age, transfer from the neonatal intensive care unit to a level II hospital, and 2 months' corrected age. We analyzed associations of brain markers and their growth with cognitive, motor, language, and behavioral outcome at 2 years' corrected age. RESULTS: In infants without brain injury, greater corpus callosum-fastigium length at 2 months was associated with better cognitive outcome. Corpus callosum length at 2 months was positively associated with cognitive, motor, and language outcome. Faster growth of the corpus callosum length between birth and 2 months was associated with better cognitive and motor function. Prediction of neurodevelopmental outcome based on neonatal risk factors with or without head circumference was significantly improved by adding corpus callosum length. CONCLUSIONS: Both corpus callosum-fastigium and corpus callosum length on cranial ultrasound are associated with neurodevelopmental outcome of very preterm infants without brain injury at 2 years, but only corpus callosum length shows the added clinical utility in predicting neurodevelopmental outcome.

Perinatal risk factors for visuospatial attention and processing dysfunctions at 1 year of age in children born between 26 and 32 weeks.

BACKGROUND: Children born preterm are at risk of visuospatial attention orienting and processing dysfunctions, which can be quantified early in life using visually-guided eye movement responses. AIMS: To identify the prevalence and perinatal risk factors for visuospatial attention orienting and processing dysfunctions in children born preterm of 1 year of corrected age (CA). STUDY DESIGN: 123 children born between 26 and 33 weeks of gestation underwent a nonverbal visuospatial test at 1y CA, using an eye tracking-based paradigm. For the detected high-salient (cartoon and contrast), intermediate-salient (form and motion) and low-salient (color) stimuli, we quantified the reaction time to fixation (RTF). RTFs were compared to normative references from an age-matched control group (N = 38). The prevalence of perinatal risk factors (gestational age and weight, indices of neurological damage, overal sickness, respiratory failure, and retinopathy) was compared between the groups with normal and delayed RTFs. RESULTS: At 1y CA, the preterm group had 7-20% less detected stimuli than the control group, particularly for intermediate and low-salient stimuli. Compared to normative RTFs, modest delays were found for high-salient cartoon (in 19% of preterm children) and contrast (8%), intermediate-salient motion (23%) and form (21%), and low-salient color stimuli (8%). These children had a significantly higher prevalence of perinatal risk factors for respiratory failure and intraventricular hemorrhages. CONCLUSIONS: Children born between 26 and 32 weeks have a modest risk (8-23%) of visuospatial attention and processing dysfunction. This warrants early monitoring and support of general visual development in preterm children at risk of respiratory distress and disrupted cerebral blood flow.

[Two-year follow-up of infants born at 24 weeks gestation; first outcomes following implementation of the new 'Guideline for perinatal policy in cases of extreme prematurity']. / Follow-up na 2 jaar van kinderen geboren bij 24 weken.

OBJECTIVE: Since 2010 the guideline 'Guideline for perinatal policy in cases of extreme prematurity' has advised an active policy in infants born at 24 weeks gestation. We investigated how infants born at 24 and 25 weeks gestation in the first year following the implementation of the guideline had developed by the age of 2 years. DESIGN: Retrospective national cohort study. METHOD: The study population consisted of all surviving infants born in the Netherlands at 24 or 25 weeks gestation in the period from 1 October 2010 to 1 October 2011. At a corrected age of 2 years the children underwent a general physical and neurological examination, and their cognitive scores were determined on the 'Bayley scales of infant and toddler development' (Bayley III). Examinations took place in the 10 neonatal intensive care units (NICU's) in the Netherlands. RESULTS: Of 185 extremely premature infants, 166 were admitted to a NICU. A total of 95 survived to a corrected age of 2 years; 78 (82%) children were examined. Their average cognitive score on the Bayley III scale was 88 (SD: 16). Among the children born at 24 weeks gestation, 20% had mild disabilities and 20% had moderate to severe disabilities. Among the children born at 25 weeks gestation, 17% had mild disabilities and 12% had moderate to severe disabilities. CONCLUSION: Of the children born at 24 weeks gestation in the first year after the introduction of active policy in the Netherlands and surviving to 2 years of age (46%), more than half had developed without disabilities. This was comparable to children born at 25 weeks gestation. Of all children born at 24 weeks gestation, 25% survived to 2 years of age without disabilities.

Improved multi-stage neonatal seizure detection using a heuristic classifier and a data-driven post-processor.

OBJECTIVE: After identifying the most seizure-relevant characteristics by a previously developed heuristic classifier, a data-driven post-processor using a novel set of features is applied to improve the performance. METHODS: The main characteristics of the outputs of the heuristic algorithm are extracted by five sets of features including synchronization, evolution, retention, segment, and signal features. Then, a support vector machine and a decision making layer remove the falsely detected segments. RESULTS: Four datasets including 71 neonates (1023h, 3493 seizures) recorded in two different university hospitals, are used to train and test the algorithm without removing the dubious seizures. The heuristic method resulted in a false alarm rate of 3.81 per hour and good detection rate of 88% on the entire test databases. The post-processor, effectively reduces the false alarm rate by 34% while the good detection rate decreases by 2%. CONCLUSION: This post-processing technique improves the performance of the heuristic algorithm. The structure of this post-processor is generic, improves our understanding of the core visually determined EEG features of neonatal seizures and is applicable for other neonatal seizure detectors. SIGNIFICANCE: The post-processor significantly decreases the false alarm rate at the expense of a small reduction of the good detection rate.

Automated artifact removal as preprocessing refines neonatal seizure detection.

OBJECTIVE: The description and evaluation of algorithms using Independent Component Analysis (ICA) for automatic removal of ECG, pulsation and respiration artifacts in neonatal EEG before automated seizure detection. METHODS: The developed algorithms decompose the EEG using ICA into its underlying sources. The artifact source was identified using the simultaneously recorded polygraphy signals after preprocessing. The EEG was reconstructed without the corrupting source, leading to a clean EEG. The impact of the artifact removal was measured by comparing the performance of a previously developed seizure detector before and after the artifact removal in 13 selected patients (9 having artifact-contaminated and 4 having artifact-free EEGs). RESULTS: A significant decrease in false alarms (p=0.01) was found while the Good Detection Rate (GDR) for seizures was not altered (p=0.50). CONCLUSIONS: The techniques reduced the number of false positive detections without lowering sensitivity and are beneficial in long term EEG seizure monitoring in the presence of disturbing biological artifacts. SIGNIFICANCE: The proposed algorithms improve neonatal seizure monitoring.

Validation of a new automated neonatal seizure detection system: a clinician's perspective.

OBJECTIVE: To validate an improved automated electroencephalography (EEG)-based neonatal seizure detection algorithm (NeoGuard) in an independent data set. METHODS: EEG background was classified into eight grades based on the evolution of discontinuity and presence of sleep-wake cycles. Patients were further sub-classified into two groups; gpI: mild to moderate (grades 1-5) and gpII: severe (grades 6-8) EEG background abnormalities. Seizures were categorised as definite and dubious. Seizure characteristics were compared between gpI and gpII. The algorithm was tested on 756 h of EEG data from 24 consecutive neonates (median 25 h per patient) with encephalopathy and recorded seizures during continuous monitoring (cEEG). No selection was made regarding the quality of EEG or presence of artefacts. RESULTS: Seizure amplitudes significantly decreased with worsening EEG background. Seizures were detected with a total sensitivity of 61.9% (1285/2077). The detected seizure burden was 66,244/97,574 s (67.9%). Sensitivity per patient was 65.9%, with a mean positive predictive value (PPV) of 73.7%. After excluding four patients with severely abnormal EEG background, and predominantly having dubious seizures, the algorithm showed a median sensitivity per patient of 86.9%, PPV of 89.5% and false positive rate of 0.28 h(-1). Sensitivity tended to be better for patients in gpI. CONCLUSIONS: The algorithm detects neonatal seizures well, has a good PPV and is suited for cEEG monitoring. Changes in electrographic characteristics such as amplitude, duration and rhythmicity in relation to deteriorating EEG background tend to worsen the performance of automated seizure detection. SIGNIFICANCE: cEEG monitoring is important for detecting seizures in the neonatal intensive care unit (NICU). Our automated algorithm reliably detects neonatal seizures that are likely to be clinically most relevant, as reflected by the associated EEG background abnormality.

Time varying neonatal seizure localization.

BACKGROUND: A common cause for damage to the neonatal brain is a shortage in the oxygen supply to the brain or asphyxia. Neonatal seizures are the most frequent manifestation of neonatal neurologic disorders. Multichannel EEG recordings allow topographic localization of seizure foci. OBJECTIVES: We want to objectively determine the spatial distribution of the seizure on the scalp, the location in time and order the dominant sources in the brain based on their strength. METHODS: In this paper we combine a method based on higher order CP-decomposition with subsequent singular value decomposition (SVD). RESULTS: We illustrate the abilities of the method on simulated as well as on real neonatal seizure EEG. CONCLUSIONS: The proposed method provides reliable time and spatial information about the seizure, gives a clear overview of what is going on in the EEG and allows easy interpretation.

Neonatal seizure localization using PARAFAC decomposition.

OBJECTIVE: The description and evaluation of two EEG-based algorithms for automatic and objective determination of the seizure location in the neonatal brain as it is reflected on the scalp. METHODS: Each algorithm extracts the electrical potential distribution of the seizure over the scalp using the higher-order canonical decomposition or Parallel Factor Analysis (PARAFAC), also referred to as the CP model. This model decomposes a tensor in a sum of rank-1 components. The two algorithms differ in the way the tensor is constructed and in the type of activity they are able to extract. While the first method extracts oscillatory seizure activity, the second extracts spike train activity. RESULTS: We compared the seizure localization results of 21 seizures from 6 neonates with post-asphyxial hypoxic ischemic encephalopathy, with that based on the visual analysis of the EEG by a clinical neurophysiologist. There was a good agreement between the two methods in the localization of seizure onset in all. CONCLUSION: The techniques presented in this paper are robust, objective methods to determine neonatal seizure localization. They can be a useful tool for neonatal EEG analysis and for continuous brain function monitoring. SIGNIFICANCE: The proposed algorithms significantly improve neonatal seizure localization and monitoring.

Automated neonatal seizure detection mimicking a human observer reading EEG.

OBJECTIVE: The description and evaluation of a novel patient-independent seizure detection for the EEG of the newborn term infant. METHODS: We identified characteristics of neonatal seizures by which a human observer is able to detect them. Neonatal seizures were divided into two types. For each type, a fully automated detection algorithm was developed based on the identified human observer characteristics. The first algorithm analyzes the correlation between high-energetic segments of the EEG. The second detects increases in low-frequency activity (<8 Hz) with high autocorrelation. RESULTS: The complete algorithm was tested on multi-channel EEG recordings of 21 patients with and 5 patients without electrographic seizures, totaling 217 h of EEG. Sensitivity of the combined algorithms was found to be 88%, Positive Predictive Value (PPV) 75% and the false positive rate 0.66 per hour. CONCLUSIONS: Our approach to separate neonatal seizures into two types yields a high sensitivity combined with a good PPV and much lower false positive rate than previously published algorithms. SIGNIFICANCE: The proposed algorithm significantly improves neonatal seizure detection and monitoring.

Heart rate changes are insensitive for detecting postasphyxial seizures in neonates.

We studied heart rate (HR) changes during 169 seizures (mean 12 per patient, range 8 to 18) in 14 neonates with severe birth asphyxia. HR changes were found in 21 seizures (12.4%) in eight patients (HR increases in four, decreases in one, and both patterns in three patients), suggesting the existence of neonatal cerebral hemispheric connections with brainstem autonomic regulatory centers. HR monitoring appears to be insensitive for detecting postasphyxial neonatal seizures.

[Green amniotic fluid as initial symptom of high intestinal obstruction in infants]. / Groen vruchtwater als eerste symptoom van hoge-darmobstructie bij het kind.

At the birth of two children the amniotic fluid was green colored. The Apgar scores were good. Because of bilious vomiting and food retention, respectively, an open stomach tube was inserted, out of which bilious stomach contains were drained. The cause of green amniotic fluid was not meconium production or infection with Listeria monocytogenes, but mixing with green bile. At further investigation the children both proved to have a high intestinal obstruction distal of the papilla duodeni major.

[Lichen sclerosus et atrophicus as a cause of genital lesions in a girl]. / Lichen sclerosus et atrophicus als oorzaak van genitale afwijkingen bij een meisje.

In a 10-year old girl with irritated vulva lichen sclerosus et atrophicus (LSA) was diagnosed. She was successfully treated with local application of equal amounts of a cortisol ointment and zinc oil. LSA is a disease of the skin infrequently seen in children, and then mostly in girls. Diagnosis is based on typical lesions in the anogenital region (sharply delineated ivory discoloration, moderate sclerosis and atrophy, haemorrhagic erosions). Knowledge of this disease is important in order to distinguish it from sexual abuse.