Geraldine B. Boylan

Defining the gap between electrographic seizure burden, clinical expression and staff recognition of neonatal seizures

Background: Neonatal seizures are often subclinical, making accurate diagnosis difficult. Objective: To describe the clinical manifestations of electrographic seizures recorded on continuous video-EEG, and to compare this description with the recognition of clinical seizures by experienced neonatal staff. Methods: Term infants, at risk of seizures, were monitored by continuous 12-channel video-EEG from <6 hours of birth for up to 72 hours. All clinical seizures were recorded by experienced neonatal staff on individual seizure charts. Video-EEG recordings were subsequently analysed. The number, duration and clinical expression of electrographic seizures were calculated (in seconds), and compared with the seizures clinically suspected by the neonatal staff. Results: Of 51 infants enrolled, nine had electrographic seizures. A further three had clinically suspected seizures, without associated electrographic abnormality. Of the total 526 electrographic seizures, 179 (34%) had clinical manifestations evident on the simultaneous video recording. The clinical seizure activity corresponded to 18.8% of the total electrographic seizure burden. Overdiagnosis also occurred frequently. Of the 177 clinically suspected seizure episodes documented by staff, 48 (27%) had corresponding electrographic evidence of seizure activity Thus, only 9% (48/526) of electrographic seizures were accompanied by clinical manifestations, which were identified and documented by neonatal staff. Conclusion: Only one-third of neonatal EEG seizures displays clinical signs on simultaneous video recordings. Moreover, two-thirds of these clinical manifestations are unrecognised, or misinterpreted by experienced neonatal staff. In the recognition and management of neonatal seizures clinical diagnosis alone is not enough.

Non-expert use of the cerebral function monitor for neonatal seizure detection

Background: The cerebral function monitor (CFM) is widely used to detect neonatal seizures, but there are very few studies comparing it with simultaneous electroencephalography (EEG). Objective: To determine the accuracy of non-expert use of the CFM and to assess interobserver agreement of CFM seizure detection. Patients: Babies admitted to the neonatal intensive care unit at King’s College Hospital who were at high risk of seizure and had video-EEG monitoring. Methods: Video-EEG was used to detect seizures. Each baby had CFM recordings at speeds of 6, 15, and 30 cm/h during the EEG. Four neonatologists, trained in CFM seizure recognition, independently rated one hour CFM samples at three speeds from each baby. Interobserver agreement was quantified using Cohen’s κ. Results: CFM traces from 19 babies with EEG seizures and 21 babies without EEG seizures were analysed. Overall non-expert interpretation of the CFM performed poorly as a seizure detector compared with simultaneous EEG (sensitivities 38% at 6 cm/h; 54% at 15 cm/h; 55% at 30 cm/h). Although babies with seizures were more likely to be correctly classified at higher speeds (p = 0.02), babies without seizures were also more likely to be misclassified (p < 0.001). Agreement between observers was not good at any speed (κ values from 0.01 to 0.39). The observers usually detected generalised seizures but often missed seizures that were focal, low amplitude, or lasted less than one minute. Conclusion: Approximately half of all neonatal seizures may be missed using CFM alone. Neonatal seizures need to be diagnosed, characterised, and quantified first using EEG. The CFM may then be useful for long term monitoring.

EEG-based neonatal seizure detection with Support Vector Machines.

Objective The study presents a multi-channel patient-independent neonatal seizure detection system based on the Support Vector Machine (SVM) classifier. Methods A machine learning algorithm (SVM) is used as a classifier to discriminate between seizure and non-seizure EEG epochs. Two post-processing steps are proposed to increase both the temporal precision and the robustness of the system. The resulting system is validated on a large clinical dataset of 267 h of EEG data from 17 full-term newborns with seizures. Results The performance of the system using event-based metrics is reported. The system showed the best up-to-date performance of a neonatal seizure detection system. The system was able to achieve an average good detection rate of ∼89% with one false seizure detection per hour, ∼96% with two false detections per hour, or ∼100% with four false detections per hour. An analysis of errors revealed sources of misclassification in terms of both missed seizures and false detections. Conclusions The results obtained with the proposed SVM-based seizure detection system allow for its practical application in neonatal intensive care units. Significance The proposed SVM-based seizure detection system can greatly assist clinical staff, in a neonatal intensive care unit, to interpret the EEG. The system allows control of the final decision by choosing different confidence levels which makes it flexible for clinical needs. The obtained results may provide a reference for future seizure detection systems.

Early EEG Findings in Hypoxic-Ischemic Encephalopathy Predict Outcomes at 2 Years

OBJECTIVE: We examined the evolution of electroencephalographic (EEG) changes after hypoxic injury. METHODS: Continuous, multichannel, video-EEG was recorded for term infants with hypoxic-ischemic encephalopathy, from <6 hours to 72 hours after delivery. One-hour segments at 6, 12, 24, and 48 hours of age of the EEG were analyzed visually, and neurologic outcome was assessed at 24 months. RESULTS: Forty-four infants completed neurodevelopmental follow-up. Of those, 20 (45%) had abnormal outcomes. The EEG grade assigned correlated significantly with outcome. EEG abnormalities improved with time, with the worst EEG grade seen on the earliest recording in all cases. The best predictive ability was seen at 6 hours of age (area under the receiver operator characteristic curve: 0.958 [95% confidence interval: 0.88–1.04]; P = .000). Normal/mildly abnormal EEG results at 6, 12, or 24 hours had 100% positive predictive values for normal outcomes and negative predictive values of 67% to 76%. By 48 hours, many of the EEG findings had improved significantly. This led to the positive predictive value of abnormal EEG results being greater at 48 hours (93%), with a concurrent negative predictive value of 71%. EEG features that were associated with abnormal outcomes were background amplitude of <30 μV, interburst interval of >30 seconds, electrographic seizures, and absence of sleep-wake cycling at 48 hours. CONCLUSIONS: Early EEG is a reliable predictor of outcome in HIE. A normal or mildly abnormal EEG results within 6 hours after birth were associated with normal neurodevelopmental outcomes at 24 months.

A comparison of quantitative EEG features for neonatal seizure detection

OBJECTIVE This study was undertaken to identify the best performing quantitative EEG features for neonatal seizures detection from a test set of 21. METHODS Each feature was evaluated on 1-min, artefact-free segments of seizure and non-seizure neonatal EEG recordings. The potential utility of each feature for neonatal seizure detection was determined using receiver operating characteristic analysis and repeated measures t-tests. A performance estimate of the feature set was obtained using a cross-fold validation and combining all features together into a linear discriminant classifier model. RESULTS Significant differences between seizure and non-seizure segments were found in 19 features for 17 patients. The best performing features for this application were the RMS amplitude, the line length and the number of local maxima and minima. An estimate of the patient independent classifier performance yielded a sensitivity of 81.08% and specificity of 82.23%. CONCLUSIONS The individual performances of 21 quantitative EEG features in detecting electrographic seizure in the neonate were compared and numerically quantified. Combining all features together into a classifier model led to superior performance than that provided by any individual feature taken alone. SIGNIFICANCE The results documented in this study may provide a reference for the optimum quantitative EEG features to use in developing and enhancing neonatal seizure detection algorithms.

Dynamic cerebral autoregulation in sick newborn infants

The sick newborn infant is vulnerable to brain injury and impaired cerebral autoregulation is thought to contribute to this. Coherent averaging is a method of measuring the dynamic cerebral autoregulatory response that is particularly suitable for neonates. We used this method in combination with a measure of the gradient of the cerebral blood flow velocity (CBFV) response following transient blood pressure (BP) peaks to study dynamic autoregulation in infants undergoing intensive care. Term and preterm infants at high risk of neurologic injury were compared with a control group of infants, also undergoing intensive care. Simultaneous video-EEG, CBFV (using transcranial Doppler), and arterial blood pressure measurements were obtained intermittently during a study period of at least 2 h. Cerebral autoregulatory response curves were constructed for high risk and control groups. Intact cerebral autoregulation produces a characteristic response consisting of a brief period when CBFV follows arterial blood pressure but quickly returns to baseline value. An impaired autoregulatory response shows CBFV mirroring the arterial blood pressure curve closely. Thirteen high-risk infants, who also had seizures (10 term and 3 preterm) and 12 control infants (6 term and 6 preterm) were studied. Autoregulation was absent in high-risk term and preterm infants. It was also absent in preterm control infants. Term, neurologically healthy infants undergoing intensive care have an intact autoregulatory response. The constant passive response seen in high-risk infants may reflect the severity of the underlying neurologic disease.

An evaluation of automated neonatal seizure detection methods

OBJECTIVE To evaluate 3 published automated algorithms for detecting seizures in neonatal EEG. METHODS One-minute, artifact-free EEG segments consisting of either EEG seizure activity or non-seizure EEG activity were extracted from EEG recordings of 13 neonates. Three published neonatal seizure detection algorithms were tested on each EEG recording. In an attempt to obtain improved detection rates, threshold values in each algorithm were manipulated and the actual algorithms were altered. RESULTS We tested 43 data files containing seizure activity and 34 data files free from seizure activity. The best results for Gotman, Liu and Celka, respectively, were as follows: sensitivities of 62.5, 42.9 and 66.1% along with specificities of 64.0, 90.2 and 56.0%. CONCLUSIONS The levels of performance achieved by the seizure detection algorithms are not high enough for use in a clinical environment. The algorithm performance figures for our data set are considerably worse than those quoted in the original algorithm source papers. The overlap of frequency characteristics of seizure and non-seizure EEG, artifacts and natural variances in the neonatal EEG cause a great problem to the seizure detection algorithms. SIGNIFICANCE This study shows the difficulties involved in detecting seizures in neonates and the lack of a reliable detection scheme for clinical use. It is clear from this study that while each algorithm does produce some meaningful information, the information would only be usable in a reliable neonatal seizure detection process when accompanied by more complex analysis, and more advanced classifiers.

Early serial EEG in hypoxic ischaemic encephalopathy

OBJECTIVES To perform early serial EEGs in infants with hypoxic ischaemic encephalopathy (HIE) and compare the findings with neurodevelopmental outcome. METHODS Nine full-term neonates with HIE had simultaneous video-EEG polygraphic studies within 8 h of birth. The EEG was repeated at 12-24 h intervals. All surviving infants had a neurodevelopmental assessment at 1 year. RESULTS Two infants had a normal or mildly abnormal EEG within 8 h of birth and neurodevelopmental outcome was normal. Seven infants had severely depressed background activity in the first 8 h of life. In 3 infants the EEG activity recovered within 12-24 h showing continuous activity with no or only minor abnormalities. All these infants had a normal outcome. The remaining 4 infants, who also had an initially inactive recording, subsequently developed severe background abnormalities. At follow-up, two infants had died and the remainder developed major neurological sequelae. CONCLUSIONS Early EEG is an excellent prognostic indicator for a favourable outcome if normal within the first 8 h of life and for a poor outcome if the background activity continues to be inactive or grossly abnormal beyond 8-12 h of life. However, an inactive or very depressed EEG within the first 8 h of life can be associated with good outcome if the EEG activity recovers within 12 h.

Bumetanide for the treatment of seizures in newborn babies with hypoxic ischaemic encephalopathy (NEMO): an open-label, dose finding, and feasibility phase 1/2 trial

BACKGROUND Preclinical data suggest that the loop-diuretic bumetanide might be an effective treatment for neonatal seizures. We aimed to assess dose and feasibility of intravenous bumetanide as an add-on to phenobarbital for treatment of neonatal seizures. METHODS In this open-label, dose finding, and feasibility phase 1/2 trial, we recruited full-term infants younger than 48 h who had hypoxic ischaemic encephalopathy and electrographic seizures not responding to a loading-dose of phenobarbital from eight neonatal intensive care units across Europe. Newborn babies were allocated to receive an additional dose of phenobarbital and one of four bumetanide dose levels by use of a bivariate Bayesian sequential dose-escalation design to assess safety and efficacy. We assessed adverse events, pharmacokinetics, and seizure burden during 48 h continuous electroencephalogram (EEG) monitoring. The primary efficacy endpoint was a reduction in electrographic seizure burden of more than 80% without the need for rescue antiepileptic drugs in more than 50% of infants. The trial is registered with ClinicalTrials.gov, number NCT01434225. FINDINGS Between Sept 1, 2011, and Sept 28, 2013, we screened 30 infants who had electrographic seizures due to hypoxic ischaemic encephalopathy. 14 of these infants (10 boys) were included in the study (dose allocation: 0·05 mg/kg, n=4; 0·1 mg/kg, n=3; 0·2 mg/kg, n=6; 0·3 mg/kg, n=1). All babies received at least one dose of bumetanide with the second dose of phenobarbital; three were withdrawn for reasons unrelated to bumetanide, and one because of dehydration. All but one infant also received aminoglycosides. Five infants met EEG criteria for seizure reduction (one on 0·05 mg/kg, one on 0·1 mg/kg and three on 0·2 mg/kg), and only two did not need rescue antiepileptic drugs (ie, met rescue criteria; one on 0·05 mg/kg and one on 0·3 mg/kg). We recorded no short-term dose-limiting toxic effects, but three of 11 surviving infants had hearing impairment confirmed on auditory testing between 17 and 108 days of age. The most common non-serious adverse reactions were moderate dehydration in one, mild hypotension in seven, and mild to moderate electrolyte disturbances in 12 infants. The trial was stopped early because of serious adverse reactions and limited evidence for seizure reduction. INTERPRETATION Our findings suggest that bumetanide as an add-on to phenobarbital does not improve seizure control in newborn infants who have hypoxic ischaemic encephalopathy and might increase the risk of hearing loss, highlighting the risks associated with the off-label use of drugs in newborn infants before safety assessment in controlled trials. FUNDING European Communitys Seventh Framework Programme.

Second-line anticonvulsant treatment of neonatal seizures: a video-EEG monitoring study.

The authors conducted a randomized trial of second-line anticonvulsant treatments for neonates. The response to treatment was assessed using continuous video-EEG because the clinical diagnosis of seizure in neonates is known to be unreliable. Of 27 neonates with EEG-confirmed seizures, 5 were excluded because of protocol violations, and 11 responded to phenobarbitone in a dose of 40 mg/kg as first line. Three of five neonates treated with lignocaine responded. Six neonates were treated with benzodiazepines as second line: None responded, and their neurodevelopmental outcome was poor.