Combined value of interictal markers and stimulated seizures to estimate the seizure onset zone in stereoelectroencephalography.

OBJECTIVE: This study was undertaken to investigate the potential of interictal electroencephalographic (EEG) findings and electrically stimulated seizures during stereo-EEG (SEEG) as surrogate markers for the spontaneous seizure onset zone (spSOZ). We hypothesized that combining the localizing information of these markers would allow clinically meaningful estimation of the spSOZ. METHODS: We included all patients (n = 63) who underwent SEEG between January 2013 and March 2020 at Helsinki University Hospital and had spontaneous seizures during the recording. We scored spikes, gamma activity, and background abnormality on each channel visually during a 12-h epoch containing waking state and sleep. Based on semiology, we classified stimulated seizures as typical or atypical/unclassifiable and estimated the stimulated SOZ (stimSOZ) for typical seizures. To assess which markers increased the odds of channel inclusion in the spSOZ, we fitted mixed effects logistic regression models. RESULTS: A combined regression model including the stimSOZ and interictal markers scored during sleep performed better in estimating which channels were part of the spSOZ than models based on stimSOZ (p < .001) or interictal markers (p < .001) alone. Of the individual markers, the effect sizes were greatest for inclusion of a channel in the stimSOZ (odds ratio [OR] = 60, 95% confidence interval [CI] = 37-97, p < .001) and for continuous (OR = 25, 95% CI = 12-55, p < .001) and subcontinuous (OR = 36, 95% CI = 21-64, p < .001) interictal spiking. At the individual level, the model's accuracy to predict spSOZ inclusion varied markedly (median accuracy = 85.7, range = 54.4-100), which was not explained by etiology (p > .05). SIGNIFICANCE: Compared to either marker alone, combining visually rated interictal SEEG markers and stimulated seizures improved prediction of which SEEG channels belonged to the spSOZ. Inclusion in the stimSOZ and continuous or subcontinuous spikes increased the odds of spSOZ inclusion the most. Future studies should investigate whether suboptimal sampling of the true epileptogenic zone can explain the model's poor performance in certain patients.

Development of a stereo-EEG based seizure matching system for clinical decision making in epilepsy surgery.

Objective.The proportion of patients becoming seizure-free after epilepsy surgery has stagnated. Large multi-center stereo-electroencephalography (SEEG) datasets can allow comparing new patients to past similar cases and making clinical decisions with the knowledge of how cases were treated in the past. However, the complexity of these evaluations makes the manual search for similar patients impractical. We aim to develop an automated system that electrographically and anatomically matches seizures to those in a database. Additionally, since features that define seizure similarity are unknown, we evaluate the agreement and features among experts in classifying similarity.Approach.We utilized 320 SEEG seizures from 95 consecutive patients who underwent epilepsy surgery. Eight international experts evaluated seizure-pair similarity using a four-level similarity score. As our primary outcome, we developed and validated an automated seizure matching system by employing patient data marked by independent experts. Secondary outcomes included the inter-rater agreement (IRA) and features for classifying seizure similarity.Main results.The seizure matching system achieved a median area-under-the-curve of 0.76 (interquartile range, 0.1), indicating its feasibility. Six distinct seizure similarity features were identified and proved effective: onset region, onset pattern, propagation region, duration, extent of spread, and propagation speed. Among these features, the onset region showed the strongest correlation with expert scores (Spearman's rho = 0.75,p< 0.001). Additionally, the moderate IRA confirmed the practicality of our approach with an agreement of 73.9% (7%), and Gwet's kappa of 0.45 (0.16). Further, the interoperability of the system was validated on seizures from five centers.Significance.We demonstrated the feasibility and validity of a SEEG seizure matching system across patients, effectively mirroring the expertise of epileptologists. This novel system can identify patients with seizures similar to that of a patient being evaluated, thus optimizing the treatment plan by considering the results of treating similar patients in the past, potentially improving surgery outcome.

Corrigendum: Building an open source classifier for the neonatal EEG background: a systematic feature-based approach from expert scoring to clinical visualization.

[This corrects the article DOI: 10.3389/fnhum.2021.675154.].

Quantitative EEG features during the first day correlate to clinical outcome in perinatal asphyxia.

OBJECTIVE: To assess whether computational electroencephalogram (EEG) measures during the first day of life correlate to clinical outcomes in infants with perinatal asphyxia with or without hypoxic-ischemic encephalopathy (HIE). METHODS: We analyzed four-channel EEG monitoring data from 91 newborn infants after perinatal asphyxia. Altogether 42 automatically computed amplitude- and synchrony-related EEG features were extracted as 2-hourly average at very early (6 h) and early (24 h) postnatal age; they were correlated to the severity of HIE in all infants, and to four clinical outcomes available in a subcohort of 40 newborns: time to full oral feeding (nasogastric tube NGT), neonatal brain MRI, Hammersmith Infant Neurological Examination (HINE) at three months, and Griffiths Scales at two years. RESULTS: At 6 h, altogether 14 (33%) EEG features correlated significantly to the HIE grade ([r]= 0.39-0.61, p < 0.05), and one feature correlated to NGT ([r]= 0.50). At 24 h, altogether 13 (31%) EEG features correlated significantly to the HIE grade ([r]= 0.39-0.56), six features correlated to NGT ([r]= 0.36-0.49) and HINE ([r]= 0.39-0.61), while no features correlated to MRI or Griffiths Scales. CONCLUSIONS: Our results show that the automatically computed measures of early cortical activity may provide outcome biomarkers for clinical and research purposes. IMPACT: The early EEG background and its recovery after perinatal asphyxia reflect initial severity of encephalopathy and its clinical recovery, respectively. Computational EEG features from the early hours of life show robust correlations to HIE grades and to early clinical outcomes. Computational EEG features may have potential to be used as cortical activity biomarkers in early hours after perinatal asphyxia.

Clinical outcome prediction with an automated EEG trend, Brain State of the Newborn, after perinatal asphyxia.

OBJECTIVE: To evaluate the utility of a fully automated deep learning -based quantitative measure of EEG background, Brain State of the Newborn (BSN), for early prediction of clinical outcome at four years of age. METHODS: The EEG monitoring data from eighty consecutive newborns was analyzed using the automatically computed BSN trend. BSN levels during the first days of life (a of total 5427 hours) were compared to four clinical outcome categories: favorable, cerebral palsy (CP), CP with epilepsy, and death. The time dependent changes in BSN-based prediction for different outcomes were assessed by positive/negative predictive value (PPV/NPV) and by estimating the area under the receiver operating characteristic curve (AUC). RESULTS: The BSN values were closely aligned with four visually determined EEG categories (p < 0·001), as well as with respect to clinical milestones of EEG recovery in perinatal Hypoxic Ischemic Encephalopathy (HIE; p < 0·003). Favorable outcome was related to a rapid recovery of the BSN trend, while worse outcomes related to a slow BSN recovery. Outcome predictions with BSN were accurate from 6 to 48 hours of age: For the favorable outcome, the AUC ranged from 95 to 99% (peak at 12 hours), and for the poor outcome the AUC ranged from 96 to 99% (peak at 12 hours). The optimal BSN levels for each PPV/NPV estimate changed substantially during the first 48 hours, ranging from 20 to 80. CONCLUSIONS: We show that the BSN provides an automated, objective, and continuous measure of brain activity in newborns. SIGNIFICANCE: The BSN trend discloses the dynamic nature that exists in both cerebral recovery and outcome prediction, supports individualized patient care, rapid stratification and early prognosis.

Networks of cortical activity show graded responses to perinatal asphyxia.

BACKGROUND: Perinatal asphyxia often leads to hypoxic-ischemic encephalopathy (HIE) with a high risk of neurodevelopmental consequences. While moderate and severe HIE link to high morbidity, less is known about brain effects of perinatal asphyxia with no or only mild HIE. Here, we test the hypothesis that cortical activity networks in the newborn infants show a dose-response to asphyxia. METHODS: We performed EEG recordings for infants with perinatal asphyxia/HIE of varying severity (n = 52) and controls (n = 53) and examined well-established computational metrics of cortical network activity. RESULTS: We found graded alterations in cortical activity networks according to severity of asphyxia/HIE. Furthermore, our findings correlated with early clinical recovery measured by the time to attain full oral feeding. CONCLUSION: We show that both local and large-scale correlated cortical activity are affected by increasing severity of HIE after perinatal asphyxia, suggesting that HIE and perinatal asphyxia are better represented as a continuum rather than the currently used discreet categories. These findings imply that automated computational measures of cortical function may be useful in characterizing the dose effects of adversity in the neonatal brain; such metrics hold promise for benchmarking clinical trials via patient stratification or as early outcome measures. IMPACT: Perinatal asphyxia causes every fourth neonatal death worldwide and provides a diagnostic and prognostic challenge for the clinician. We report that infants with perinatal asphyxia show specific graded responses in cortical networks according to severity of asphyxia and ensuing hypoxic-ischaemic encephalopathy. Early EEG recording and automated computational measures of brain function have potential to help in clinical evaluation of infants with perinatal asphyxia.

Seizure Burden and Neurologic Outcomes After Neonatal Encephalopathy.

BACKGROUND AND OBJECTIVES: Seizures are common during neonatal encephalopathy (NE), but the contribution of seizure burden (SB) to outcomes remains controversial. This study aims to examine the relationship between electrographic SB and neurologic outcomes after NE. METHODS: This prospective cohort study recruited newborns ≥36 weeks postmenstrual age around 6 hours of life between August 2014 and November 2019 from a neonatal intensive care unit (NICU). Participants underwent continuous electroencephalography for at least 48 hours, brain MRI within 3-5 days of life, and structured follow-up at 18 months. Electrographic seizures were identified by board-certified neurophysiologists and quantified as total SB and maximum hourly SB. A medication exposure score was calculated based on all antiseizure medications given during NICU admission. Brain MRI injury severity was classified based on basal ganglia and watershed scores. Developmental outcomes were measured using the Bayley Scales of Infant Development, Third Edition. Multivariable regression analyses were performed, adjusting for significant potential confounders. RESULTS: Of 108 enrolled infants, 98 had continuous EEG (cEEG) and MRI data collected, of which 5 were lost to follow-up, and 6 died before age 18 months. All infants with moderate-severe encephalopathy completed therapeutic hypothermia. cEEG-confirmed neonatal seizures occurred in 21 (24%) newborns, with a total SB mean of 12.5 ± 36.4 minutes and a maximum hourly SB mean of 4 ± 10 min/h. After adjusting for MRI brain injury severity and medication exposure, total SB was significantly associated with lower cognitive (-0.21, 95% CI -0.33 to -0.08, p = 0.002) and language (-0.25, 95% CI -0.39 to -0.11, p = 0.001) scores at 18 months. Total SB of 60 minutes was associated with 15-point decline in language scores and 70 minutes for cognitive scores. However, SB was not significantly associated with epilepsy, neuromotor score, or cerebral palsy (p > 0.1). DISCUSSION: Higher SB during NE was independently associated with worse cognitive and language scores at 18 months, even after adjusting for exposure to antiseizure medications and severity of brain injury. These observations support the hypothesis that neonatal seizures occurring during NE independently contribute to long-term outcomes.

An automated bedside measure for monitoring neonatal cortical activity: a supervised deep learning-based electroencephalogram classifier with external cohort validation.

BACKGROUND: Electroencephalogram (EEG) monitoring is recommended as routine in newborn neurocritical care to facilitate early therapeutic decisions and outcome predictions. EEG's larger-scale implementation is, however, hindered by the shortage of expertise needed for the interpretation of spontaneous cortical activity, the EEG background. We developed an automated algorithm that transforms EEG recordings to quantified interpretations of EEG background and provides simple intuitive visualisations in patient monitors. METHODS: In this method-development and proof-of-concept study, we collected visually classified EEGs from infants recovering from birth asphyxia or stroke. We used unsupervised learning methods to explore latent EEG characteristics, which guided the supervised training of a deep learning-based classifier. We assessed the classifier performance using cross-validation and an external validation dataset. We constructed a novel measure of cortical function, brain state of the newborn (BSN), from the novel EEG background classifier and a previously published sleep-state classifier. We estimated clinical utility of the BSN by identification of two key items in newborn brain monitoring, the onset of continuous cortical activity and sleep-wake cycling, compared with the visual interpretation of the raw EEG signal and the amplitude-integrated (aEEG) trend. FINDINGS: We collected 2561 h of EEG from 39 infants (gestational age 35·0-42·1 weeks; postnatal age 0-7 days). The external validation dataset included 105 h of EEG from 31 full-term infants. The overall accuracy of the EEG background classifier was 92% in the whole cohort (95% CI 91-96; range 85-100 for individual infants). BSN trend values were closely related to the onset of continuous EEG activity or sleep-wake cycling, and BSN levels showed robust difference between aEEG categories. The temporal evolution of the BSN trends showed early diverging trajectories in infants with severely abnormal outcomes. INTERPRETATION: The BSN trend can be implemented in bedside patient monitors as an EEG interpretation that is intuitive, transparent, and clinically explainable. A quantitative trend measure of brain function might harmonise practices across medical centres, enable wider use of brain monitoring in neurocritical care, and might facilitate clinical intervention trials. FUNDING: European Training Networks Funding Scheme, the Academy of Finland, Finnish Pediatric Foundation (Lastentautiensäätiö), Aivosäätiö, Sigrid Juselius Foundation, HUS Children's Hospital, HUS Diagnostic Center, National Health and Medical Research Council of Australia.

Poor aEEG background recovery after perinatal hypoxic ischemic encephalopathy predicts postneonatal epilepsy by age 4 years.

OBJECTIVE: This study evaluated the accuracy of neonatal amplitude-integrated electroencephalography (aEEG) brain monitoring for predicting development of postneonatal epilepsy after perinatal hypoxic ischemic encephalopathy (HIE). METHODS: We studied a population-based cohort of 85 consecutive neonates with moderate-to-severe HIE that had aEEG started <12 hours postnatally. We marked electrographic seizures and graded each hour of the aEEG background as inactive, burst-suppression, or continuous without or with sleep cycling. These aEEG parameters were compared to outcome at 4-years age (deceased, epilepsy, cerebral palsy without epilepsy, favorable), which was available for 80 children. RESULTS: At group level, total seizure burden (p = 0.003), maximum hourly seizure burden (p = 0.007), and aEEG background recovery (p < 0.001) were all significantly associated with outcome. At individual level six children developed epilepsy, and the most accurate predictors for later epilepsy were inactive aEEG at 24 hours (accuracy 97%, positive predictive value 100%, two false negatives) and inactive aEEG at the onset of seizures (accuracy 97%, sensitivity of 100%, one false positive). CONCLUSIONS: At individual level aEEG background recovery was a better predictor for later epilepsy than neonatal seizures, although both were associated with epilepsy at group level. SIGNIFICANCE: Poor aEEG background recovery predicts development of epilepsy after perinatal HIE at individual level.

Ensemble Learning Using Individual Neonatal Data for Seizure Detection.

OBJECTIVE: Sharing medical data between institutions is difficult in practice due to data protection laws and official procedures within institutions. Therefore, most existing algorithms are trained on relatively small electroencephalogram (EEG) data sets which is likely to be detrimental to prediction accuracy. In this work, we simulate a case when the data can not be shared by splitting the publicly available data set into disjoint sets representing data in individual institutions. METHODS AND PROCEDURES: We propose to train a (local) detector in each institution and aggregate their individual predictions into one final prediction. Four aggregation schemes are compared, namely, the majority vote, the mean, the weighted mean and the Dawid-Skene method. The method was validated on an independent data set using only a subset of EEG channels. RESULTS: The ensemble reaches accuracy comparable to a single detector trained on all the data when sufficient amount of data is available in each institution. CONCLUSION: The weighted mean aggregation scheme showed best performance, it was only marginally outperformed by the Dawid-Skene method when local detectors approach performance of a single detector trained on all available data. CLINICAL IMPACT: Ensemble learning allows training of reliable algorithms for neonatal EEG analysis without a need to share the potentially sensitive EEG data between institutions.

Sleep State Trend (SST), a bedside measure of neonatal sleep state fluctuations based on single EEG channels.

OBJECTIVE: To develop and validate an automated method for bedside monitoring of sleep state fluctuations in neonatal intensive care units. METHODS: A deep learning-based algorithm was designed and trained using 53 EEG recordings from a long-term (a)EEG monitoring in 30 near-term neonates. The results were validated using an independent dataset from 30 polysomnography recordings. In addition, we constructed Sleep State Trend (SST), a bedside-ready means for visualizing classifier outputs. RESULTS: The accuracy of quiet sleep detection in the training data was 90%, and the accuracy was comparable (85-86 %) in all bipolar derivations available from the 4-electrode recordings. The algorithm generalized well to a polysomnography dataset, showing 81% overall accuracy despite different signal derivations. SST allowed an intuitive, clear visualization of the classifier output. CONCLUSIONS: Fluctuations in sleep states can be detected at high fidelity from a single EEG channel, and the results can be visualized as a transparent and intuitive trend in the bedside monitors. SIGNIFICANCE: The Sleep State Trend (SST) may provide caregivers and clinical studies a real-time view of sleep state fluctuations and its cyclicity.

Validating an SVM-based neonatal seizure detection algorithm for generalizability, non-inferiority and clinical efficacy.

Neonatal seizure detection algorithms (SDA) are approaching the benchmark of human expert annotation. Measures of algorithm generalizability and non-inferiority as well as measures of clinical efficacy are needed to assess the full scope of neonatal SDA performance. We validated our neonatal SDA on an independent data set of 28 neonates. Generalizability was tested by comparing the performance of the original training set (cross-validation) to its performance on the validation set. Non-inferiority was tested by assessing inter-observer agreement between combinations of SDA and two human expert annotations. Clinical efficacy was tested by comparing how the SDA and human experts quantified seizure burden and identified clinically significant periods of seizure activity in the EEG. Algorithm performance was consistent between training and validation sets with no significant worsening in AUC (p > 0.05, n = 28). SDA output was inferior to the annotation of the human expert, however, re-training with an increased diversity of data resulted in non-inferior performance (Δκ = 0.077, 95% CI: -0.002-0.232, n = 18). The SDA assessment of seizure burden had an accuracy ranging from 89 to 93%, and 87% for identifying periods of clinical interest. The proposed SDA is approaching human equivalence and provides a clinically relevant interpretation of the EEG.

Asymmetry in sleep spindles and motor outcome in infants with unilateral brain injury.

AIM: To determine whether interhemispheric difference in sleep spindles in infants with perinatal unilateral brain injury could link to a pathological network reorganization that underpins the development of unilateral cerebral palsy (CP). METHOD: This was a multicentre retrospective study of 40 infants (19 females, 21 males) with unilateral brain injury. Sleep spindles were detected and quantified with an automated algorithm from electroencephalograph records performed at 2 months to 5 months of age. The clinical outcomes after 18 months were compared to spindle power asymmetry (SPA) between hemispheres in different brain regions. RESULTS: We found a significantly increased SPA in infants who later developed unilateral CP (n=13, with the most robust interhemispheric difference seen in the central spindles. The best individual-level prediction of unilateral CP was seen in the centro-occipital spindles with an overall accuracy of 93%. An empiric cut-off level for SPA at 0.65 gave a positive predictive value of 100% and a negative predictive value of 93% for later development of unilateral CP. INTERPRETATION: Our data suggest that automated analysis of interhemispheric SPA provides a potential biomarker of unilateral CP at a very early age. This holds promise for guiding the early diagnostic process in infants with a perinatally identified brain injury. WHAT THIS PAPER ADDS: Unilateral perinatal brain injury may affect the development of electroencephalogram (EEG) sleep spindles. Interhemispheric asymmetry in sleep spindles can be quantified with automated EEG analysis. Spindle power asymmetry can be a potential biomarker of unilateral cerebral palsy.

Building an Open Source Classifier for the Neonatal EEG Background: A Systematic Feature-Based Approach From Expert Scoring to Clinical Visualization.

Neonatal brain monitoring in the neonatal intensive care units (NICU) requires a continuous review of the spontaneous cortical activity, i.e., the electroencephalograph (EEG) background activity. This needs development of bedside methods for an automated assessment of the EEG background activity. In this paper, we present development of the key components of a neonatal EEG background classifier, starting from the visual background scoring to classifier design, and finally to possible bedside visualization of the classifier results. A dataset with 13,200 5-minute EEG epochs (8-16 channels) from 27 infants with birth asphyxia was used for classifier training after scoring by two independent experts. We tested three classifier designs based on 98 computational features, and their performance was assessed with respect to scoring system, pre- and post-processing of labels and outputs, choice of channels, and visualization in monitor displays. The optimal solution achieved an overall classification accuracy of 97% with a range across subjects of 81-100%. We identified a set of 23 features that make the classifier highly robust to the choice of channels and missing data due to artefact rejection. Our results showed that an automated bedside classifier of EEG background is achievable, and we publish the full classifier algorithm to allow further clinical replication and validation studies.

Secondary somatosensory cortex evoked responses and 6-year neurodevelopmental outcome in extremely preterm children.

OBJECTIVE: We assessed in extremely preterm born (EPB) children whether secondary somatosensory cortex (SII) responses recorded with magnetoencephalography (MEG) at term-equivalent age (TEA) correlate with neurodevelopmental outcome at age 6 years. Secondly, we assessed whether SII responses differ between 6-year-old EPB and term-born (TB) children. METHODS: 39 EPB children underwent MEG with tactile stimulation at TEA. At age 6 years, 32 EPB and 26 TB children underwent MEG including a sensorimotor task requiring attention and motor inhibition. SII responses to tactile stimulation were modeled with equivalent current dipoles. Neurological outcome, motor competence, and general cognitive ability were prospectively evaluated at age 6 years. RESULTS: Unilaterally absent SII response at TEA was associated with abnormal motor competence in 6-year-old EPB children (p = 0.03). At age 6 years, SII responses were bilaterally detectable in most EPB (88%) and TB (92%) children (group comparison, p = 0.69). Motor inhibition was associated with decreased SII peak latencies in TB children, but EPB children lacked this effect (p = 0.02). CONCLUSIONS: Unilateral absence of an SII response at TEA predicted poorer motor outcome in EPB children. SIGNIFICANCE: Neurophysiological methods may provide new means for outcome prognostication in EPB children.

Early oxygen levels contribute to brain injury in extremely preterm infants.

BACKGROUND: Extremely low gestational age newborns (ELGANs) are at risk of neurodevelopmental impairments that may originate in early NICU care. We hypothesized that early oxygen saturations (SpO2), arterial pO2 levels, and supplemental oxygen (FiO2) would associate with later neuroanatomic changes. METHODS: SpO2, arterial blood gases, and FiO2 from 73 ELGANs (GA 26.4 ± 1.2; BW 867 ± 179 g) during the first 3 postnatal days were correlated with later white matter injury (WM, MRI, n = 69), secondary cortical somatosensory processing in magnetoencephalography (MEG-SII, n = 39), Hempel neurological examination (n = 66), and developmental quotients of Griffiths Mental Developmental Scales (GMDS, n = 58). RESULTS: The ELGANs with later WM abnormalities exhibited lower SpO2 and pO2 levels, and higher FiO2 need during the first 3 days than those with normal WM. They also had higher pCO2 values. The infants with abnormal MEG-SII showed opposite findings, i.e., displayed higher SpO2 and pO2 levels and lower FiO2 need, than those with better outcomes. Severe WM changes and abnormal MEG-SII were correlated with adverse neurodevelopment. CONCLUSIONS: Low oxygen levels and high FiO2 need during the NICU care associate with WM abnormalities, whereas higher oxygen levels correlate with abnormal MEG-SII. The results may indicate certain brain structures being more vulnerable to hypoxia and others to hyperoxia, thus emphasizing the role of strict saturation targets. IMPACT: This study indicates that both abnormally low and high oxygen levels during early NICU care are harmful for later neurodevelopmental outcomes in preterm neonates. Specific brain structures seem to be vulnerable to low and others to high oxygen levels. The findings may have clinical implications as oxygen is one of the most common therapies given in NICUs. The results emphasize the role of strict saturation targets during the early postnatal period in preterm infants.

Towards multimodal brain monitoring in asphyxiated newborns with amplitude-integrated EEG and simultaneous somatosensory evoked potentials.

BACKGROUND: Somatosensory evoked potentials (SEPs) offer an additional bedside tool for outcome prediction after perinatal asphyxia. AIMS: To assess the reliability of SEPs recorded with bifrontoparietal amplitude-integrated electroencephalography (aEEG) brain monitoring setup for outcome prediction in asphyxiated newborns undergoing therapeutic hypothermia. STUDY DESIGN: Retrospective observational single-center study. SUBJECTS: 27 consecutive asphyxiated full- or near-term newborns (25 under hypothermia) that underwent median nerve aEEG-SEPs as part of their clinical evaluation at the neonatal intensive care unit of Helsinki University Hospital. OUTCOME MEASURES: aEEG-SEP classification (present, absent or unreliable) was compared to classification of SEPs recorded with a full EEG montage (EEG-SEP), and outcome determined from medical records at approximately 12-months-age. Unfavorable outcome included death, cerebral palsy, or severe epilepsy. RESULTS: The aEEG-SEP and EEG-SEP classifications were concordant in 21 of the 22 newborns with both recordings available. All five newborns with bilaterally absent aEEG-SEPs had absent EEG-SEPs and the four with outcome information available had an unfavorable outcome (one was lost to follow-up). Of the newborns with aEEG-SEPs present, all with follow-up exams available had bilaterally present EEG-SEPs and a favorable outcome (one was lost to follow-up). One newborn with unilaterally absent aEEG-SEP at 25 h of age had bilaterally present EEG-SEPs on the next day, and a favorable outcome. CONCLUSIONS: aEEG-SEPs recorded during therapeutic hypothermia on the first postnatal days are reliable for assessing brain injury severity. Adding SEP into routine aEEG brain monitoring offers an additional tool for very early outcome prediction after birth asphyxia.

Association of fast ripples on intracranial EEG and outcomes after epilepsy surgery.

OBJECTIVE: To examine whether fast ripples (FRs) are an accurate marker of the epileptogenic zone, we analyzed overnight stereo-EEG recordings from 43 patients and hypothesized that FR resection ratio, maximal FR rate, and FR distribution predict postsurgical seizure outcome. METHODS: We detected FRs automatically from an overnight recording edited for artifacts and visually from a 5-minute period of slow-wave sleep. We examined primarily the accuracy of removing ≥50% of total FR events or of channels with FRs to predict postsurgical seizure outcome (Engel class I = good, classes II-IV = poor) according to the whole-night and 5-minute analysis approaches. Secondarily, we examined the association of low overall FR rates or absence or incomplete resection of 1 dominant FR area with poor outcome. RESULTS: The accuracy of outcome prediction was highest (81%, 95% confidence interval [CI] 67%-92%) with the use of the FR event resection ratio and whole-night recording (vs 72%, 95% CI 56%-85%, for the visual 5-minute approach). Absence of channels with FR rates >6/min (p = 0.001) and absence or incomplete resection of 1 dominant FR area (p < 0.001) were associated with poor outcome. CONCLUSIONS: FRs are accurate in predicting epilepsy surgery outcome at the individual level when overnight recordings are used. Absence of channels with high FR rates or absence of 1 dominant FR area is a poor prognostic factor that may reflect suboptimal spatial sampling of the epileptogenic zone or multifocality, rather than an inherently low sensitivity of FRs. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that FRs are accurate in predicting epilepsy surgery outcome.

Neonatal neuroimaging and neurophysiology predict infantile onset epilepsy after perinatal hypoxic ischemic encephalopathy.

PURPOSE: To evaluate the accuracy of hypoxic ischemic encephalopathy (HIE) grade, and neonatal neurophysiological and neuroimaging measures for predicting development of infantile spasms syndrome (IS) or other postneonatal, infantile onset epilepsy after perinatal HIE. METHODS: We examined a population-based cohort of 92 consequent infants with moderate-to-severe HIE. The HIE grade and neonatal neuroimaging (MRI) and neurophysiology (EEG and somatosensory evoked potentials, SEPs) findings were compared to the development of IS or other epilepsy within the first year of life. RESULTS: Out of 74 surviving infants with follow-up information, five developed IS and one developed a focal onset epilepsy. They all had recovered from severe HIE. All survivors with inactive neonatal EEG (recorded within the first few postnatal days, n = 4) or the most severe type of brain injury in MRI (n = 3) developed epilepsy (positive predictive value, PPV 100 %). Bilaterally absent SEPs had 100 % sensitivity and 75 % PPV for epilepsy. A combination of absent SEPs and a poor MRI finding (combined deep and cortical gray matter injury) resulted in higher PPV (86 %) without lowering sensitivity (100 %). Follow-up EEGs showed recurrent epileptiform activity already between 1- and 2-months age in those that developed epilepsy, distinguishing them from those surviving without epilepsy. CONCLUSIONS: Poor neonatal neuroimaging and neurophysiological findings provide accurate prediction for development of infantile onset epilepsy after HIE. Of the neonates with severe HIE, the ones with severe neonatal MRI and neurophysiological abnormalities need frequent follow-up, including repeated EEGs, for early detection of IS.