Postictal Generalized EEG Suppression Research Articles

Effect of adenosinergic manipulations on amygdala-kindled seizures in mice: Implications for sudden unexpected death in epilepsy.

Sudden unexpected death in epilepsy (SUDEP) results in more years of potential life lost than any neurological condition with the exception of stroke. It is generally agreed that SUDEP happens due to some form of respiratory, cardiac, and electrocerebral dysfunction following a seizure; however, the mechanistic cause of these perturbations is unclear. One possible explanation lies with adenosinergic signaling. Extracellular levels of the inhibitory neuromodulator adenosine rapidly rise during seizures, a countermeasure that is necessary for seizure termination. Previous evidence has suggested that excessive adenosinergic inhibition could increase the risk of SUDEP by silencing brain areas necessary for life, such as the respiratory nuclei of the brainstem. The goal of this investigation was to further clarify the role of adenosine in seizure-induced respiratory and electrocerebral dysfunction. To determine the role of adenosine in postictal physiological dysregulation, we pharmacologically manipulated adenosine signaling prior to amygdala-kindled seizures in mice while recording electroencephalogram (EEG), electromyogram, and breathing using whole body plethysmography. The adenosinergic drugs used in this study included selective and nonselective adenosine receptor antagonists and inhibitors of adenosine metabolism. We found that high doses of adenosine receptor antagonists caused some seizures to result in seizure-induced death; however, counterintuitively, animals in these conditions that did not experience seizure-induced death had little or no postictal generalized EEG suppression. Inhibitors of adenosine metabolism had no effect on postictal breathing but did worsen some postictal electrocerebral outcomes. The unexpected effect of high doses of adenosine antagonists on seizure-induced death observed in this study may be due to the increase in seizure severity, vasoconstriction, or phosphodiesterase inhibition caused by these drugs at high doses. These findings further clarify the role of adenosine in seizure-induced death and may have implications for the consumption of caffeine in epilepsy patients and the prevention of SUDEP.

Effect of adenosinergic manipulations on amygdala-kindled seizures in mice: Implications for sudden unexpected death in epilepsy.

Sudden unexpected death in epilepsy (SUDEP) results in more years of potential life lost than any neurological condition with the exception of stroke. It is generally agreed that SUDEP happens due to some form of respiratory, cardiac, and electrocerebral dysfunction following a seizure; however, the mechanistic cause of these perturbations is unclear. One possible explanation lies with adenosinergic signaling. Extracellular levels of the inhibitory neuromodulator adenosine rapidly rise during seizures, a countermeasure that is necessary for seizure termination. Previous evidence has suggested that excessive adenosinergic inhibition could increase the risk of SUDEP by silencing brain areas necessary for life, such as the respiratory nuclei of the brainstem. The goal of this investigation was to further clarify the role of adenosine in seizure-induced respiratory and electrocerebral dysfunction. To determine the role of adenosine in postictal physiological dysregulation, we pharmacologically manipulated adenosine signaling prior to amygdala-kindled seizures in mice while recording electroencephalogram (EEG), electromyogram, and breathing using whole body plethysmography. The adenosinergic drugs used in this study included selective and nonselective adenosine receptor antagonists and inhibitors of adenosine metabolism. We found that high doses of adenosine receptor antagonists caused some seizures to result in seizure-induced death; however, counterintuitively, animals in these conditions that did not experience seizure-induced death had little or no postictal generalized EEG suppression. Inhibitors of adenosine metabolism had no effect on postictal breathing but did worsen some postictal electrocerebral outcomes. The unexpected effect of high doses of adenosine antagonists on seizure-induced death observed in this study may be due to the increase in seizure severity, vasoconstriction, or phosphodiesterase inhibition caused by these drugs at high doses. These findings further clarify the role of adenosine in seizure-induced death and may have implications for the consumption of caffeine in epilepsy patients and the prevention of SUDEP.

Progressive generalized tonic-clonic seizures in a transgenic mouse model of adult-onset epilepsy: Implications for morphological changes in cortico-limbic and brainstem structures

Video/cortical electroencephalography (EEG) is monitored to assess progressive severity of generalized tonic clonic seizures (GTCSs) in a transgenic mouse model of adult-onset epilepsy with increased death risk. The mice overexpress the brain derived neurotrophic factor (BDNF) in the forebrain under the calcium/calmodulin dependent protein kinase 2a (termed TgBDNF) and develop GTCSs in response to tail suspension/cage agitation stimulation at 3–4 months of age. With successive GTCSs (a total of 16 across 10 weeks of assessment), seizures became more severe as evidenced by increased duration of postictal generalized EEG suppression (PGES) associated with loss of posture/consciousness. Mice also developed spike wave discharges with behavioral arrest during the seizure recovery that increased in duration as a function of number of GTCSs. Overall seizure duration (from preictal spike to offset of PGES) and ictal spectral power (full spectra) were also increased. Half of the TgBDNF mice expired following a long period of PGES at the last recorded GTCS. Seizure-evoked general arousal impairment was associated with a striking decrease in total number of gigantocellular neurons of the brainstem nucleus pontis oralis along with increase in volumes of the anterior cingulate cortex and dorsal dentate gyrus in severely convulsive TgBDNF mice compared to litter-matched WT controls and non-convulsive TgBDNF mice. The latter effect was accompanied with an increase in total number of hippocampal granule neurons. These results provide structure-function associations in an animal model of adult-onset GTCSs that progressively increase in severity with clinical relevance for sudden unexpected death following generalized seizures.

Automated Detection of Central-positive Complexes & Postictal Generalized EEG Suppression in ECT-induced Seizures – An Illustrative Case Report (P10-1.009)

<h3>Objective:</h3> We present a case of using automated algorithms to analyze central-positive complexes (CPCs) and postictal generalized electroencephalographic suppression (PGES) in the context of ECT-induced seizures. Our goal is to characterize peri-ictal EEG changes in ECT-induced seizures to define their unique features as generalized seizures. <h3>Background:</h3> Generalized seizures induced by electroconvulsive therapy (ECT) have been proposed as a model to study epileptic seizures in humans. We recently described generalized, high-amplitude waveforms with maximum positive voltage over the vertex (CPCs) during ECT-induced seizures. Furthermore, we developed an automated algorithm for detecting postictal PGES, defined as electroencephalographic (EEG) activity of ≤10 mV following a generalized seizure. <h3>Design/Methods:</h3> This investigation is a secondary analysis of data collected from Reconstructing Consciousness and Cognition Phase 2 study (ClinicalTrials.gov NCT02761330). A 53-year-old female with a diagnosis of major depressive disorder underwent 5 sessions of right unilateral ECT during concurrent 64-channel EEG recordings. Quantitative EEG analysis allowed for assessment of spatiotemporal properties and spectral content during the peri-ictal period. CPC and PGES durations, quantified by automated algorithms, were compared to those derived through visual interpretation by expert epileptologists. <h3>Results:</h3> CPCs occurred during Phase III of all recorded seizures, with a median duration of 41 seconds. CPC evolved from 4.1 to 3.2 Hz as the seizures progressed. Peak-amplitude CPC scalp topology was consistent across patient’s seizures, showing maximal positive polarity over the vertex and surrounding regions and maximal negative polarity over the subocular electrodes. PGES duration lasted 5–45 seconds (median of 22 seconds) following the seizure and the median time to responsiveness after the seizure was 18.3 minutes. The findings using the automated algorithm were comparable to manual visual interpretation. <h3>Conclusions:</h3> This case presents the utility of automated algorithms to analyze CPCs and PGES characteristics, many of which were comparable to those evaluated through visual EEG interpretation. <b>Disclosure:</b> Dr. Nayak has nothing to disclose. Dr. Kafashan has nothing to disclose. Dr. Nascimento has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Zogenix. The institution of Dr. Nascimento has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Biocodex. Dr. Nascimento has received personal compensation in the range of $500-$4,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for AAN. The institution of Dr. Nascimento has received research support from Zogenix. The institution of Dr. Nascimento has received research support from Biocodex. The institution of Dr. Nascimento has received research support from Greenwich. Dr. Nascimento has received publishing royalties from a publication relating to health care. Dr. Nascimento has a non-compensated relationship as a Editorial Team Member with Neurology RFS that is relevant to AAN interests or activities. Dr. Nascimento has a non-compensated relationship as a Production Team with Neurology Podcast that is relevant to AAN interests or activities. The institution of Dr. Palanca has received research support from National Institutes of Health. The institution of Dr. Palanca has received research support from McDonnell Center for Systems Neuroscience. The institution of Dr. Palanca has received research support from American Federation for Suicide Prevention. Dr. Palanca has received intellectual property interests from a discovery or technology relating to health care. Dr. Palanca has received personal compensation in the range of $100,000-$499,999 for serving as a Employee with Washington University School of Medicine in St. Louis. Dr. Hogan has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Neurelis. The institution of Dr. Hogan has received research support from Biogen Inc. The institution of Dr. Hogan has received research support from Engage Therapeutics. The institution of Dr. Hogan has received research support from Otsuka Pharmaceuticals. The institution of Dr. Hogan has received research support from Cerevel Therapeutics.

A hybrid unsupervised and supervised learning approach for postictal generalized EEG suppression detection.

Sudden unexpected death of epilepsy (SUDEP) is a catastrophic and fatal complication of epilepsy and is the primary cause of mortality in those who have uncontrolled seizures. While several multifactorial processes have been implicated including cardiac, respiratory, autonomic dysfunction leading to arrhythmia, hypoxia, and cessation of cerebral and brainstem function, the mechanisms underlying SUDEP are not completely understood. Postictal generalized electroencephalogram (EEG) suppression (PGES) is a potential risk marker for SUDEP, as studies have shown that prolonged PGES was significantly associated with a higher risk of SUDEP. Automated PGES detection techniques have been developed to efficiently obtain PGES durations for SUDEP risk assessment. However, real-world data recorded in epilepsy monitoring units (EMUs) may contain high-amplitude signals due to physiological artifacts, such as breathing, muscle, and movement artifacts, making it difficult to determine the end of PGES. In this paper, we present a hybrid approach that combines the benefits of unsupervised and supervised learning for PGES detection using multi-channel EEG recordings. A K-means clustering model is leveraged to group EEG recordings with similar artifact features. We introduce a new learning strategy for training a set of random forest (RF) models based on clustering results to improve PGES detection performance. Our approach achieved a 5-second tolerance-based detection accuracy of 64.92%, a 10-second tolerance-based detection accuracy of 79.85%, and an average predicted time distance of 8.26 seconds with 286 EEG recordings using leave-one-out (LOO) cross-validation. The results demonstrated that our hybrid approach provided better performance compared to other existing approaches.

Quiescence during burst suppression and postictal generalized EEG suppression are distinct patterns of activity

ObjectivePeriods of low-amplitude electroencephalographic (EEG) signal (quiescence) are present during both anesthetic-induced burst suppression (BS) and postictal generalized electroencephalographic suppression (PGES). PGES following generalized seizures induced by electroconvulsive therapy (ECT) has been previously linked to antidepressant response. The commonality of quiescence during both BS and PGES motivated trials to recapitulate the antidepressant effects of ECT using high doses of anesthetics. However, there have been no direct electrographic comparisons of these quiescent periods to address whether these are distinct entities. MethodsWe compared periods of EEG quiescence recorded from two human studies: BS induced in 29 healthy adult volunteers by isoflurane general anesthesia and PGES in 11 patients undergoing right unilateral ECT for treatment-resistant depression. An automated algorithm allowed detection of EEG quiescence based on a 10-microvolt amplitude threshold. Spatial, spectral, and temporal analyses compared quiescent epochs during BS and PGES. ResultsThe median (interquartile range) voltage for quiescent periods during PGES was greater than during BS (1.81 (0.22) microvolts vs 1.22 (0.33) microvolts, p < 0.001). Relative power was greater for quiescence during PGES than BS for the 1–4 Hz delta band (p < 0.001), at the expense of power in the theta (4–8 Hz, p < 0.001), beta (13–30 Hz, p = 0.04) and gamma (30–70 Hz, p = 0.006) frequency bands. Topographic analyses revealed that amplitude across the scalp was consistently higher for quiescent periods during PGES than BS, whose voltage was within the noise floor. ConclusionsQuiescent epochs during PGES and BS have distinct patterns of EEG signals across voltage, frequency, and spatial domains. SignificanceQuiescent epochs during PGES and BS, important neurophysiological markers for clinical outcomes, are shown to have distinct voltage and frequency characteristics.

Abnormal heart rate variability during non-REM sleep and postictal generalized EEG suppression in focal epilepsy

ObjectiveTo identify any relationship between abnormal interictal heart rate variability (HRV) during sleep and the occurrence of postictal generalized EEG suppression (PGES), a potential biomarker of sudden unexpected death in epilepsy (SUDEP), in patients with focal epilepsy. MethodsWe retrospectively reviewed 34 consecutive patients with focal epilepsy, who presented with focal to bilateral tonic-clonic seizures (FBTCSs), 19 with PGES (PGES+) and 15 without PGES (PGES−), and 14 patients without epilepsy as controls. HRV spectrum analysis was performed for periods of 10 minutes during wakefulness, non-REM sleep, and REM sleep. HRV spectra consisted of high frequency (HF) and low frequency (LF) regions. Normalized HF representing parasympathetic activities and normalized LF were compared between the PGES+, PGES−, and control groups for each sleep stage. ResultsThe PGES+ group showed significantly decreased normalized HF and increased normalized LF during non-REM sleep compared to the PGES− (P < 0.05) and control (P < 0.01) groups, but not during wakefulness or REM sleep. ConclusionsAbnormal interictal HRV during non-REM sleep, indicating abnormally decreased parasympathetic activities, was associated with PGES in patients with FBTCS. SignificanceThis study proposes to further investigate the relation between decreased parasympathetic activities and PGES.

Duration of postictal impaired awareness after bilateral tonic-clonic seizures: EEG and patient characteristics

ObjectiveIctal and postictal phenomena that may impact the duration of postictal impaired awareness have not been well studied. Postictal unresponsiveness invariably occurs following bilateral tonic-clonic seizures (BTCS). Bilateral tonic-clonic seizures are a major risk factor for sudden unexpected death in epilepsy (SUDEP). We quantify the effects of seizure characteristics on postictal recovery of awareness following BTCS. Factors include: the total seizure duration, the duration of the tonic phase of a BTCS, presence of postictal generalized EEG suppression (PGES), duration of postictal tonic electromyographic discharge, peri-ictal respiratory dysfunction, patient age, duration of epilepsy, and gender. MethodsFifty-eight patients admitted to the epilepsy monitoring unit with BTCS were studied. Forty-one had unilateral onset temporal seizures. The remainder had bitemporal onsets, extratemporal onsets, undetermined onsets, or were generalized at onset. Following the first BTCS, time to initial recovery of awareness and its possible association with patient and seizure characteristics as well as peri-ictal respiratory dysfunction were evaluated. The presence or absence of postictal agitation was noted. ResultsThe severity of respiratory dysfunction and seizure characteristics were not associated with time to initial recovery of awareness. A shorter time to recovery of awareness was significantly associated with a younger age (p = 0.007). Postictal agitation was more common in males (p = 0.023). SignificanceFocal seizures may impair awareness by active inhibition of subcortical arousal mechanisms. Focal seizures progressing to bilateral tonic-clonic seizures (BTCS) result in further widespread cerebral dysfunction impacting postictal awareness. MRI studies show accelerated brain aging in patients with temporal lobe epilepsy. Our findings suggest that patient age, as a surrogate marker for the lifetime burden of seizures, results in a progressive worsening in time to recovery after BTCS by an increasing negative impact on networks involved in arousal.

Heart rate variability in patients with refractory epilepsy: The influence of generalized convulsive seizures

ObjectivePatients with epilepsy, mainly drug-resistant, have reduced heart rate variability (HRV), linked to an increased risk of sudden death in various other diseases. In this context, it could play a role in SUDEP. Generalized convulsive seizures (GCS) are one of the most consensual risk factors for SUDEP. Our objective was to assess the influence of GCS in HRV parameters in patients with drug-resistant epilepsy. MethodsWe prospectively evaluated 121 patients with refractory epilepsy admitted to our Epilepsy Monitoring Unit. All patients underwent a 48-hour Holter recording. Only patients with GCS were included (n = 23), and we selected the first as the index seizure. We evaluated HRV (AVNN, SDNN, RMSSD, pNN50, LF, HF, and LF/HF) in 5-min epochs (diurnal and nocturnal baselines; preictal – 5 min before the seizure; ictal; postictal – 5 min after the seizure; and late postictal – >5 h after the seizure). These data were also compared with normative values from a healthy population (controlling for age and gender). ResultsWe included 23 patients, with a median age of 36 (min-max, 16–55) years and 65% were female. Thirty percent had cardiovascular risk factors, but no previously known cardiac disease. HRV parameters AVNN, RMSSD, pNN50, and HF were significantly lower in the diurnal than in the nocturnal baseline, whereas the opposite occurred with LF/HF and HR. Diurnal baseline parameters were inferior to the normative population values (which includes only diurnal values). We found significant differences in HRV parameters between the analyzed periods, especially during the postictal period. All parameters but LF/HF suffered a reduction in that period. LF/HF increased in that period but did not reach statistical significance. Visually, there was a tendency for a global reduction in our patients’ HRV parameters, namely AVNN, RMSSD, and pNN50, in each period, comparing with those from a normative healthy population. No significant differences were found in HRV between diurnal and nocturnal seizures, between temporal lobe and extra-temporal-lobe seizures, between seizures with and without postictal generalized EEG suppression, or between seizures of patients with and without cardiovascular risk factors. Significance/conclusionOur work reinforces the evidence of autonomic cardiac dysfunction in patients with refractory epilepsy, at baseline and mainly in the postictal phase of a GCS. Those changes may have a role in some SUDEP cases. By identifying patients with worse autonomic cardiac function, HRV could fill the gap of a lacking SUDEP risk biomarker.

The Good, the Bad, and the Deadly: Adenosinergic Mechanisms Underlying Sudden Unexpected Death in Epilepsy.

Adenosine is an inhibitory modulator of neuronal excitability. Neuronal activity results in increased adenosine release, thereby constraining excessive excitation. The exceptionally high neuronal activity of a seizure results in a surge in extracellular adenosine to concentrations many-fold higher than would be observed under normal conditions. In this review, we discuss the multifarious effects of adenosine signaling in the context of epilepsy, with emphasis on sudden unexpected death in epilepsy (SUDEP). We describe and categorize the beneficial, detrimental, and potentially deadly aspects of adenosine signaling. The good or beneficial characteristics of adenosine signaling in the context of seizures include: (1) its direct effect on seizure termination and the prevention of status epilepticus; (2) the vasodilatory effect of adenosine, potentially counteracting postictal vasoconstriction; (3) its neuroprotective effects under hypoxic conditions; and (4) its disease modifying antiepileptogenic effect. The bad or detrimental effects of adenosine signaling include: (1) its capacity to suppress breathing and contribute to peri-ictal respiratory dysfunction; (2) its contribution to postictal generalized EEG suppression (PGES); (3) the prolonged increase in extracellular adenosine following spreading depolarization waves may contribute to postictal neuronal dysfunction; (4) the excitatory effects of A2A receptor activation is thought to exacerbate seizures in some instances; and (5) its potential contributions to sleep alterations in epilepsy. Finally, the adverse effects of adenosine signaling may potentiate a deadly outcome in the form of SUDEP by suppressing breathing and arousal in the postictal period. Evidence from animal models suggests that excessive postictal adenosine signaling contributes to the pathophysiology of SUDEP. The goal of this review is to discuss the beneficial, harmful, and potentially deadly roles that adenosine plays in the context of epilepsy and to identify crucial gaps in knowledge where further investigation is necessary. By better understanding adenosine dynamics, we may gain insights into the treatment of epilepsy and the prevention of SUDEP.

Automated Analysis of Risk Factors for Postictal Generalized EEG Suppression.

Rationale: Currently, there is some ambiguity over the role of postictal generalized electro-encephalographic suppression (PGES) as a biomarker in sudden unexpected death in epilepsy (SUDEP). Visual analysis of PGES, known to be subjective, may account for this. In this study, we set out to perform an analysis of PGES presence and duration using a validated signal processing tool, specifically to examine the association between PGES and seizure features previously reported to be associated with visually analyzed PGES.Methods: This is a prospective, multicenter epilepsy monitoring study of autonomic and breathing biomarkers of SUDEP in adult patients with intractable epilepsy. We studied videoelectroencephalogram (vEEG) recordings of generalized convulsive seizures (GCS) in a cohort of patients in whom respiratory and vEEG recording were carried out during the evaluation in the epilepsy monitoring unit. A validated automated EEG suppression detection tool was used to determine presence and duration of PGES.Results: We studied 148 GCS in 87 patients. PGES occurred in 106/148 (71.6%) seizures in 70/87 (80.5%) of patients. PGES mean duration was 38.7 ± 23.7 (37; 1–169) seconds. Presence of tonic phase during GCS, including decerebration, decortication and hemi-decerebration, were 8.29 (CI 2.6–26.39, p = 0.0003), 7.17 (CI 1.29–39.76, p = 0.02), and 4.77 (CI 1.25–18.20, p = 0.02) times more likely to have PGES, respectively. In addition, presence of decerebration (p = 0.004) and decortication (p = 0.02), older age (p = 0.009), and hypoxemia duration (p = 0.03) were associated with longer PGES durations.Conclusions: In this study, we confirmed observations made with visual analysis, that presence of tonic phase during GCS, longer hypoxemia, and older age are reliably associated with PGES. We found that of the different types of tonic phase posturing, decerebration has the strongest association with PGES, followed by decortication, followed by hemi-decerebration. This suggests that these factors are likely indicative of seizure severity and may or may not be associated with SUDEP. An automated signal processing tool enables objective metrics, and may resolve apparent ambiguities in the role of PGES in SUDEP and seizure severity studies.

Proteomics and Transcriptomics of the Hippocampus and Cortex in SUDEP and High-Risk SUDEP Patients.

ObjectiveTo identify the molecular signaling pathways underlying sudden unexpected death in epilepsy (SUDEP) and high-risk SUDEP compared to control patients with epilepsy.MethodsFor proteomics analyses, we evaluated the hippocampus and frontal cortex from microdissected postmortem brain tissue of 12 patients with SUDEP and 14 with non-SUDEP epilepsy. For transcriptomics analyses, we evaluated hippocampus and temporal cortex surgical brain tissue from patients with mesial temporal lobe epilepsy: 6 low-risk and 8 high-risk SUDEP as determined by a short (<50 seconds) or prolonged (≥50 seconds) postictal generalized EEG suppression (PGES) that may indicate severely depressed brain activity impairing respiration, arousal, and protective reflexes.ResultsIn autopsy hippocampus and cortex, we observed no proteomic differences between patients with SUDEP and those with non-SUDEP epilepsy, contrasting with our previously reported robust differences between epilepsy and controls without epilepsy. Transcriptomics in hippocampus and cortex from patients with surgical epilepsy segregated by PGES identified 55 differentially expressed genes (37 protein-coding, 15 long noncoding RNAs, 3 pending) in hippocampus.ConclusionThe SUDEP proteome and high-risk SUDEP transcriptome were similar to those in other patients with epilepsy in hippocampus and cortex, consistent with diverse epilepsy syndromes and comorbid conditions associated with SUDEP. Studies with larger cohorts and different epilepsy syndromes, as well as additional anatomic regions, may identify molecular mechanisms of SUDEP.

Origin of post-ictal and post-anesthesia adverse effects and possibly of SUDEP

The origin of post-ictal malfunctions is debatable. We want to propose a novel idea of a cause of these adverse results occurring following epileptic seizures and anesthesia. Previously we have put forward the idea that epileptic seizures termination is caused by the function of the glymphatic system in the brain. A new measurement shows that this system can be much faster than what was estimated before. Moreover, the method enabling this speeding was actually measured in brains of epilepsy subjects. So, the main objection to our model is relegated. As a possible consequence of the glymphatic process, there can be an excess cleaning of the brain's interstitial fluid. We discuss possible adverse results of this process. This over-cleaning (that can, to a lower extent, occur also during anesthesia) which results post-ictally from the previous overexpression of fluid materials by the neurons during their seizure operation, can reduce ingredients essential for regular neuronal functioning, thereby leading to function reduction and EEG suppression which last until those materials are replenished. We argue that this ingredients' scarcity is the cause of post-ictal generalized EEG suppression (PGES), of post-ictal immobility (PI) and possibly of Sudden Unexpected Death in Epilepsy Patients (SUDEP). Similarly, such cleaning can lead to morbidity and even mortality problems following anesthesia.If our assumption is correct, this understanding of the process of the problems' origin can lead to a method to remedy them by judicial supplement of the lost materials.

Automated detection of activity onset after postictal generalized EEG suppression

BackgroundSudden unexpected death in epilepsy (SUDEP) is a leading cause of premature death in patients with epilepsy. If timely assessment of SUDEP risk can be made, early interventions for optimized treatments might be provided. One of the biomarkers being investigated for SUDEP risk assessment is postictal generalized EEG suppression [postictal generalized EEG suppression (PGES)]. For example, prolonged PGES has been found to be associated with a higher risk for SUDEP. Accurate characterization of PGES requires correct identification of the end of PGES, which is often complicated due to signal noise and artifacts, and has been reported to be a difficult task even for trained clinical professionals. In this work we present a method for automatic detection of the end of PGES using multi-channel EEG recordings, thus enabling the downstream task of SUDEP risk assessment by PGES characterization.MethodsWe address the detection of the end of PGES as a classification problem. Given a short EEG snippet, a trained model classifies whether it consists of the end of PGES or not. Scalp EEG recordings from a total of 134 patients with epilepsy are used for training a random forest based classification model. Various time-series based features are used to characterize the EEG signal for the classification task. The features that we have used are computationally inexpensive, making it suitable for real-time implementations and low-power solutions. The reference labels for classification are based on annotations by trained clinicians identifying the end of PGES in an EEG recording.ResultsWe evaluated our classification model on an independent test dataset from 34 epileptic patients and obtained an AUreceiver operating characteristic (ROC) (area under the curve) of 0.84. We found that inclusion of multiple EEG channels is important for better classification results, possibly owing to the generalized nature of PGES. Of among the channels included in our analysis, the central EEG channels were found to provide the best discriminative representation for the detection of the end of PGES.ConclusionAccurate detection of the end of PGES is important for PGES characterization and SUDEP risk assessment. In this work, we showed that it is feasible to automatically detect the end of PGES—otherwise difficult to detect due to EEG noise and artifacts—using time-series features derived from multi-channel EEG recordings. In future work, we will explore deep learning based models for improved detection and investigate the downstream task of PGES characterization for SUDEP risk assessment.

Categorisation of EEG suppression using enhanced feature extraction for SUDEP risk assessment

BackgroundSudden Unexpected Death in Epilepsy (SUDEP) has increased in awareness considerably over the last two decades and is acknowledged as a serious problem in epilepsy. However, the scientific community remains unclear on the reason or possible bio markers that can discern potentially fatal seizures from other non-fatal seizures. The duration of postictal generalized EEG suppression (PGES) is a promising candidate to aid in identifying SUDEP risk. The length of time a patient experiences PGES after a seizure may be used to infer the risk a patient may have of SUDEP later in life. However, the problem becomes identifying the duration, or marking the end, of PGES (Tomson et al. in Lancet Neurol 7(11):1021–1031, 2008; Nashef in Epilepsia 38:6–8, 1997).MethodsThis work addresses the problem of marking the end to PGES in EEG data, extracted from patients during a clinically supervised seizure. This work proposes a sensitivity analysis on EEG window size/delay, feature extraction and classifiers along with associated hyperparameters. The resulting sensitivity analysis includes the Gradient Boosted Decision Trees and Random Forest classifiers trained on 10 extracted features rooted in fundamental EEG behavior using an EEG specific feature extraction process (pyEEG) and 5 different window sizes or delays (Bao et al. in Comput Intell Neurosci 2011:1687–5265, 2011).ResultsThe machine learning architecture described above scored a maximum AUC score of 76.02% with the Random Forest classifier trained on all extracted features. The highest performing features included SVD Entropy, Petrosan Fractal Dimension and Power Spectral Intensity.ConclusionThe methods described are effective in automatically marking the end to PGES. Future work should include integration of these methods into the clinical setting and using the results to be able to predict a patient’s SUDEP risk.

Sudden unexpected death after acute symptomatic seizures in a patient on mechanical ventilation

BackgroundThe mechanism of sudden unexpected death in epilepsy remains poorly understood. Seizure induced cardiac arrhythmia, central and obstructive apneas have been proposed as possible causes of death. Here we report a unique case of seizure related sudden unexpected death in a patient whose airway was fully protected by intubation and mechanic ventilation in the absence of fatal cardiac arrhythmia.Case presentationA 70-year-old woman was undergoing mechanical ventilation and video-electroencephalography (EEG) monitoring following two convulsive seizures with ictal hypoventilation and hypoxemia. Several hours after intubation, she suffered another generalized tonic clonic seizure lasted for 3 min and developed postictal generalized EEG suppression in the presence of stable vital signs with SpO2 > 90%. EEG suppression persisted throughout the postictal phase. There was a significant fluctuation of systolic blood pressure between 50 and 180 mmHg with several bouts of hypotension < 60 mmHg. She remained unresponsive after the convulsive seizure and died of diffuse cerebral edema 12 h later. Autopsy revealed no clear cause of death, except for possible hypoxic and ischemic injury leading to the diffuse cerebral edema.ConclusionGiven the reliable periictal airway protection, neither seizure induced central apnea nor obstructive apnea appeared to be the direct cause of death in this unique case. In the absence of fatal cardiac arrhythmia, diffuse cerebral edema secondary to seizure-induced autonomic dysfunction, hypotension and hypoxemia might be the cause of death, highlighting the etiological heterogeneity of sudden unexpected death in epilepsy.

Post-ictal Generalized EEG Suppression is reduced by Enhancing Dorsal Raphe Serotonergic Neurotransmission

Sudden unexpected death in epilepsy (SUDEP) is the leading cause of death in patients with refractory epilepsy. A proposed risk marker for SUDEP is the duration of post-ictal generalized EEG suppression (PGES). The mechanisms underlying PGES are unknown. Serotonin (5-HT) has been implicated in SUDEP pathophysiology. Seizures suppress activity of 5-HT neurons in the dorsal raphe nucleus (DRN). We hypothesized that suppression of DRN 5-HT neuron activity contributes to PGES and increasing 5-HT neurotransmission or stimulating the DRN before a seizure would decrease PGES duration. Adult C57BL/6J and Pet1-Cre mice received EEG/EMG electrodes, a bipolar stimulating/recording electrode in the right basolateral amygdala, and either a microdialysis guide cannula or an injection of adeno-associated virus (AAV) allowing expression of channelrhodopsin2 plus an optic fiber into the DRN. Systemic application of the selective 5-HT reuptake inhibitor citalopram (20 mg/kg) decreased PGES duration from seizures induced during wake (n = 23) and non-rapid eye movement (NREM) sleep (n = 13) whereas fluoxetine (10 mg/kg) pretreatment decreased PGES duration following seizures induced from wake (n = 11), but not NREM sleep (n = 9). Focal chemical (n = 6) or optogenetic (n = 8) stimulation of the DRN reduced PGES duration following seizures in kindled mice induced during wake. During PGES, animals exhibited immobility and suppression of EEG activity that was reduced by citalopram pretreatment. These results suggest 5-HT and the DRN may regulate PGES.

Cardiorespiratory and autonomic function in epileptic seizures: A video-EEG monitoring study

PurposeSeizure-induced cardiorespiratory and autonomic dysfunction has long been recognized, and growing evidence points to its implication in sudden unexpected death in epilepsy (SUDEP). However, a comprehensive understanding of cardiorespiratory function in the preictal, ictal, and postictal periods are lacking. MethodsWe examined continuous cardiorespiratory and autonomic function in 157 seizures (18 convulsive and 139 nonconvulsive) from 70 consecutive patients who had a seizure captured on concurrent video-encephalogram (EEG) monitoring and polysomnography between February 1, 2012 and May 31, 2017. Heart and respiratory rates, heart rate variability (HRV), and oxygen saturation were assessed across four distinct periods: baseline (120 s), preictal (60 s), ictal, and postictal (300 s). Heart and respiratory rates were further followed for up to 60 min after seizure termination to assess return to baseline. ResultsIctal tachycardia occurred during both convulsive and nonconvulsive seizures, but the maximum rate was higher for convulsive seizures (mean: 138.8 beats/min, 95% confidence interval (CI): 125.3–152.4) compared with nonconvulsive seizures (mean: 105.4 beats/min, 95% CI: 101.2–109.6; p < 0.001). Convulsive seizures were associated with a lower ictal minimum respiratory rate (mean: 0 breaths/min, 95% CI: 0–0) compared with nonconvulsive seizures (mean: 11.0 breaths/min, 95% CI: 9.5–12.6; p < 0.001). Ictal obstructive apnea was associated with convulsive compared with nonconvulsive seizures. The low-frequency (LF) power band of ictal HRV was higher among convulsive seizures than nonconvulsive seizures (ratio of means (ROM): 2.97, 95% CI: 1.34–6.60; p = 0.008). Postictal tachycardia was substantially prolonged, characterized by a longer return to baseline for convulsive seizures (median: 60.0 min, interquartile range (IQR): 46.5–60.0) than nonconvulsive seizures (median: 0.26 min, IQR: 0.008–0.9; p < 0.001). For postictal hyperventilation, the return to baseline was longer in convulsive seizures (median: 25.3 min, IQR: 8.1–60) than nonconvulsive seizures (median: 1.0 min, IQR: 0.07–3.2; p < 0.001). The LF power band of postictal HRV was lower in convulsive seizures than nonconvulsive seizures (ROM: 0.33, 95% CI: 0.11–0.96; p = 0.043). Convulsive seizures with postictal generalized EEG suppression (PGES; n = 12) were associated with lower postictal heart and respiratory rate, and increased HRV, compared with those without (n = 6). ConclusionsProfound cardiorespiratory and autonomic dysfunction associated with convulsive seizures may explain why these seizures carry the greatest risk of SUDEP.

Delta-gamma phase-amplitude coupling as a biomarker of postictal generalized EEG suppression.

Postictal generalized EEG suppression is the state of suppression of electrical activity at the end of a seizure. Prolongation of this state has been associated with increased risk of sudden unexpected death in epilepsy, making characterization of underlying electrical rhythmic activity during postictal suppression an important step in improving epilepsy treatment. Phase-amplitude coupling in EEG reflects cognitive coding within brain networks and some of those codes highlight epileptic activity; therefore, we hypothesized that there are distinct phase-amplitude coupling features in the postictal suppression state that can provide an improved estimate of this state in the context of patient risk for sudden unexpected death in epilepsy. We used both intracranial and scalp EEG data from eleven patients (six male, five female; age range 21–41 years) containing 25 seizures, to identify frequency dynamics, both in the ictal and postictal EEG suppression states. Cross-frequency coupling analysis identified that during seizures there was a gradual decrease of phase frequency in the coupling between delta (0.5–4 Hz) and gamma (30+ Hz), which was followed by an increased coupling between the phase of 0.5–1.5 Hz signal and amplitude of 30–50 Hz signal in the postictal state as compared to the pre-seizure baseline. This marker was consistent across patients. Then, using these postictal-specific features, an unsupervised state classifier—a hidden Markov model—was able to reliably classify four distinct states of seizure episodes, including a postictal suppression state. Furthermore, a connectome analysis of the postictal suppression states showed increased information flow within the network during postictal suppression states as compared to the pre-seizure baseline, suggesting enhanced network communication. When the same tools were applied to the EEG of an epilepsy patient who died unexpectedly, ictal coupling dynamics disappeared and postictal phase-amplitude coupling remained constant throughout. Overall, our findings suggest that there are active postictal networks, as defined through coupling dynamics that can be used to objectively classify the postictal suppression state; furthermore, in a case study of sudden unexpected death in epilepsy, the network does not show ictal-like phase-amplitude coupling features despite the presence of convulsive seizures, and instead demonstrates activity similar to postictal. The postictal suppression state is a period of elevated network activity as compared to the baseline activity which can provide key insights into the epileptic pathology.

CTnI, BNP and CRP profiling after seizures in patients with drug-resistant epilepsy

PurposeTo profile serum levels of high sensitivity Troponin I (hs-cTnI), B-Type Natriuretic Peptide (BNP), and high sensitivity C Reactive Protein (hs-CRP), after epileptic seizures in patients with focal drug-resistant epilepsy, relating the results to the revised SUDEP-7 inventory. MethodsWe prospectively evaluated patients admitted to our Epilepsy Monitoring Unit. hs-cTnI, BNP, and hs-CRP were measured at admission and after the first seizure. The revised SUDEP-7 Risk Inventory was calculated. The statistical significance level was set at 0.05. ResultsFifty-eight patients were included (53.4 % female). The index seizure was a focal to bilateral tonic-clonic seizure (FBTCS) in 25.9 % of the patients, and 17.5 % had post-ictal generalized EEG suppression (PGES). After the seizure, 25.9 % had a significant (above 50 %) increase in hs-cTnI, 23.3 % in BNP, and 4.3 % in hs-CRP. About 40 % had cardiovascular risk factors (CRF), without known cardiac disease. The elevation of one biomarker did not compel the elevation of another. hs-cTnI increase was associated with FBTCS, PGES, longer seizures, maximal ictal heart rate, and HR change. Increases in BNP were associated with CRF. hs-CRP increase was associated with PGES. We found no significant association between SUDEP-7 and any biomarker increase. SignificanceSeveral patients had increases in biomarkers of myocardial necrosis/dysfunction after seizures, without significant association with the SUDEP-7 inventory. Different patterns of biomarkers’ elevations point to multifactorial pathophysiologies hypothetically associated with incipient myocardial lesions. A larger cohort with follow-up data could help to clarify the clinical relevance of these findings.