Neonatal seizure detection based on single-channel EEG: instrumentation and algorithms

Seizure activity in the perinatal period, which constitutes the most common neurological emergency in the neonate, can cause brain disorders later in life or even death depending on their severity. This issue remains unsolved to date, despite the several attempts in tackling it using numerous methods. Therefore, a method is still needed that can enable neonatal cerebral activity monitoring to identify those at risk. Currently, electroencephalography (EEG) and amplitude-integrated EEG (aEEG) have been exploited for the identification of seizures in neonates, however both lack automation. EEG and aEEG are mainly visually analysed, requiring a specific skill set and as a result the presence of an expert on a 24/7 basis, which is not feasible. Additionally, EEG devices employed in neonatal intensive care units (NICU) are mainly designed around adults, meaning that their design specifications are not neonate specific, including their size due to multi-channel requirement in adults - adults minimum requirement is ≥ 32 channels, while gold standard in neonatal is equal to 10; they are bulky and occupy significant space in NICU. This thesis addresses the challenge of reliably, efficiently and effectively detecting seizures in the neonatal brain in a fully automated manner. Two novel instruments and two novel neonatal seizure detection algorithms (SDAs) are presented. The first instrument, named PANACEA, is a high-performance, wireless, wearable and portable multi-instrument, able to record neonatal EEG, as well as a plethora of (bio)signals. This device despite its high-performance characteristics and ability to record EEG, is mostly suggested to be used for the concurrent monitoring of other vital biosignals, such as electrocardiogram (ECG) and respiration, which provide vital information about a neonate's medical condition. The two aforementioned biosignals constitute two of the most important artefacts in the EEG and their concurrent acquisition benefit the SDA by providing information to an artefact removal algorithm. The second instrument, called neoEEG Board, is an ultra-low noise, wireless, portable and high precision neonatal EEG recording instrument. It is able to detect and record minute signals (< 10 nVp) enabling cerebral activity monitoring even from lower layers in the cortex. The neoEEG Board accommodates 8 inputs each one equipped with a patent-pending tunable filter topology, which allows passband formation based on the application. Both the PANACEA and the neoEEG Board are able to host low- to middle-complexity SDAs and they can operate continuously for at least 8 hours on 3-AA batteries. Along with PANACEA and the neoEEG Board, two novel neonatal SDAs have been developed. The first one, termed G prime-smoothed (G ́_s), is an on-line, automated, patient-specific, single-feature and single-channel EEG based SDA. The G ́_s SDA, is enabled by the invention of a novel feature, termed G prime (G ́) and can be characterised as an energy operator. The trace that the G ́_s creates, can also be used as a visualisation tool because of its distinct change at a presence of a seizure. Finally, the second SDA is machine learning (ML)-based and uses numerous features and a support vector machine (SVM) classifier. It can be characterised as automated, on-line and patient-independent, and similarly to G ́_s it makes use of a single-channel EEG. The proposed neonatal SDA introduces the use of the Hilbert-Huang transforms (HHT) in the field of neonatal seizure detection. The HHT analyses the non-linear and non-stationary EEG signal providing information for the signal as it evolves. Through the use of HHT novel features, such as the per intrinsic mode function (IMF) (0-3 Hz) sub-band power, were also employed. Detection rates of this novel neonatal SDA is comparable to multi-channel SDAs.Open Acces

A Nonstationary Model of Newborn EEG

The detection of seizure in the newborn is a critical aspect of neurological research. Current automatic detection techniques are difficult to assess due to the problems associated with acquiring and labelling newborn electroencephalogram (EEG) data. A realistic model for newborn EEG would allow confident development, assessment and comparison of these detection techniques. This paper presents a model for newborn EEG that accounts for its self-similar and non-stationary nature. The model consists of background and seizure sub-models. The newborn EEG background model is based on the short-time power spectrum with a time-varying power law. The relationship between the fractal dimension and the power law of a power spectrum is utilized for accurate estimation of the short-time power law exponent. The newborn EEG seizure model is based on a well-known time-frequency signal model. This model addresses all significant time-frequency characteristics of newborn EEG seizure which include; multiple components or harmonics, piecewise linear instantaneous frequency laws and harmonic amplitude modulation. Estimates of the parameters of both models are shown to be random and are modelled using the data from a total of 500 background epochs and 204 seizure epochs. The newborn EEG background and seizure models are validated against real newborn EEG data using the correlation coefficient. The results show that the output of the proposed models has a higher correlation with real newborn EEG than currently accepted models (a 10% and 38% improvement for background and seizure models, respectively)

Temporal evolution of quantitative EEG within 3 days of birth in early preterm infants

For the premature newborn, little is known about changes in brain activity during transition to extra-uterine life. We aim to quantify these changes in relation to the longer-term maturation of the developing brain. We analysed EEG for up to 72 hours after birth from 28 infants bornPeer reviewe

Investigating and managing neonatal seizures in the UK: an explanatory sequential mixed methods approach

Background Neonatal seizures are difficult to diagnose and, when they are, tradition dictates first line treatment is phenobarbital. There is little data on how consultants diagnose neonatal seizures, choose when to treat or how they choose aetiological investigations or drug treatments. The purpose of this study was to assess the variation across the UK in the management of neonatal seizures and explore paediatricians’ views on their diagnosis and treatment. Methods An explanatory sequential mixed methods approach was used (QUAN→QUAL) with equal waiting between stages. We collected quantitative data from neonatology staff and paediatric neurologists using a questionnaire sent to neonatal units and via emails from the British Paediatric Neurology Association. We asked for copies of neonatal unit guidelines on the management of seizures. The data from questionnaires was used to identify16 consultants using semi-structured interviews. Thematic analysis was used to interpret qualitative data, which was triangulated with quantitative questionnaire data. Results One hundred questionnaires were returned: 47.7% thought levetiracetam was as, or equally, effective as phenobarbital; 9.2% thought it was less effective. 79.6% of clinicians had seen no side effects in neonates with levetiracetam. 97.8% of unit guidelines recommended phenobarbital first line, with wide variation in subsequent drug choice, aetiological investigations, and advice on when to start treatment. Thematic analysis revealed three themes: ‘Managing uncertainty with neonatal seizures’, ‘Moving practice forward’ and ‘Multidisciplinary team working’. Consultants noted collecting evidence on anti-convulsant drugs in neonates is problematic, and recommended a number of solutions, including collaboration to reach consensus guidelines, to reduce diagnostic and management uncertainty. Conclusions There is wide variation in the management of neonatal seizures and clinicians face many uncertainties. Our data has helped reveal some of the reasons for current practice and decision making. Suggestions to improve certainty include: educational initiatives to improve the ability of neonatal staff to describe suspicious events, greater use of video, closer working between neonatologists and neurologists, further research, and a national discussion to reach a consensus on a standardised approach to managing neonatal epileptic seizures

Ability of early neurological assessment and continuous EEG to predict long term neurodevelopmental outcome at 5 years in infants following hypoxic-ischaemic encephalopathy

Hypoxic-ischaemic encephalopathy (HIE) symptoms evolve during the first days of life and their monitoring is critical for treatment decisions and long-term outcome predictions. This thesis aims to report the five-year outcome of a HIE cohort born in the pre-therapeutic hypothermia era and to evaluate the predictive value of (a) neonatal neurological and EEG markers and (b) development in the first 24 months, for outcome. Methods: Participants were recruited at age five from two birth cohorts; HIE and Comparison. Repeated neonatal neurological assessments using the Amiel-TisonNeurological-Assessment-at-Term, continuous video EEG monitoring in the first 72 hours, and Sarnat grading at 24 hours were recorded. EEG severity grades were assigned at 6, 12 and 24 hours. Development was assessed in the HIE cohort at 6, 12 and 24 months using the Griffiths Mental Development (0-2) Revised Scales. At age five, intellectual (WPPSI-IIIUK scale), neuropsychological (NEPSY-II scales), neurological and ophthalmic testing was completed. Results: 5-year outcomes were available for 81.5% (n=53) of HIE and 71.4% (n=30) of Comparison cohorts. In HIE, 47.2% (27% mild, 47% moderate, 83% severe Sarnat), had non-intact outcome vs. 3.3% of the Comparison cohort. Non-intact outcome rates by 6-hour EEG-grade were: grade0=3%, grade1=25%, grade2=54%, grade3/4=79%. In HIE, processing speed (p=0.01) and verbal short-term memory (p=0.005) were below test norms. No significant differences were found in IQ, NEPSY-II or ocular biometry scores between children following mild and moderate HIE. Median IQ scores for mild (99(94-112),p=grade 2) at 24hours had superior positive predictive value (74%; AUROC(95%CI)=0.70(0.55-0.85) for non-intact 5-year outcome than abnormal EEG at 6 hours (68%; AUROC(95%CI)=0.71(0.56-0.87). Within-child development scores were inconsistent across the first 24 months. Although all children with intact 24-month Griffiths quotient (n=30) had intact 5-year IQ, 8/30 had non-intact overall outcome. Conclusion: Predictive value of neonatal neurological assessments and an EEG grading system for outcome was confirmed. Intact early childhood outcomes post-HIE may mask subtle adverse neuropsychological sequelae into the school years. This thesis supports emerging evidence that mild-grade HIE is not a benign condition and its inclusion in studies of neuroprotective treatments for HIE is warranted

Early development of sleep and brain functional connectivity in term-born and preterm infants

The proper development of sleep and sleep-wake rhythms during early neonatal life is crucial to lifelong neurological well-being. Recent data suggests that infants who have poor quality sleep demonstrate a risk for impaired neurocognitive outcomes. Sleep ontogenesis is a complex process, whereby alternations between rudimentary brain states-active vs. wake and active sleep vs. quiet sleep-mature during the last trimester of pregnancy. If the infant is born preterm, much of this process occurs in the neonatal intensive care unit, where environmental conditions might interfere with sleep. Functional brain connectivity (FC), which reflects the brain's ability to process and integrate information, may become impaired, with ensuing risks of compromised neurodevelopment. However, the specific mechanisms linking sleep ontogenesis to the emergence of FC are poorly understood and have received little investigation, mainly due to the challenges of studying causal links between developmental phenomena and assessing FC in newborn infants. Recent advancements in infant neuromonitoring and neuroimaging strategies will allow for the design of interventions to improve infant sleep quality and quantity. This review discusses how sleep and FC develop in early life, the dynamic relationship between sleep, preterm birth, and FC, and the challenges associated with understanding these processes. Impact Sleep in early life is essential for proper functional brain development, which is essential for the brain to integrate and process information. This process may be impaired in infants born preterm. The connection between preterm birth, early development of brain functional connectivity, and sleep is poorly understood. This review discusses how sleep and brain functional connectivity develop in early life, how these processes might become impaired, and the challenges associated with understanding these processes. Potential solutions to these challenges are presented to provide direction for future research.Peer reviewe

Scale-free bursting in human cortex following hypoxia at birth

The human brain is fragile in the face of oxygen deprivation. Even a briefinterruption of metabolic supply at birth challenges an otherwise healthy neonatal cortex, leading to a cascade of homeostatic responses. During recovery from hypoxia, cortical activity exhibits a period of highly irregular electrical fluctuations known as burst suppression. Here we show that these bursts have fractal properties, with power-law scaling of burst sizes across a remarkable 5 orders of magnitude and a scale-free relationship between burst sizes and durations. Although burst waveforms vary greatly, their average shape converges to a simple form that is asymmetric at long time scales. Using a simple computational model, we argue that this asymmetry reflects activity-dependent changes in the excitatory-inhibitory balance of cortical neurons. Bursts become more symmetric following the resumption of normal activity, with a corresponding reorganization of burst scaling relationships. These findings place burst suppression in the broad class of scale-free physical processes termed crackling noise and suggest that the resumption of healthy activity reflects a fundamental reorganization in the relationship between neuronal activity and its underlying metabolic constraints