Diagnosis of Electrical Status Epilepticus During Slow-Wave Sleep With 100 Seconds of Sleep

The First 100 Seconds of Sleep of rEEGs Can Be a Reliable Scoring Method for D/EE-SWAS

PURPOSE

Developmental/epileptic encephalopathy with spike wave activation with sleep, formerly known as electrical status epilepticus in sleep, is an electrographic pattern in which the interictal epileptiform activity is augmented by transition to sleep. Recent studies demonstrate the utility of the first 100 seconds of sleep of long-term monitoring (LTM) as a scoring method for electrical status epilepticus in sleep. Our aim was to measure the reliability of the spike-wave index (SWI) of the first 100 seconds of sleep of routine EEG (rEEG) as a tool for diagnosis of developmental/epileptic encephalopathy with spike wave activation with sleep.

METHODS

Approximately three hundred forty LTMs were reviewed, and 25 studies from 25 unique patients had comparable rEEGs. Two neurophysiologists calculated the SWI of the first 100 seconds of spontaneous stage II non-random eye movement sleep, the first 5-minute bin of sleep, and three separate 5-minute bins throughout sleep in LTM. This was compared to the SWI of the first 100 seconds of sleep in rEEG. Agreement was analyzed using Lin's concordance correlation coefficient (CCC).

RESULTS

Using 50% as a diagnostic cut-off, we observed moderate agreement between the SWI of the first 100 seconds of sleep of rEEG and three bin LTM (CCC = 0.94, 95% CI: 0.88-0.97). Agreement was slightly higher for the comparison to first bin LTM SWI (CCC = 0.96, 95% CI: 0.92-0.98) and first 100 seconds LTM SWI (CCC = 0.96, 95% CI: 0.92-0.98).

CONCLUSIONS

This study demonstrates the first 100 seconds of sleep of rEEG technique as a time efficient diagnostic tool for patients with concern for developmental/epileptic encephalopathy with spike wave activation with sleep.

The prospective study of 54 children with electrical status epilepticus during sleep: How to simplify the electroencephalogram diagnosis and guide the treatment

OBJECTIVE

To simplify the electroencephalogram (EEG) diagnosis and guide the treatment of electrical status epilepticus during sleep (ESES).

METHODS

We recruited 54 children with ESES from December 2019 to December 2020 and compared various spike-wave index (SWI) calculation methods. Time-frequency analysis assessed the correlation between high-frequency oscillations energy and the SWI. We divided 42 children into responder and non-responder treatment groups based on the observations made during a 12-month follow-up period and evaluate different treatment and the independent risk factors of refractory ESES.

RESULTS

The SWI of 5 min before the second sleep cycle of non-rapid eye movement (NREM; long method II) and that of all NREM sleep (total method) were not significantly different (p = .06). The average energy of γ (r = .288, p = .002) and ripple (r = .203, p = .04) oscillations were correlated with the SWI. Multivariable logistic regression analysis showed that encephalomalacia was an independent risk factor for refractory ESES (OR: 10.48, 95% CI: 1.62-67.63). The clinical seizure improvement rates of anti-seizure medications (ASMs), ASMs with benzodiazepines, and ASMs with benzodiazepines and steroids after 12 months were 9.3%, 42.8%, and 53.8%, EEG improvement rate were 5.5%, 30.9% and 37%, respectively. The intelligence of the children in the responder treatment group has improved during the 1-year follow-up.

SIGNIFICANCE

These findings demonstrate EEG and clinical features of ESES and may provide basis for simplifying diagnosis and guiding the treatment of children with ESES.

The Spike-Wave Index of the First 100 Seconds of Sleep Can Be a Reliable Scoring Method for Electrographic Status Epilepticus in Sleep

INTRODUCTION

Electrical status epilepticus in sleep (ESES) is an electrographic pattern in which interictal epileptiform activity is augmented by the transition to sleep, with non-rapid eye movement sleep state characterized by near-continuous lateralized or bilateral epileptiform discharges. The aim of this study was to measure the reliability of the spike-wave index (SWI) of the first 100 seconds of sleep as a tool for the diagnosis of ESES.

METHODS

One hundred forty studies from 60 unique patients met the inclusion. Two neurophysiologists calculated the SWI of the first 100 seconds of spontaneous stage II non-rapid eye movement sleep. This was compared with the SWI of the first 5 minutes of non-rapid eye movement sleep and the cumulative SWI of three 5-minute bins of sleep. Agreement between the three SWI methods were analyzed using several statistical tools and methods.

RESULTS

Using an SWI of 50% as a diagnostic cutoff, 57% of records had a diagnosis of ESES based on the first 100 seconds of sleep. Fifty-four percent of records had a diagnosis of ESES based on the method of using the SWI of three bins. This resulted in a diagnostic accuracy of 92%, sensitivity of 96%, and specificity of 88%. Positive predictive values of children diagnosed with ESES using the first 100 seconds of sleep, compared with 3 combined bins, was determined to be 90% and a negative predictive value was determined to be 95%.

CONCLUSIONS

This analysis confirmed the diagnostic accuracy of using the SWI of the first 100 seconds of sleep and the cumulative total of three 5-minute bins.

Is sleep captured during a standard daytime EEG sufficient to diagnose Electrical Status Epilepticus in Sleep

Electrical Status epilepticus of sleep (SES) is an EEG pattern where there is significant activation of epileptiform activity in NREM sleep. A spike wave index (SWI) of > 80-85% is often labelled as typical SES. We aimed to explore if sleep during a standard daytime-EEG, as compared an overnight-EEG, was adequate to diagnose ESES. Ten children with daytime and overnight studies suggestive of SES were audited. SWI and Spike Wave Density (SWD) were calculated for 5-minute epochs of wake in the daytime and overnight study, as well daytime-EEG sleep and first and last NREM cycle in the overnight-EEG. SWI in daytime NREM was not significantly different from SWI in the first sleep cycle of the overnight study. SWI in the last sleep cycle was significantly lower than the first sleep cycle in the overnight-EEG. SWD was significantly higher in the first sleep cycle in the overnight-EEG than the daytime sleep and the last NREM cycle. SES may be diagnosed in NREM sleep from a daytime-EEG study. Larger studies are needed to explore the significance of the disparity between SWI and SWD in the first and last NREM cycles in the overnight study.

Relationship between brain activity, cognitive function, and sleep spiking activation in new-onset self-limited epilepsy with centrotemporal spikes

OBJECTIVE

This study aimed to investigate the relationship between cognitive function sleep spiking activation and brain activity in self-limited epilepsy with centrotemporal spikes (SeLECTS).

METHODS

We used spike-wave index (SWI), which means the percentage of the spike and slow wave duration to the total non-REM (NREM) sleep time, as the grouping standard. A total of 14 children with SeLECTS (SWI ≥ 50%), 21 children with SeLECTS (SWI < 50%), and 20 healthy control children were recruited for this study. Cognitive function was evaluated using the Wechsler Intelligence Scale for Children, Fourth Edition (Chinese version) (WISC-IV). Magnetic source activity was assessed using magnetoencephalography calculated for each frequency band using the accumulated source imaging (ASI) technique.

RESULTS

Children with SeLECTS (SWI ≥ 50%) had the lowest cognitive function scores, followed by those with SeLECTS (SWI < 50%) and then healthy controls. There were significant differences in the localization of magnetic source activity between the three groups: in the alpha (8-12 Hz) frequency band, children with SeLECTS (SWI ≥ 50%) showed deactivation of the medial frontal cortex (MFC) region; in the beta (12-30 Hz) frequency band, children with SeLECTS (SWI ≥ 50%) showed deactivation of the posterior cingulate cortex (PCC) segment; and in the gamma (30-80 Hz) frequency band, children in the healthy group showed activation of the PCC region.

CONCLUSION

This study revealed significant decreases in cognitive function in children with SeLECTS (SWI ≥ 50%) compared to children with SeLECTS (SWI < 50%) and healthy children, as well as significant differences in magnetic source activity between the three groups. The findings suggest that deactivation of magnetic source activity in the PCC and MFC regions is the main cause of cognitive function decline in SeLECTS patients with some frequency dependence.

Electrical Status Epilepticus during Sleep and Evaluating the Electroencephalogram

Electrical status epilepticus during sleep (ESES) is an age-related, self-limited epileptic encephalopathy characterized by heterogeneous clinical manifestations and a specific electroencephalographic pattern of continuous spikes and waves during slow sleep. The etiology of ESES is not completely clear, although structural brain lesions, abnormal immunological markers, and genetic mutations have been associated with the syndrome. ESES was first described in 1971 and since then, the diagnostic criteria have changed multiple times. Additionally, inconsistency between authors in how to record and evaluate the electroencephalogram also leads to variability between studies. These inconsistencies hamper objectivity, comparison, and generalization. Because of this, one of the first priorities of physicians treating this condition should be defining the parameters of this disease so that cooperative building can occur.

Determining the Spike-Wave Index Using Automated Detection Software

PURPOSE

The spike-wave index (SWI) is a key feature in the diagnosis of electrical status epilepticus during slow-wave sleep. Estimating the SWI manually is time-consuming and is subject to interrater and intrarater variability. Use of automated detection software would save time. Thereby, this software will consistently detect a certain EEG phenomenon as epileptiform and is not influenced by human factors. To determine noninferiority in calculating the SWI, we compared the performance of a commercially available spike detection algorithm (P13 software, Persyst Development Corporation, San Diego, CA) with human expert consensus.

METHODS

The authors identified all prolonged EEG recordings for the diagnosis or follow-up of electrical status epilepticus during slow-wave sleep carried out from January to December 2018 at an epilepsy tertiary referral center. The SWI during the first 10 minutes of sleep was estimated by consensus of two human experts. This was compared with the SWI calculated by the automated spike detection algorithm using the three available sensitivity settings: "low," "medium," and "high." In the software, these sensitivity settings are denoted as perception values.

RESULTS

Forty-eight EEG recordings from 44 individuals were analyzed. The SWIs estimated by human experts did not differ from the SWIs calculated by the automated spike detection algorithm in the "low" perception mode (P = 0.67). The SWIs calculated in the "medium" and "high" perception settings were, however, significantly higher than the human expert estimated SWIs (both P < 0.001).

CONCLUSIONS

Automated spike detection (P13) is a useful tool in determining SWI, especially when using the "low" sensitivity setting. Using such automated detection tools may save time, especially when reviewing larger epochs.

Reduced thalamic volume is strongly associated with electrical status epilepticus in sleep

To identify the relationship between thalamic volume and electrical status epilepticus in sleep (ESES). We analyzed subcortical gray matter volumes in patients with an ESES pattern on their electroencephalographs. All magnetic resonance imaging scans were considered within normal limits. The patients were not receiving antiepileptic drug at the time of the MRI study. High resolution T1-weighted 3-dimensional MPRAGE scans were assessed for segmentation and quantitative volumetric analysis of the brain by using the "volBrain" method. After correcting for total brain volume, volumes were compared with a group of healthy controls (HCs) and patients with benign childhood epilepsy with centrotemporal spikes (BECTS). Fifteen patients with ESES, 15 patients with BECTS, and 30 HCs were included. The median age of the patients with ESES was 8.5 (range, 5.8-13) years, 8 (range, 5-14) years for the HCs, and 7.8 (range, 4-13.5) years for the patients with BECTS. The total relative thalamic volume was significantly lower in patients with ESES than in the healthy controls (0.87 ± 0.07 vs. 0.93 ± 0.03, p = 0.002), and in patients with ESES than in those with BECTS (0.87 ± 0.07 vs. 0.93 ± 0.03, p = 0.006). There was no significant difference the HCs and patients with BECTS (0.93 ± 0.03 vs. 0.93 ± 0.03, p = 0.999). Both right and left relative thalamic volumes were lower in patients with ESES than in HCs (right thalamus: 0.43 ± 0.04 vs. 0.46 ± 0.02, p = 0.003, left thalamus: 0.44 ± 0.03 vs. 0.47 ± 0.02, p = 0.002), in patients with ESES than in patients with BECTS (right thalamus: 0.43 ± 0.04 vs. 0.46 ± 0.01, p = 0.01, left thalamus: 0.43 ± 0.04 vs. 0.47 ± 0.01, p = 0.007); however, there was no significant difference between the HCs and patients with BECTS (right thalamus: 0.46 ± 0.02 vs. 0.46 ± 0.01, p = 0.999, left thalamus: 0.47 ± 0.02 vs. 0.47 ± 0.01, p = 0.999). This study highlights the association between thalamic involvement and ESES, even when not severe enough to cause a detectable lesion on visual interpretation of MRI.

A qualitative awake EEG score for the diagnosis of continuous spike and waves during sleep (CSWS) syndrome in self-limited focal epilepsy (SFE): A case-control study

PURPOSE

To determine whether awake EEG criteria can differentiate epileptic encephalopathy with continuous spike and waves during sleep (EE-CSWS) at the time of cognitive regression from typical, self-limited focal epilepsy (SFE).

METHODS

This retrospective case-control study was based on the analysis of awake EEGs and included 15 patients with EE-CSWS and 15 age-matched and sex-matched patients with typical SFE. The EEGs were anonymised and scored by four independent readers. The following qualitative and quantitative EEG indices were analysed: slow-wave index (SLWI), spike-wave index (SWI), spike-wave frequency (SWF), long spike-wave clusters (CLSW) and EEG score (between grades 0 and 4). Sensitivity and specificity were assessed using receiver operating characteristic (ROC) curves and their reproducibility with a kappa test.

RESULTS

Based on a highly sensitive cut-off, EE-CSWS patients were 8.4 times more likely than those with SFE to have an SLWI > 6%, 15 times more likely to have an SWI > 10 % and six times more likely to have a CLSW of ≥ 1 s. There was substantial agreement between readers (with kappa values of 0.64, 0.69 and 0.67). EE-CSWS patients were 13 times more likely to have an SWF of > 11 % and 149 times more likely to have an EEG score of ≥ 3 than typical SFE patients. Agreement about these ratings was almost perfect (kappa 0.91 and 0.86).

CONCLUSION

An EEG score of ≥ 3 on a 20-min awake EEG differentiates typical SFE from EE-CSWS at the time of cognitive regression, with good reliability across readers with different levels of expertise.

Catalpol Exerts an Anti-Epilepticus Effect, Possibly by Regulating the Nrf2-Keap1-ARE Signaling Pathway

Background

Status epilepticus (SE) is a refractory neurological disease with high mortality and morbidity rates. SE can be induced by numerous factors, including oxidative stress. Catalpol has several biological activities, including regulating the oxidative stress response. However, the role of catapol in SE has not been fully elucidated.

Material/Methods

Thirty Wistar rats were randomly and equally divided into 3 groups: a control group, an SE group established by LiCl-pilocarpine intraperitoneal injection, and an SE+catalpol group established administering catalpol to SE rats. Epileptic seizure level and after-discharge duration (ADD) were analyzed. Cognitive function was assessed by Morris water maze. Myeloperoxidase (MPO) and superoxide dismutase (SOD) activities were tested. Keap1 and ARE mRNA expressions were detected by real-time PCR. Nrf2 protein expression was determined by Western blot.

Results

Catalpol significantly decreased epileptic seizure level, extended ADD, and improved cognitive function compared with the SE group (P<0.05). MPO was increased, SOD was reduced, Keap1 mRNA was upregulated, and Nrf2 protein and ARE mRNA were reduced in the SE group compared with the control group (P<0.05). Catalpol markedly decreased MPO, enhanced SOD activity, decreased Keap1 mRNA level, and elevated Nrf2 protein and ARE mRNA expressions compared with the SE group (P<0.05).

Conclusions

Catalpol plays an anti-epileptic role and improves cognitive function by regulating the Nrf2-Keap1-ARE signaling pathway to inhibit oxidative stress response.

Semiautomated Spike Detection Software Persyst 13 Is Noninferior to Human Readers When Calculating the Spike-Wave Index in Electrical Status Epilepticus in Sleep

PURPOSE

Our objective was to use semiautomatic methods for calculating the spike-wave index (SWI) in electrical status epilepticus in slow-wave sleep (ESES) and to determine whether this calculation is noninferior to human experts (HEs).

METHODS

Each HE marked identical 300-second epochs for all spikes and calculated the SWI in sleep EEGs of patients diagnosed with ESES. Persyst 13 was used to mark spikes (high sensitivity setting) in the same 300-second epochs marked by HEs. The spike-wave index was calculated. Pairwise HE differences and pairwise Persyst 13 (P13)-HE differences for the SWI were calculated. Bootstrap resampling (BCa, N = 3,000) was performed to better estimate mean differences and their 95% confidence bounds between HE and P13-HE pairs. Potential noninferiority of P13 to HEs was tested by comparing the 95% confidence bounds of the mean differences between pairs for the SWI.

RESULTS

Twenty EEG records were analyzed. Each HE marked 100 minutes of EEG. HEs 1, 2, 3, and 4 marked 10,075, 8,635, 9,710, and 9,898 spikes, respectively. The highest and lowest 95% confidence bound of the mean difference in the SWI between HE pairs was: High: 10.3%; Low: -10.2%. Highest and lowest 95% confidence bound of the mean difference in the SWI between P13 and HE pairings was as follows: high, 9.5% and low, -6.7%. The lack of a difference between P13 and HEs supports that the algorithm is not inferior to HEs.

CONCLUSIONS

Persyst 13 is noninferior to HEs in calculating the SWI in ESES, thus suggesting that an automated approach to SWI calculation may be a useful clinical tool.