Elena Pavlidis

Neonatal status epilepticus: Differences between preterm and term newborns

BACKGROUND Despite the many studies on neonatal seizures, neonatal status epilepticus (NSE) remains a controversial entity, with no general consensus about its definition. We report the characteristics of newborns with NSE in order to assess whether they showed homogeneous features or displayed clinical and/or instrumental differences depending on gestational age (GA). Preterm and term neonates were compared and risk factors for adverse outcome evaluated. METHODS From 154 newborns with video-EEG confirmed neonatal seizures admitted to the NICU of Parma University Hospital between January 1999 and December 2012, we collected a cohort of 47 newborns (19 preterm, 28 full-term) with NSE. NSE was defined as continuous seizure activity for at least 30 min or recurrent seizures lasting a total of 30 min without definite return to the baseline neurologic condition between seizures. Outcome was assessed at least at one year. We applied the χ(2) test to compare nominal data, and multivariate logistic regression analysis to determine independent risk factors for adverse outcome. RESULTS Only Apgar scores and neurologic examination (p ≤ .02) were different between the groups. None of the preterm newborns had a favourable outcome compared to 25% of the full-term ones (p = .032). Moreover, 52.6% of preterm neonates died compared to 17.8% of the full-term newborns (p = .01; OR = 5.11). The only variable related to outcome was Apgar score at 5 min (p = .02). CONCLUSION Newborns with NSE represented a quite homogeneous group regardless of the GA. Outcome was unfavourable in most of the subjects; however adverse outcome and death were more represented in preterm newborns.

Real-time automated detection of clonic seizures in newborns

OBJECTIVE The aim of this study is to apply a real-time algorithm for clonic neonatal seizures detection, based on a low complexity image processing approach extracting the differential average luminance from videotaped body movements. METHODS 23 video-EEGs from 12 patients containing 78 electrographically confirmed neonatal seizures of clonic type were reviewed and all movements were divided into noise, random movements, clonic seizures or other seizure types. Six video-EEGs from 5 newborns without seizures were also reviewed. Videos were then separately analyzed using either single, double or triple windows (these latter with 50% overlap) each of a 10s duration. RESULTS With a decision threshold set at 0.5, we obtained a sensitivity of 71% (corresponding specificity: 69%) with double-window processing for clonic seizures diagnosis. The discriminatory power, indicated by the Area Under the Curve (AUC), is higher with two interlaced windows (AUC=0.796) than with single (AUC=0.788) or triple-window (AUC=0.728). Among subjects without neonatal seizures, our algorithm showed a specificity of 91% with double-window processing. CONCLUSIONS Our algorithm reliably detects neonatal clonic seizures and differentiates them from either noise, random movements and other seizure types. SIGNIFICANCE It could represent a low-cost, low complexity, real-time automated screening tool for clonic neonatal seizures.

Epileptic features in Cornelia de Lange syndrome: Case report and literature review

INTRODUCTION Cornelia de Lange syndrome is a rare genetic disease, caused by mutations in three known different genes: NIBPL (crom 5p), SMC1A (crom X) and SMC3 (crom 10q), that account for about 65% of cases. This syndrome is characterized by distinctive facial features, psychomotor delay, growth retardation since the prenatal period (second trimester of pregnancy), hands and feet abnormalities, and involvement of other organs/systems. SMC1A and SMC3 mutations are responsible for a mild phenotype of the syndrome. METHODS We report the electroclinical features of epilepsy in a child with a mild Cornelia de Lange syndrome and furthermore we reviewed the descriptions of the epileptic findings available in the literature in patients with such syndrome. RESULTS A large heterogeneity of the epileptic findings in the literature is reported. CONCLUSION The presence of epilepsy could be related to pathophysiological factors independent of those implicated in the characterization of main classical phenotypic features. A more detailed description of the epileptic findings could help clinicians in the diagnosis of this syndrome in those cases lacking of the typical features.

A wire-free, non-invasive, low-cost video processing-based approach to neonatal apnoea detection

In this paper, we present a non-invasive, low-cost, wire-free video processing-based approach to neonatal apnoea detection. Our method consists in evaluating the presence or absence of apnoea events through an innovative analysis of a motion signal extracted from a video live capturing or recording of a patient. In particular, we pre-process the video by a recently proposed selective magnification algorithm, which has the purpose of emphasizing respiratory movements. Subsequently, by relying on a motion detection method based on the difference of consecutive frames, we extract a signal representative of the “quantity” of movement. Then, since breathing is characterized by periodic movements of specific body parts (e.g., the chest), using the Maximum Likelihood (ML) criterion we detect the presence or absence of a periodic component in the motion signal, so that the presence/absence of the respiratory movements and, therefore, of apnoea episodes can be inferred. Our method is tested on a newborn with recurrent apnoea events, affected by Congenital Central Hypoventilation Syndrome (CCHS). With the proposed method, we can identify 90-100% of the apnoea events detected by polysomnography, depending on the acceptable detection delay. The results, although preliminary, are thus very promising and show that apnoea events can be identified with non-invasive, low-cost, wire-free devices.

Neonatal seizures therapy: we are still looking for the efficacious drug

Therapeutic options currently available for neonatal seizures are still unsatisfactory both in terms of efficacy and of risk for long-term neurotoxicity, even if there is growing recognition of their potential to worsen neurodevelopmental outcome. A recent paper by Slaughter and colleagues entitled “Pharmacological treatment of neonatal seizures: a systematic review” has been published with the aim to provide a treatment algorithm, but, due to the relative paucity of clinical studies, it relies mainly on traditional antiepileptic drugs and does not distinguish between different neonatal populations, especially preterm and hypothermic neonates, who might require a dedicated approach in order to improve seizure control and reduce side effects.

Monitoring infants by automatic video processing

A unified approach to contact-less and low-cost video processing for automatic detection of neonatal diseases characterized by specific movement patterns is presented. This disease category includes neonatal clonic seizures and apneas. Both disorders are characterized by the presence or absence, respectively, of periodic movements of parts of the body-e.g., the limbs in case of clonic seizures and the chest/abdomen in case of apneas. Therefore, one can analyze the data obtained from multiple video sensors placed around a patient, extracting relevant motion signals and estimating, using the Maximum Likelihood (ML) criterion, their possible periodicity. This approach is very versatile and allows to investigate various scenarios, including: a single Red, Green and Blue (RGB) camera, an RGB-depth sensor or a network of a few RGB cameras. Data fusion principles are considered to aggregate the signals from multiple sensors. In the case of apneas, since breathing movements are subtle, the video can be pre-processed by a recently proposed algorithm which is able to emphasize small movements. The performance of the proposed contact-less detection algorithms is assessed, considering real video recordings of newborns, in terms of sensitivity, specificity, and Receiver Operating Characteristic (ROC) curves, with respect to medical gold standard devices. The obtained results show that a video processing-based system can effectively detect the considered specific diseases, with increasing performance for increasing number of sensors.

Predicting 2-y outcome in preterm infants using early multimodal physiological monitoring

Background:Preterm infants are at risk of adverse outcome. The aim of this study is to develop a multimodal model, including physiological signals from the first days of life, to predict 2-y outcome in preterm infants.Methods:Infants <32 wk gestation had simultaneous multi-channel electroencephalography (EEG), peripheral oxygen saturation (SpO2), and heart rate (HR) monitoring. EEG grades were combined with gestational age (GA) and quantitative features of HR and SpO2 in a logistic regression model to predict outcome. Bayley Scales of Infant Development-III assessed 2-y neurodevelopmental outcome. A clinical course score, grading infants at discharge as high or low morbidity risk, was used to compare performance with the model.Results:Forty-three infants were included: 27 had good outcomes, 16 had poor outcomes or died. While performance of the model was similar to the clinical course score graded at discharge, with an area under the receiver operator characteristic (AUC) of 0.83 (95% confidence intervals (CI): 0.69–0.95) vs. 0.79 (0.66–0.90) (P = 0.633), the model was able to predict 2-y outcome days after birth.Conclusion:Quantitative analysis of physiological signals, combined with GA and graded EEG, shows potential for predicting mortality or delayed neurodevelopment at 2 y of age.

Children's Headache: Drawings in the Diagnostic Work Up.

OBJECTIVES This study aims to evaluate the drawings effectiveness in childhood headache assessment. BACKGROUND Headache is a common cause of pain in children. Although drawings have been used in childhood to recognize psychological insights and pain perception, they were rarely used for headache characterization. METHODS We collected drawings from 67 subjects with cephalalgia during a 22-month timeframe. The clinical diagnosis was made according to the 2nd edition of The International Headache Classification. Drawings were independently categorized as migraine or tension-type headache (TTH) by two child neuropsychiatrists blinded to the clinical data. Cohen kappa for interrater agreement, sensitivity, specificity, and positive predictive value (PPV) were calculated. Subjects were also divided into three age groups to assess the influence of age. Finally, a control group of 90 subjects was collected and K-means cluster analysis was performed. RESULTS The drawings had a sensitivity of 85.71 and 81.48%, a specificity of 81.48 and 85.71%, and a PPV of 85.71 and 81.48%, for migraine and TTH diagnosis, respectively. Drawings by the older age group showed the highest predictability degree. Finally, by mean of cluster analysis, 59 of the 67 patients were correctly classified, whereas control subjects were similarly distributed between the two clusters. CONCLUSIONS Drawings are a useful instrument for migraine and TTH differential diagnosis. Thus, we suggest their inclusion in childhood headache diagnostic assessment.

The role of electroencephalogram in neonatal seizure detection

ABSTRACT Introduction: The role of EEG in neonatal seizure detection is well-established, being the multichannel video-EEG the gold standard. However, in the clinical practice often amplitude integrated EEG (aEEG) is used, in order to overcome the difficulties related to EEG use. Areas covered: An overview regarding neonatal seizures, current tools used to detect these (multichannel EEG versus aEEG) with respective strenghts and limitations, and some tools that can implement the use of multichannel EEG in the NICU. Expert commentary: Multichannel video-EEG is still a gold standard for seizure detection. Indeed, this tool allows to avoid both underestimation of neonatal seizure incidence and overtreatment with anticonvulsant drugs. Furthermore, it has to be acknowledged that multichannel video-EEG monitoring is not limited to the only seizure detection, providing also the information needed for a more accurate assessment of the background activity and some specific waves/pattern and features indicative of the brain development.

EEG - A Valuable Biomarker of Brain Injury in Preterm Infants

This review focuses on the role of electroencephalography (EEG) in monitoring abnormalities of preterm brain function. EEG features of the most common developmental brain injuries in preterm infants, including intraventricular haemorrhage, periventricular leukomalacia, and perinatal asphyxia, are described. We outline the most common EEG biomarkers associated with these injuries, namely seizures, positive rolandic sharp waves, EEG suppression/increased interburst intervals, mechanical delta brush activity, and other deformed EEG waveforms, asymmetries, and asynchronies. The increasing survival rate of preterm infants, in particular those that are very and extremely preterm, has led to a growing demand for a specific and shared characterization of the patterns related to adverse outcome in this unique population. This review includes abundant high-quality images of the EEG patterns seen in premature infants and will provide a valuable resource for everyone working in developmental neuroscience.