Differentiating ictal/subclinical spikes and waves in childhood absence epilepsy by spectral and network analyses: A pilot study

ObjectiveChildhood absence epilepsy (CAE) is a disease with distinct seizure semiology and electroencephalographic (EEG) features. Differentiating ictal and subclinical generalized spikes and waves discharges (GSWDs) in the EEG is challenging, since they appear to be identical upon visual inspection. Here, spectral and functional connectivity (FC) analyses were applied to routine EEG data of CAE patients, to differentiate ictal and subclinical GSWDs.MethodsTwelve CAE patients with both ictal and subclinical GSWDs were retrospectively selected for this study. The selected EEG epochs were subjected to frequency analysis in the range of 1–30 Hz. Further, FC analysis based on the imaginary part of coherency was used to determine sensor level networks.ResultsDelta, alpha and beta band frequencies during ictal GSWDs showed significantly higher power compared to subclinical GSWDs. FC showed significant network differences for all frequency bands, demonstrating weaker connectivity between channels during ictal GSWDs.ConclusionUsing spectral and FC analyses significant differences between ictal and subclinical GSWDs in CAE patients were detected, suggesting that these features could be used for machine learning classification purposes to improve EEG monitoring.SignificanceIdentifying differences between ictal and subclinical GSWDs using routine EEG, may improve understanding of this syndrome and the management of patients with CAE.     

Optimized set of criteria for defining interictal epileptiform EEG discharges

ObjectiveTo find and validate the optimal combination of criteria that define interictal epileptiform EEG discharges (IEDs). Our target was a specificity over 95%, to avoid over-reading in clinical EEG.MethodsWe constructed 63 combinations of the six criteria from the operational definition of IEDs, recently issued in the EEG-glossary of the International Federation of Clinical Neurophysiology (IFCN). The diagnostic gold standard was derived from video-EEG recordings. In a testing EEG dataset from 100 patients, we selected the best performing combinations of criteria and then we validated them in an independent dataset from 70 patients. We compared their performance with subjective, expert-scorings and we determined inter-rater agreement (IRA).ResultsWithout using criteria, the specificity of expert-scorings was lower than the pre-defined threshold (86%). The best performing combination of criteria was the following: waves with spiky morphology, followed by a slow-afterwave and voltage map suggesting a source in the brain. In the validation dataset this achieved a specificity of 97% and a sensitivity of 89%. IRA was substantial.ConclusionsThe optimized set of criteria for defining IEDs has high accuracy and IRA.SignificanceUsing these criteria will contribute to decreasing over-reading of EEG and avoid misdiagnosis of epilepsy.     

Continuous electroencephalography characteristics and acute symptomatic seizures in COVID-19 patients

ObjectiveAs concerns regarding neurological manifestations in COVID-19 (coronavirus disease 2019) patients increase, limited data exists on continuous electroencephalography (cEEG) findings in these patients. We present a retrospective cohort study of cEEG monitoring in COVID-19 patients to better explore this knowledge gap.MethodsAmong 22 COVID-19 patients, 19 underwent cEEGs, and 3 underwent routine EEGs (<1 h). Demographic and clinical variables, including comorbid conditions, discharge disposition, survival and cEEG findings, were collected.ResultscEEG was performed for evaluation of altered mental status (n = 17) or seizure-like events (n = 5). Five patients, including 2 with epilepsy, had epileptiform abnormalities on cEEG. Two patients had electrographic seizures without a prior epilepsy history. There were no acute neuroimaging findings. Periodic discharges were noted in one-third of patients and encephalopathic EEG findings were not associated with IV anesthetic use.ConclusionsInterictal epileptiform abnormalities in the absence of prior epilepsy history were rare. However, the discovery of asymptomatic seizures in two of twenty-two patients was higher than previously reported and is therefore of concern.SignificancecEEG monitoring in COVID-19 patients may aid in better understanding an epileptogenic potential of SARS-CoV2 infection. Nevertheless, larger studies utilizing cEEG are required to better examine acute epileptic risk in COVID-19 patients.     

Effect of interictal epileptiform discharges on EEG-based functional connectivity networks

ObjectiveFunctional connectivity networks (FCNs) based on interictal electroencephalography (EEG) can identify pathological brain networks associated with epilepsy. FCNs are altered by interictal epileptiform discharges (IEDs), but it is unknown whether this is due to the morphology of the IED or the underlying pathological activity. Therefore, we characterized the impact of IEDs on the FCN through simulations and EEG analysis.MethodsWe introduced simulated IEDs to sleep EEG recordings of eight healthy controls and analyzed the effect of IED amplitude and rate on the FCN. We then generated FCNs based on epochs with and without IEDs and compared them to the analogous FCNs from eight subjects with infantile spasms (IS), based on 1340 visually marked IEDs. Differences in network structure and strength were assessed.ResultsIEDs in IS subjects caused increased connectivity strength but no change in network structure. In controls, simulated IEDs with physiological amplitudes and rates did not alter network strength or structure.ConclusionsIncreases in connectivity strength in IS subjects are not artifacts caused by the interictal spike waveform and may be related to the underlying pathophysiology of IS.SignificanceDynamic changes in EEG-based FCNs during IEDs may be valuable for identification of pathological networks associated with epilepsy.     

Encephalopathy related to status epilepticus during slow sleep (ESES) as atypical evolution of Panayiotopoulos syndrome: an EEG and neuropsychological study

Aim: We report two patients with Panayiotopoulos syndrome (PS) who developed encephalopathy related to status epilepticus during slow sleep (ESES) at the peak of their clinical course. Methods: Clinical charts and EEG data were reviewed. Results: The patients exhibited nocturnal autonomic seizures and occipital EEG foci, the latter of which later evolved into multifocal EEG foci with synchronous frontopolar and occipital spikes (Fp‐O EEG foci), and finally into continuous spikes‐waves during sleep (CSWS; spike‐wave index >85% based on whole‐night sleep recording) at eight years and seven years of age, respectively. The occipital spikes always preceded frontopolar spikes by 30～50 mseconds based on the analysis of CSWS. Neuropsychological ability, including IQ, deteriorated during the CSWS period in both patients. The autonomic seizures and focal to bilateral tonic‐clonic seizures were initially resistant to antiepileptic drugs (AEDs), and occurred more than 10 times in both patients. However, the seizures and EEG findings gradually resolved, and AEDs were successfully terminated in both patients. Conclusion: PS can progress to ESES if the clinical course exhibits atypical evolution. The initial autonomic symptom of the seizures and interictal Fp‐O EEG foci should be carefully monitored in patients with CSWS or ESES.     

Interictal epileptiform discharges vary across age groups

ObjectiveTo investigate whether the occurrence and morphology of interictal epileptiform discharges (IEDs) in scalp-EEG change by age.Methods10,547 patients who had a standard or sleep deprived EEG recording reported using the SCORE standard were included. 875 patients had at least one EEG with focal IEDs. Focal IED morphology was analyzed by age using quantitative measures in EEGLAB and by visual classification based on the SCORE standard. We present distributions of IED measures by age group, with medians, interquartiles, 5th and 95th percentiles.ResultsFocal IEDs occurred most frequently in children and elderly. IED morphology and localization depended on age (p < 0.001). IEDs had higher amplitudes, sharper peaks, larger slopes, shorter durations, larger slow-wave areas and wider distributions in children. These morphological characteristics diminished and the IEDs became more lateralized with increasing age. Spike asymmetry was stable across all age groups.ConclusionsIEDs have age-dependent characteristics. A spike detector, human or computer, should not operate with the same set of thresholds for patients at various age. With increasing age, focal IEDs are less sharp, have lower amplitudes, have less prominent slow-waves and they become more lateralized. Our findings can help EEG readers in detecting and correctly describing IEDs in patients of various age.SignificanceEEG readers should always consider patient age when interpreting interictal epileptiform discharges.     

EEG abnormalities preceding the epilepsy onset in tuberous sclerosis complex patients – A prospective study of 5 patients

Tuberous sclerosis complex (TSC) is a multisystem, autosomal dominant disorder characterized by multiple hamartomas development. Epilepsy is the most common symptom appearing in 80–90% of the patients mainly in the first year of life. A prompt and early seizure control is crucial and can prevent development of an epileptic encephalopathy and secondary mental retardation. Therefore the very early identification of seizures seems to be of a great importance. We present the cases of 5 patients diagnosed with TSC prenatally or perinatally and regularly monitored (at 4–6 weeks intervals) with EEG before the epilepsy onset. The patients' age at baseline varied from 9 days to 9 weeks. In all of the patients epileptiform discharges preceded the epilepsy onset. The time interval between abnormality detection on EEG and the epilepsy onset varied from 1 to 8 days. The patient's age at the epilepsy onset ranged from the 17th day to the 5th month of life. In one patient the EEG was abnormal from the beginning and in this patient the epileptic seizures started from the neonatal period. In the rest of the patients (4/5) the EEG remained normal throughout the first months of life. In all of the children epilepsy started with focal motor seizures. Our study is the first prospective one showing the results of the EEG monitoring in TSC patients and the natural evolution of the EEG patterns in patients with the seizures types other than infantile spasms.