Prevalence of benign epileptiform variants

14 and 6 Hz like spike wave activity is a common finding in young patients with Prader-Willi syndrome

STUDY OBJECTIVES

Our aim was to characterize the 14 and 6 like spike wave activity seen on electroencephalograms (EEG) in children with Prader-Willi syndrome (PWS) undergoing polysomnograms.

METHODS

We performed a retrospective review of children with PWS and healthy controls who underwent diagnostic polysomnograms between January 1, 2007, and December 31, 2020, at SickKids, Toronto, Canada. EEGs from the polysomnograms were reviewed for the presence of the 14 and 6 like spike wave activity and its characteristics. Clinical correlation of the EEG variant with sleep-disordered breathing indices from the polysomnograms was also evaluated.

RESULTS

A total of 94 children with PWS and 50 healthy controls were included. The median age and interquartile range for the cohort was 1.42 (0.6, 4.2) years. There were 50 (53.2%) males in the PWS cohort. The EEG variant prevalence in this cohort was 51.0% (n = 48) in children with PWS and 0% for the healthy controls. 14 and 6 Hz like spike wave activity was bilateral in 52% (25/48) children with PWS. The waves had a negative deflection in almost all patients, 44/48 (92%), with PWS. It was predominantly located in the frontal leads for children with PWS, 23/48 (47.9%). It most frequently occurred during non-rapid eye movement stage 2 sleep for children with PWS, 25/48 (52.0%). The mean (standard deviation) frequency was 6.8 (0.97) Hz. The median (interquartile range) length of the waves was 1.1 (0.8, 1.4) seconds in children with PWS. There was no correlation between the presence of the EEG variant and sleep-disordered breathing indices in children with PWS.

CONCLUSIONS

The 14 and 6 Hz like spike wave activity EEG variant was present in more than 50% of a pediatric cohort of children with PWS compared with 0% in healthy children. This EEG variant did not appear to be associated with sleep-disordered breathing indices in children with PWS and is of unknown clinical significance.

CITATION

Alzaid M, Sunkonkit K, Massicotte C, Otsubo H, Amin R, Al-Saleh S. 14 and 6 Hz like spike wave activity is a common finding in young patients with Prader-Willi syndrome. J Clin Sleep Med. 2024;20(8):1227-1232.

Benign epileptiform variants in EEG: A comprehensive study of 3000 patients

BACKGROUND

The analysis of EEG demands expertise and keen observation to distinguish epileptiform discharges from benign epileptiform variants (BEVs), a frequent source of erroneous interpretation. The prevalence of BEVs varies based on geographical, racial, and ethnic characteristics. However, most data on BEVs originates from Western populations, and additional studies on different cohorts would enrich the existing literature.

METHODS

We reviewed EEGs from our institutional database to study the prevalence of benign epileptiform variants and analyzed their frequency, topography, and other characteristics. Additionally, we investigated the co-existence of epileptiform discharges with BEVs.

RESULTS

We identified 296 patients with BEVs after reviewing 3000 EEGs (9.9%). The most common BEV was small sharp spikes (SSS), observed in 114 patients (3.8%). Wicket waves, 6 Hz spike and slow wave, 14 and 6 Hz positive bursts, and Rhythmic Temporal Theta of Drowsiness (RTTD) were identified in 67 (2.2%), 40 (1.3%), 39 (1.3%), and 35 (1.16%) patients, respectively and one patient with Subclinical Rhythmic EEG Discharges in Adults (SREDA). Additionally, we observed the co-existence of epileptiform discharges with BEVs, most commonly with SSS (27.8%).

CONCLUSIONS

The present study is a large study with 3000 EEGs to describe the BEV characteristics. BEVs were seen in 9.9% of patients, BSSS being the most common. There were minor differences in frequency, gender or age distribution compared to existing literature. We demonstrated the co-existence of epileptiform discharges. Morphological characteristics remain the cornerstone in recognising BEVs. EEG readers need to be aware of features of BEVs to avoid wrongly interpretation.

Overcoming traps and pitfalls leading to misinterpretation of normal EEG variants and variation of the background activity

Normal EEG variants, especially the epileptiform variants, can be challenging to interpret because they often have sharp contours and may be confused with "epileptic" interictal activities. However, they can be recognized by the fact that "most spikes or sharp wave discharges of clinical import are followed by a slow wave or a series of slow deflections" (Maulsby, 1971). If there is no wave after the spike, electroencephalographers should be suspicious of artifacts and normal EEG variants. Most normal EEG variants display a single rhythm with the same frequency within the pattern and the morphology remains stable throughout the entire EEG recording with repetition of the same pattern. In case of doubt or difficulties with a standard EEG, it is recommended to undergo an EEG that includes sleep stages with or without sleep deprivation. Finally, epileptiform is an ambiguous term corresponding to an electroencephalographic trait. Epileptiform does not imply a pathological condition, including epilepsy. The clinical context remains the most paramount in the diagnosis of epilepsy. In this article, we propose a set of rules and guidelines to identify normal EEG variants in EEG tracings and normal variation of the background activity. It is not easy to accurately assign a specific/precise name to all EEG activity, but with an orderly approach to EEG that involves using a set of criteria, nonepileptic activity can be identified.

Can epilepsy affect normal EEG variants? A comparative study between subjects with and without epilepsy

OBJECTIVES

To compare the prevalence of benign EEG variants (BEVs) between epileptic and non-epileptic subjects.

METHODS

A prospective, observational EEG study of 1,163 consecutive patients, using the 10-20 international system with systematically two additional anterior/inferior temporal electrodes. The video-EEG monitoring duration was between 24 h and eight days.

RESULTS

We identified 917 (78.9%) epileptic patients (mean age: 33.42 ± 15.5 years; females: 53.4%) and 246 (21.2%) non-epileptic patients (mean age: 35.6 ± 18.75 years; females: 54.9%). Despite a shorter mean duration of the EEG recordings, the prevalence of BEVs was higher in non-epileptic vs. epileptic patients (73.2% vs. 57.8%, p = 0.000011). This statistical difference was confirmed for lambda waves (23.6% in the non-epilepsy group vs. 14.8% in the epilepsy group, p = 0.001), POSTs (50.8% vs. 32.5%, p < 0.000001), wicket spikes (20.3% vs. 13.6%, p = 0.009) in particular in NREM and REM sleep, and 14- and 6-Hz positive bursts (13% vs. 7.1% p = 0.003). Mu rhythm was observed at the same frequency in both groups (21.1% in the non-epilepsy group vs. 22.7% in the epilepsy group). There was no difference between the two groups for rarer rhythms, such as rhythmic mid-temporal theta burst of drowsiness, small sharp spikes, and midline theta rhythm.

CONCLUSIONS

There was no increase in any of the BEVs in the epilepsy group. On the contrary, BEVs were more frequent and diversified in the non-epilepsy group. Epilepsy may negatively affect the occurrence of the most common BEVs, with the exception of the mu rhythm, which is present in about one-fifth of the population with or without epilepsy.

Four Atypical Cases of Subclinical Rhythmic Electroencephalogram Discharge of Adults: Case Series and Review

SUMMARY

Subclinical rhythmic EEG discharge of adults is an uncommon variant that represents a diagnostic challenge in the clinical practice because it can be mistaken for an electrographic seizure. We present a case series of four patients who underwent EEG because of suspicious events or an unclear medical history of epilepsy. In all cases, the EEG revealed atypical features including focal and asymmetric distribution, presentation during NREM and REM sleep, rhythmic activity in the delta range with a notched appearance and blocked by eyes opening.

Pitfalls in scalp EEG: Current obstacles and future directions

Although electroencephalography (EEG) serves a critical role in the evaluation and management of seizure disorders, it is commonly misinterpreted, resulting in avoidable medical, social, and financial burdens to patients and health care systems. Overinterpretation of sharply contoured transient waveforms as being representative of interictal epileptiform abnormalities lies at the core of this problem. However, the magnitude of these errors is amplified by the high prevalence of paroxysmal events exhibited in clinical practice that compel investigation with EEG. Neurology training programs, which vary considerably both in the degree of exposure to EEG and the composition of EEG didactics, have not effectively addressed this widespread issue. Implementation of competency-based curricula in lieu of traditional educational approaches may enhance proficiency in EEG interpretation amongst general neurologists in the absence of formal subspecialty training. Efforts in this regard have led to the development of a systematic, high-fidelity approach to the interpretation of epileptiform discharges that is readily employable across medical centers. Additionally, machine learning techniques hold promise for accelerating accurate and reliable EEG interpretation, particularly in settings where subspecialty interpretive EEG services are not readily available. This review highlights common diagnostic errors in EEG interpretation, limitations in current educational paradigms, and initiatives aimed at resolving these challenges.

Normal variants and artifacts: Importance in EEG interpretation

Overinterpretation of EEG is an important contributor to the misdiagnosis of epilepsy. For the EEG to have a high diagnostic value and high specificity, it is critical to recognize waveforms that can be mistaken for abnormal patterns. This article describes artifacts, normal rhythms, and normal patterns that are prone to being misinterpreted as abnormal. Artifacts are potentials generated outside the brain. They are divided into physiologic and extraphysiologic. Physiologic artifacts arise from the body and include EMG, eyes, various movements, EKG, pulse, and sweat. Some physiologic artifacts can be useful for interpretation such as EMG and eye movements. Extraphysiologic artifacts arise from outside the body, and in turn can be divided into the environments (electrodes, equipment, and cellphones) and devices within the body (pacemakers and neurostimulators). Normal rhythms can be divided into awake patterns (alpha rhythm and its variants, mu rhythm, lambda waves, posterior slow waves of youth, HV-induced slowing, photic driving, and photomyogenic response) and sleep patterns (POSTS, vertex waves, spindles, K complexes, sleep-related hypersynchrony, and frontal arousal rhythm). Breach can affect both awake and sleep rhythms. Normal variants or variants of uncertain clinical significance include variants that may have been considered abnormal in the early days of EEG but are now considered normal. These include wicket spikes and wicket rhythms (the most common normal pattern overread as epileptiform), small sharp spikes (aka benign epileptiform transients of sleep), rhythmic midtemporal theta of drowsiness (aka psychomotor variant), Cigánek rhythm (aka midline theta), 6 Hz phantom spike-wave, 14 and 6 Hz positive spikes, subclinical rhythmic epileptiform discharges of adults (SREDA), slow-fused transients, occipital spikes of blindness, and temporal slowing of the elderly. Correctly identifying artifacts and normal patterns can help avoid overinterpretation and misdiagnosis. This is an educational review paper addressing a learning objective of the International League Against Epilepsy (ILAE) curriculum.

Non-epileptic paroxysmal events at pediatric video-electroencephalography monitoring unit over a 15-year period

OBJECTIVE

Non-epileptic paroxysmal events (NEPEs) are common in pediatric patients and may be misdiagnosed as epileptic seizures. We aimed to study the distribution of NEPEs across age groups and with different comorbidities, and to correlate the patients' presenting symptoms with their final diagnosis after video-EEG.

METHODS

We retrospectively analyzed video-EEG recordings of children aged one month to 18 years who were admitted between March 2005 and March 2020. Patients who experienced any NEPE while under video-EEG monitorization were evaluated in this study. Subjects with concomitant epilepsy were also included. The patients were first divided into 14 groups according to the basic characteristics of symptoms they reported at admission. The events captured on video-EEG were then classified into six NEPE categories based on the nature of the events. These groups were compared according to video-EEG results.

RESULTS

We retrospectively evaluated 1338 records of 1173 patients. The final diagnosis was non-epileptic paroxysmal event in 226 (19.3%) of 1173 patients. The mean age of the patients was 105.4 ± 64.4 months at the time of the monitoring. The presenting symptoms were motor in 149/226 (65.9%) patients, with jerking being the most common (n = 40, 17.7%). Based on video-EEG, the most common NEPE was psychogenic non-epileptic seizures (PNES) (n = 66, 29.2%), and the most common PNES subtype was major motor movements (n = 19/66, 28.8%). Movement disorders (n = 46, 20.4%) were the second most common NEPE and the most common NEPE (n = 21/60, 35%) in children with developmental delay (n = 60). Other common NEPEs were physiological motor movements during sleep (n = 33, 14.6%), normal behavioral events (n = 31, 13.7%), and sleep disorders (n = 15, 6.6%). Almost half of the patients had a prior diagnosis of epilepsy (n = 105, 46.5%). Following the diagnosis of NEPE, antiseizure medication (ASM) was discontinued in 56 (24.8%) patients.

CONCLUSION

Non-epileptiform paroxysmal events can be difficult to distinguish from epileptic seizures in children, especially in patients with developmental delay, epilepsy, abnormal interictal EEG, or abnormal MRI findings. Correct diagnosis of NEPEs by video-EEG prevents unnecessary ASM exposure in children and guides appropriate management of NEPEs.

MindReader: Unsupervised Classification of Electroencephalographic Data

Electroencephalogram (EEG) interpretation plays a critical role in the clinical assessment of neurological conditions, most notably epilepsy. However, EEG recordings are typically analyzed manually by highly specialized and heavily trained personnel. Moreover, the low rate of capturing abnormal events during the procedure makes interpretation time-consuming, resource-hungry, and overall an expensive process. Automatic detection offers the potential to improve the quality of patient care by shortening the time to diagnosis, managing big data and optimizing the allocation of human resources towards precision medicine. Here, we present MindReader, a novel unsupervised machine-learning method comprised of the interplay between an autoencoder network, a hidden Markov model (HMM), and a generative component: after dividing the signal into overlapping frames and performing a fast Fourier transform, MindReader trains an autoencoder neural network for dimensionality reduction and compact representation of different frequency patterns for each frame. Next, we processed the temporal patterns using a HMM, while a third and generative component hypothesized and characterized the different phases that were then fed back to the HMM. MindReader then automatically generates labels that the physician can interpret as pathological and non-pathological phases, thus effectively reducing the search space for trained personnel. We evaluated MindReader's predictive performance on 686 recordings, encompassing more than 980 h from the publicly available Physionet database. Compared to manual annotations, MindReader identified 197 of 198 epileptic events (99.45%), and is, as such, a highly sensitive method, which is a prerequisite for clinical use.

EEG normal variants: A prospective study using the SCORE system

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We analyzed the number of normal variants in a SCORE database of 3050 EEG recordings.

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The most common normal variant was sharp transients.

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We present typical examples and detailed characterization of the normal variants.

Objective

To determine the prevalence and characteristics of normal variants in EEG recordings in a large cohort, and provide readers with typical examples of all normal variants for educational purposes.

Methods

Using the SCORE EEG system (Standardized Computer-Based Organized Reporting of EEG), we prospectively extracted EEG features in consecutive patients. In this dataset, we analyzed 3050 recordings from 2319 patients (mean age 38.5 years; range: 1–89 years).

Results

The distribution of the normal variants was as follows: sharp transients 19.21% (including wicket spikes), rhythmic temporal theta of drowsiness 6.03%, temporal slowing of the old 2.89%, slow fused transients 2.59%, 14-and 6-Hz bursts 1.83%, breach rhythm 1.25%, small sharp spikes 1.05%, 6-Hz spike and slow wave 0.69% and SREDA 0.03%.

Conclusions

The most prevalent normal variants are the sharp transients, which must not be over-read as epileptiform discharges.

Significance

EEG readers must be familiar with the normal variants to avoid misdiagnosis and misclassification of patients referred to clinical EEG recordings.

Normvarianten im kindlichen EEG: Pitfalls in der Praxis

Normvarianten sind nicht obligat auftretenden EEG-Veränderung. Die Kenntnis von Normvarianten ist wichtig, da sie sonst als pathologische Veränderungen fehlinterpretiert werden können. Daraus können klinisch falsche diagnostische und therapeutische Schlussfolgerungen entstehen. Die vorliegende Übersicht dient einer Zusammenfassung und Erläuterung der im Kindes- und Jugendalter auftretenden Normvarianten.

Intracranial correlates of small sharp spikes

OBJECTIVE

To identify cortical correlates of scalp small sharp spikes (SSS) using simultaneous scalp and intracranial EEG recordings.

METHODS

Patients were retrospectively evaluated based on a database of intracranial long-term recordings at the Epilepsy Center Freiburg. Inclusion criteria were: simultaneous recordings with intracranial and scalp EEGs and the presence of at least five unequivocal SSS in the scalp EEG. Intracranial recordings were analyzed regarding the co-occurring intracranial potentials during scalp SSS.

RESULTS

33 patients, aged 9-60y, 17 females, fulfilled the above-mentioned criteria. Almost all patients had intracranial SSS correlates in the form of spike/polyspike-waves in the temporal lobe, predominantly in the hippocampus (24/28), less frequently involving the amygdala (5/29), temporal basal (3/18), lateral neocortical (4/32), entorhinal cortices (1/12), and the parietal lobe (2/13). Amplitudes of intrahippocampal spikes or polyspikes co-occurring with SSS were significantly higher than intracranial discharges without scalp correlates. In 45% of patients, intracranial spikes accompanying SSS were located within the seizure onset zone (SOZ).

CONCLUSIONS

Our results strongly support an epileptic origin of SSS and provide evidence about their heterogenous generators.

SIGNIFICANCE

This study suggests that SSS cannot with certainty be classified as "benign" but rather considered as one of the EEG manifestations of focal epilepsy.

Subclinical epileptiform activity accelerates the progression of Alzheimer's disease: A long-term EEG study

OBJECTIVE

While many studies suggest that patients with Alzheimer's disease have a higher chance for developing epileptic seizures, only a few studies are available examining independent epileptic discharges. The major aims of our study was to determine the prevalence of subclinical epileptiform activity (SEA) in AD compared to healthy elderly controls with the hypothesis that SEA is more frequent in AD than in cognitively normal individuals. Another aim was to analyze the effect of baseline SEA captured with electroencephalography on the progression of the disease with longitudinal cognitive testing.

METHODS

We investigated 52 Alzheimer patients with no history of epileptic seizures and 20 healthy individuals. All participants underwent a 24-hour electroencephalography, neurology, neuroimaging and neuropsychology examination. Two independent raters analyzed visually the electroencephalograms and both raters were blind to the diagnoses. Thirty-eight Alzheimer patients were enrolled in a 3-year long prospective follow-up study with yearly repeated cognitive evaluation.

RESULTS

Subclinical epileptiform discharges were recorded significantly (p:0.018) more frequently in Alzheimer patients (54%) than in healthy elderly (25%). Epileptiform discharges were associated with lower performance scores in memory. Alzheimer patients with spikes showed 1.5-times faster decline in global cognitive scores than patients without (p < 0.001). The decline in cognitive performance scores showed a significant positive correlation with spike frequency (r:+0.664; p < 0.001).

CONCLUSIONS

Subclinical epileptiform activity occurs in half of Alzheimer patients who have never suffered epileptic seizures. Alzheimer patients with subclinical epileptiform activity showed accelerated cognitive decline with a strong relation to the frequency and spatial distribution (left temporal) of spikes.

SIGNIFICANCE

Our findings suggest the prominent role of epileptiform discharges in the pathomechanism of Alzheimer's disease which might serve as potential therapeutic target.

Benign EEG variants in the sleep-wake cycle: A prospective observational study using the 10-20 system and additional electrodes

OBJECTIVES

To study the prevalence of benign EEG variants (BEVs) in the sleep-wake cycle among 1163 consecutive patients.

METHODS

Prospective, observational EEG study using the 10-20 system with systematically two additional anterior-temporal electrodes. Depending on clinical indications, other electrodes were added. REM sleep identification was based on its characteristic EEG grapho-elements and rapid eye movements, clearly detectable with the additional anterior-temporal and fronto-polar electrodes due to eye proximity. The video-EEG monitoring duration was between 24hours and eight days.

RESULTS

We identified 710 patients (61%) with BEVs. Positive occipital sharp transients of sleep (POSTs) were observed in 36.4% of participants, mu rhythm in 22.4%, lambda waves in 16.7%, wicket spikes (WS) in 15%, 14- and 6-Hz positive bursts in 8.3%, benign sporadic sleep spikes (BSSS) in 3.3%, rhythmic mid-temporal theta burst of drowsiness (RMTD) in 2.15%, midline theta rhythm in 2.1% and six-Hz spike and wave (SW) bursts in 0.1%. WS and RMTD were present during wakefulness, NREM (14.1%, 1.3%, respectively) and REM sleep (3.3%, 1.1%, respectively). Mu rhythm was also observed during NREM (1.5%) and REM sleep (7.7%). Fourteen- and 6-Hz positive bursts were present during NREM (4.5%) and REM sleep (6.5%). BSSS and six-Hz SW bursts were only observed during NREM sleep.

CONCLUSIONS

The prevalence of BEVs is much higher than current estimates. POSTs and WS can no longer be considered as unusual patterns but physiological patterns of NREM sleep. RMTD and mu rhythm may be observed during NREM and REM sleep.

Prevalence of benign epileptiform variants from an EEG laboratory in India and frequency of their misinterpretation

OBJECTIVE

To prospectively study the prevalence of benign epileptiform variants (BEVs) and their impact on epilepsy misdiagnosis.

METHODS

Consecutive patients, older than one year, who underwent EEG from January 2016 to December 2019 were prospectively studied for the presence of BEVs. We used descriptions of Klass and Westmoreland (1985) to categorize the BEVs. We reviewed old EEG reports and records in patients with BEV to determine whether they were interpreted as abnormal.

RESULTS

Of the 1862 subjects included, 1474 (79 %) patients had epilepsy while 388 (21 %) had other diagnoses. The mean age of the subjects was 23.1 ± 15.3 years and 1111 (60 %) were males. BEVs were noted in 223 (12 %) subjects undergoing EEG. The most common BEVs were wicket waves (n = 127, 6.8 %) and small sharp spikes (n = 69, 3.7 %) while 6 Hz spike-wave discharges (0.9 %), 14 and 6 Hz positive spikes (0.6 %), rhythmic mid-temporal theta burst of drowsiness (0.4 %) and subclinical rhythmic epileptiform discharges in adults (0.2 %) were less common. Patients with BEVs were older and were more likely to have normal EEG (68.2 % vs. 55.8 %; p < 0.001). BEVs were not mentioned in any of the 282 previous EEG reports. BEVS were considered to be over-interpreted as epileptiform abnormalities in 31 of 101 (30 %) records available for review.

CONCLUSION

BEVs are present in 12 % of subjects undergoing EEG. BEVS are largely unrecognized and are misdiagnosed as epileptiform discharges in one third of the patients by the general neurologists.

Inhibiting Epileptiform Activity in Cognitive Disorders: Possibilities for a Novel Therapeutic Approach

Cognitive impairment is a common and seriously debilitating symptom of various mental and neurological disorders including autism, attention deficit hyperactivity disorder, multiple sclerosis, epilepsy, and neurodegenerative diseases, like Alzheimer's disease. In these conditions, high prevalence of epileptiform activity emerges as a common pathophysiological hallmark. Growing body of evidence suggests that this discrete but abnormal activity might have a long-term negative impact on cognitive performance due to neuronal circuitries' remodeling, altered sleep structure, pathological hippocampo-cortical coupling, and even progressive neuronal loss. In animal models, epileptiform activity was shown to enhance the formation of pathological amyloid and tau proteins that in turn trigger network hyperexcitability. Abolishing epileptiform discharges might slow down the cognitive deterioration. These findings might provide basis for therapeutic use of antiepileptic drugs in neurodegenerative cognitive disorders. The aim of our review is to describe the data on the prevalence of epileptiform activity in various cognitive disorders, to summarize the current knowledge of the mechanisms of epileptic activity in relation to cognitive impairment, and to explore the utility of antiepileptic drugs in the therapy of cognitive disorders. We also propose future directions for drug development and novel therapeutic interventions targeting epileptiform discharges in these disorders.

Critically ill benign EEG variants: Is there such a thing?

Despite growing use of critical care electroencephalography (ccEEG) to detect seizures and status epilepticus in the intensive care unit (ICU), integrating ccEEG findings with traditionally described benign EEG variants (BEVs) is a relatively new concept. BEV-like waveforms are now increasingly encountered in the ICU, and have also been explicitly included in proposed definitions of brief potentially ictal rhythmic discharges (BIRDs) in the ICU, bringing to the fore the question of if and which EEG patterns in critically ill patients can be safely deemed "benign". Though well-characterized as benign in healthy outpatients at low pre-test risk for neurologic disease, the significance of BEVs in the ICU remains largely unknown. Simultaneously, there has been mounting evidence to suggest that certain BEVs can arise from heterogeneous intracranial sources, including some pathologic generators. We conducted an extensive literature review on all known BEVs to assess what is known of BEVs in the ICU. Here we discuss critically ill BEVs and how to interpret them.

Normal Variants Are Commonly Overread as Interictal Epileptiform Abnormalities

Electroencephalographers may misclassify benign variant EEG patterns as epileptiform discharges, resulting in delays in the diagnosis and appropriate treatment of other paroxysmal disorders, such as psychogenic nonepileptic seizures, anxiety/panic disorders, and near syncope. These benign variant patterns include wicket spikes, small sharp spikes, and rhythmic mid-temporal theta of drowsiness. Cautious interpretations of semi-rhythmic sharp transients, usually gradually rising from the EEG background in drowsiness, can help avoid misdiagnosing patients as having seizures. Viewing the EEG as confirmatory for a clear clinical diagnosis is also helpful-elderly patients with syncope, for example, often have microvascular disease and EEG wicket rhythms in drowsiness-a careful review of the clinical history and the paroxysmal EEG pattern usually help distinguish normal variant patterns from interictal sharp waves and spikes and avoid misdiagnosing epilepsy.

A Framework Proposal to Follow-Up on Preclinical Convulsive Signals of a New Molecular Entity in First-in-Human Studies Using Electroencephalographic Monitoring

Traditionally, in dose-escalating first-in-human (FiH) studies, a dose cap with a 10-fold safety margin to the no observed effect level in animals is implemented if convulsive events are observed in animals. However, the convulsive risk seen in animals does not generally translate to humans. Several lines of evidence are summarized indicating that in a dose-escalating setting, electroencephalographic epileptiform abnormalities occur at lower doses than clinical convulsive events. Therefore, we propose to consider the occurrence of epileptiform abnormalities in toxicology studies as premonitory signals for convulsions in dose-escalating FiH studies. Compared with the traditional dose-cap approach, this may allow the exploration of higher doses in FiH and, subsequently, phase II studies without compromising human safety. Similarly, the presence or absence of electroencephalographic epileptiform abnormalities may also aid the assessment of proconvulsive risk in situations of increased perpetrator burden as potentially present in pharmacokinetic and/or pharmacodynamic drug-drug interactions.

SREDA: A Rare but Confusing Benign EEG Variant

INTRODUCTION

Subclinical rhythmic EEG discharges in adults (SREDA) is a very rare benign EEG pattern. The electrophysiological features and atypical variants of SREDA has wide spectrum and they are poorly known. It resembles ictal discharges, and overinterpretation of SREDA may lead to misdiagnosis of epilepsy. Herein, we aimed to report patients with SREDA to identify the frequency and characterized clinical, demographic, electrophysiological features.

METHODS

We reviewed 22,234 EEG reports that are reported by the same experienced clinical neurophysiologists, between 2012 and 2018. The EEGs with SREDA were reevaluated blindly by three clinical neurophysiologists. The demographic, clinical characteristics, and neuroimaging features of the patients were reviewed.

RESULTS

Subclinical rhythmic EEG discharges in adults was present in 14 EEG records (0.06%), in nine patients. The mean age of patients was 52.1 ± 17.7 (range, 21-71) years. The patients had been diagnosed with several neurologic diseases, including cerebrovascular disease, epilepsy, psychogenic nonepileptic seizures, mental retardation, Alzheimer disease, and transient global amnesia. One patient had unilateral lesion, in whom SREDA had appeared on contralateral side of the lesion, whereas other patients with normal or nonlateralized lesions had SREDA bilaterally and symmetrical. This variant had been misdiagnosed as an ictal discharge in previous EEGs in three patients.

CONCLUSIONS

This study indicates that SREDA is difficult to associate with any specific condition. The pathophysiology of SREDA can not be explained by a single mechanism. Even if it is mostly observed in older adults, it is also observed in young adults in this study. It is important to differentiate SREDA from ictal discharge to prevent misdiagnosis of epilepsy especially in nonepileptic paroxysmal events.

Atypical SREDA During Wakefulness, NREM and REM Sleep in a Young Teenager: A Diagnostic Challenge

Subclinical rhythmic electrographic discharges of adult (SREDA) is a rare variant considered to be normal in EEG. It consists of sharp-contoured or sinusoidal waveforms in the theta frequency range (usually 5-7 Hz), occurring in a widespread distribution, often maximal over the parietotemporal regions. SREDA is usually bilateral but could be unilateral (atypical SREDA). Compared with true ictal activities, SREDA has little change in frequency, morphology, and distribution. It has been described in adults and is extremely rare in children/adolescents. There are only 5 cases published in children. We report a case of atypical SREDA in a child that may have occurred in wakefulness, non-rapid eye movement (NREM) sleep, and REM sleep. This finding was initially misdiagnosed as epilepsy. Inpatient video-EEG demonstrated that during periods of SREDA in quiet wakefulness the patient did not have any signs or symptoms with the SREDA pattern; SREDA abated whenever the patient was alerted or if he spontaneously initiated some activities. SREDA with same morphology was noted in NREM and REM sleep. The patient had no true epileptogenic abnormalities and hence antiepileptic medication was discontinued and stopped uneventfully. This case illustrates the importance of recognizing this rare variant, avoiding a misdiagnosis of epilepsy as it occurred in our case.

Electrographic spikes are common in wildtype mice

High-voltage rhythmic electroencephalographic (EEG) spikes have been recorded in wildtype (WT) rats during periods of light slow-wave sleep and passive wakefulness. The source of this activity is unclear but has been attributed to either an inherent form of absence epilepsy or a normal feature of rodent sleep EEG. In contrast, little is known about epileptiform spikes in WT mice. We thus characterize and quantify epileptiform discharges in WT mice for the first time. Thirty-six male WT C57 mice with 24-h wireless telemetry video-EEG recordings were manually scored by blinded reviewers to mark individual spikes and spike trains. Epileptiform spikes were detected in 100% of the recorded WT mice, and spike trains of at least three spikes were recorded in 90% of mice. The spikes were more frequent during the day than at night and were inversely correlated to each animal's locomotor activity. However, the discharges were not absent during active nighttime periods. These discharges may indicate a baseline tendency toward epileptic seizures or perhaps are benign variants of normal rodent background EEG. Nevertheless, a better understanding of baseline WT EEG activity will aid in differentiating pathological and normal EEG activity in mouse epilepsy models.

Prevalence of benign epileptiform variants during initial EEG examination in French military aircrew

INTRODUCTION

In France, a systematic EEG is performed during initial examination in military aircrew applicants, which may provide an estimation of the prevalence of benign epileptiform variants in healthy adults.

METHODS

We analyzed standard EEG (21 scalp electrodes, 20minutes, 400Hz sampling rate) of military aircrew applicants examined in the French Main Aeromedical Center in 2016. EEGs were analyzed using both bipolar and referential montages. The collected data were EEG abnormalities and benign epileptiform variants. The kappa inter-observer index for the detection of benign epileptiform variants was calculated.

RESULTS

Our population was composed of 495 subjects (86.7% males, mean age 22.5±4.8 years), wishing to become a pilot in 69.7% of cases. None of the applicants reported any neurological disease and none was taking regular medication. EEG was considered as normal for 96.4% of them. Encountered EEG abnormalities were mainly asymmetric and sharp slow wave bursts. Drowsiness was recorded during 13.9% of these EEG. Benign epileptiform variants were present in 7.7% of our population: anterior theta activities (4%), posterior slow waves (2.8%), alpha variants (0.6%) and wicket spikes (0.2%). Hyperventilation induced EEG slowing in 14.1% of cases. During intermittent photic stimulation, physiological photic driving was observed in 15.2% of subjects.

DISCUSSION

Many previous studies have been dedicated to the prevalence of benign epileptiform variants but results are often heterogeneous and based on patients in whom there was an indication for EEG. Our results thus bring data on benign epileptiform variants prevalence in a young adult population characterized by the absence of neurologic disorders. Our study demonstrates that anterior theta activities, posterior slow waves, alpha variants and wicket spikes are the most frequent benign EEG variants in such a young adult population.

The Success Rate of Neurology Residents in EEG Interpretation After Formal Training

EEG is an important tool for neurologists in both diagnosis and classification of seizures. It is not uncommon in clinical practice to see patients who were erroneously diagnosed as epileptic. Most of the time incorrect interpretation of EEG contributes significantly to this problem. In this study, we aimed to investigate the success rate of neurology residents in EEG interpretation after formal training. Eleven neurology residents were included in the study. Duration of EEG training (3 vs 4 months) and time since completion of EEG education were determined. Residents were randomly presented 30 different slides of representative EEG screenshots. They received 1 point for each correct response. The effect of training duration and time since training were investigated statistically. Besides, we looked at the success rate of each question to see whether certain patterns were more readily recognized than others. EEG training duration ( P = .93) and time since completion of training ( P = .16) did not influence the results. The success rate of residents for correct responses was between 17% and 50%. On the other hand, the success rate for each question varied between 0% and 91%. Overall, benign variants and focal ictal onset patterns were the most difficult to recognize. On 13 occasions (6.5%) nonepileptiform patterns were thought to represent epileptiform abnormalities. After formal training, neurology residents could identify ≤50% of the EEG patterns correctly. The wide variation in success rate among residents and also between questions implies that both personal characteristics and inherent EEG features influence successful EEG interpretation.

Interictal epileptiform discharge characteristics underlying expert interrater agreement

OBJECTIVE

The presence of interictal epileptiform discharges (IED) in the electroencephalogram (EEG) is a key finding in the medical workup of a patient with suspected epilepsy. However, inter-rater agreement (IRA) regarding the presence of IED is imperfect, leading to incorrect and delayed diagnoses. An improved understanding of which IED attributes mediate expert IRA might help in developing automatic methods for IED detection able to emulate the abilities of experts. Therefore, using a set of IED scored by a large number of experts, we set out to determine which attributes of IED predict expert agreement regarding the presence of IED.

METHODS

IED were annotated on a 5-point scale by 18 clinical neurophysiologists within 200 30-s EEG segments from recordings of 200 patients. 5538 signal analysis features were extracted from the waveforms, including wavelet coefficients, morphological features, signal energy, nonlinear energy operator response, electrode location, and spectrogram features. Feature selection was performed by applying elastic net regression and support vector regression (SVR) was applied to predict expert opinion, with and without the feature selection procedure and with and without several types of signal normalization.

RESULTS

Multiple types of features were useful for predicting expert annotations, but particular types of wavelet features performed best. Local EEG normalization also enhanced best model performance. As the size of the group of EEGers used to train the models was increased, the performance of the models leveled off at a group size of around 11.

CONCLUSIONS

The features that best predict inter-rater agreement among experts regarding the presence of IED are wavelet features, using locally standardized EEG. Our models for predicting expert opinion based on EEGer's scores perform best with a large group of EEGers (more than 10).

SIGNIFICANCE

By examining a large group of EEG signal analysis features we found that wavelet features with certain wavelet basis functions performed best to identify IEDs. Local normalization also improves predictability, suggesting the importance of IED morphology over amplitude-based features. Although most IED detection studies in the past have used opinion from three or fewer experts, our study suggests a "wisdom of the crowd" effect, such that pooling over a larger number of expert opinions produces a better correlation between expert opinion and objectively quantifiable features of the EEG.

Audit of EEG Reporting Temporal Abnormalities

Background:

To ensure the overall quality of our electroencephalogram (EEG) laboratory, we decided to perform an audit of EEGs interpreted at our institution, focusing initially on EEGs reporting temporal abnormalities.

Methods:

Reports of all EEGs performed between January 1st and June 30th, 2006 were reviewed in order to identify tracings mentioning abnormalities in the temporal regions. These records were then independently reviewed by two epileptologists on two distinct occasions, separated by an interval of at least six months. If the recording was considered normal after this process, the cause for misinterpretation was identified and the patient's chart was reviewed to determine if he was epileptic or not based on available evidence until June 2009.

Results:

In the first half of 2006,143 out of 773 EEGs mentioned temporal abnormalities (18.5%). In general, intra- and interrater agreement ratios between our two epileptologists were moderate to substantial for normality, presence of epileptic activity and presence of slowing. Forty-five recordings (31.5%) were reported as normal independently by them on two distinct sittings six months apart. The most common causes for misinterpretation were the presence of benign epileptiform variants, normal sharply contoured patterns of somnolence or hyperventilation. Chart review confirmed that most were non-epileptic patients (60% non-epileptic, 27% epileptic, 13% unknown).

Conclusion:

Moderate to substantial intra- and interrater agreement as well as frequent misinterpretation of physiological variants indicate that some corrective measures need to be implemented to improve the consistency of EEG interpretation amongst our group of electroencephalographers.

Mesial temporal lobe epilepsy: a distinct electroclinical subtype of temporal lobe epilepsy

Mesial temporal lobe epilepsy is a common subtype of temporal lobe epilepsy. Its most common cause is hippocampal sclerosis, which contributes to its distinct electroclinical phenotype that is seen commonly in the epilepsy monitoring unit setting. The common electrophysiological data show anterior temporal interictal sharp waves as well as rhythmic theta activity in the same localization. While the electrophysiological data can at times be misleading, its stereotyped and characteristic semiology can often allow for accurate diagnosis on its own. As patients with mesial temporal lobe epilepsy often fail medical therapy, surgical therapy can be considered. Early accurate diagnosis in these patients is essential for optimal care.

Preliminary Findings of Single- and Multifocused Epileptiform Discharges in Nonepileptic Psychiatric Patients

Epileptiform discharges (EDs) in nonepileptic populations remain controversial as to their role in psychopathology. Previous studies have unsuccessfully attempted to correlate specific waveforms of EDs, defined by duration and morphology, with broad diagnostic categories such as depression and anxiety. These diagnostic categories often include heterogeneous patient populations, with potentially divergent biological underpinnings of clinical presentation. This study examined epileptiform activities as a single phenomenon, identifying the relationships between distribution patterns of EDs and endorsement of clinical symptoms across affective, cognitive, and somatic domains. In a sample of 71 nonepileptic psychiatric patients, those with EDs appearing in homologous electrode pairs endorsed significantly fewer symptoms related to affective deregulation. These patients were also significantly less likely to endorse a history of severe symptomatology, including suicidal ideation/previous attempt, self-injurious behavior, psychoses or dissociation, and previous psychiatric hospitalization. Conversely, patients with isolated EDs focused to a single brain region endorsed greater affective deregulation and severe clinical symptoms. These findings offer new possibilities regarding the potentially protective role that EDs may play when distributed across hemispheres, particularly in light of recent theories exploring functional connectivity of neuronal networks.

Detection of interictal epileptiform discharges using signal envelope distribution modelling: application to epileptic and non-epileptic intracranial recordings

Interictal epileptiform discharges (spikes, IEDs) are electrographic markers of epileptic tissue and their quantification is utilized in planning of surgical resection. Visual analysis of long-term multi-channel intracranial recordings is extremely laborious and prone to bias. Development of new and reliable techniques of automatic spike detection represents a crucial step towards increasing the information yield of intracranial recordings and to improve surgical outcome. In this study, we designed a novel and robust detection algorithm that adaptively models statistical distributions of signal envelopes and enables discrimination of signals containing IEDs from signals with background activity. This detector demonstrates performance superior both to human readers and to an established detector. It is even capable of identifying low-amplitude IEDs which are often missed by experts and which may represent an important source of clinical information. Application of the detector to non-epileptic intracranial data from patients with intractable facial pain revealed the existence of sharp transients with waveforms reminiscent of interictal discharges that can represent biological sources of false positive detections. Identification of these transients enabled us to develop and propose secondary processing steps, which may exclude these transients, improving the detector's specificity and having important implications for future development of spike detectors in general.

Neural circuit mechanisms of post-traumatic epilepsy

Traumatic brain injury (TBI) greatly increases the risk for a number of mental health problems and is one of the most common causes of medically intractable epilepsy in humans. Several models of TBI have been developed to investigate the relationship between trauma, seizures, and epilepsy-related changes in neural circuit function. These studies have shown that the brain initiates immediate neuronal and glial responses following an injury, usually leading to significant cell loss in areas of the injured brain. Over time, long-term changes in the organization of neural circuits, particularly in neocortex and hippocampus, lead to an imbalance between excitatory and inhibitory neurotransmission and increased risk for spontaneous seizures. These include alterations to inhibitory interneurons and formation of new, excessive recurrent excitatory synaptic connectivity. Here, we review in vivo models of TBI as well as key cellular mechanisms of synaptic reorganization associated with post-traumatic epilepsy (PTE). The potential role of inflammation and increased blood-brain barrier permeability in the pathophysiology of PTE is also discussed. A better understanding of mechanisms that promote the generation of epileptic activity versus those that promote compensatory brain repair and functional recovery should aid development of successful new therapies for PTE.

EEG and epilepsy monitoring

PURPOSE OF REVIEW

This article reviews the utility of EEG and prolonged video-EEG telemetry in the diagnosis and management of a patient with epilepsy.

RECENT FINDINGS

The EEG can be the most helpful test to determine a diagnosis of epilepsy; it can also distinguish focal and generalized neurophysiologic correlates of epilepsy. Furthermore, when paired with video monitoring, EEG can not only define epileptic and nonepileptic events but also aid in localization of seizures in patients with epilepsy. Finally, when history and other imaging modalities are considered with the EEG, the epileptic syndrome can usually be defined and the treatment can be focused. In critically ill patients, continuous EEG monitoring can define subclinical seizures, although a variety of periodic patterns may also be identified.

SUMMARY

EEG is an invaluable tool in the diagnosis and management of a patient with epilepsy, and continuous EEG monitoring is useful in identifying subclinical seizures and nonconvulsive status epilepticus in critically ill patients.

Electroencephalography in mesial temporal lobe epilepsy: a review

Electroencephalography (EEG) has an important role in the diagnosis and classification of epilepsy. It can provide information for predicting the response to antiseizure drugs and to identify the surgically remediable epilepsies. In temporal lobe epilepsy (TLE) seizures could originate in the medial or lateral neocortical temporal region, and many of these patients are refractory to medical treatment. However, majority of patients have had excellent results after surgery and this often relies on the EEG and magnetic resonance imaging (MRI) data in presurgical evaluation. If the scalp EEG data is insufficient or discordant, invasive EEG recording with placement of intracranial electrodes could identify the seizure focus prior to surgery. This paper highlights the general information regarding the use of EEG in epilepsy, EEG patterns resembling epileptiform discharges, and the interictal, ictal and postictal findings in mesial temporal lobe epilepsy using scalp and intracranial recordings prior to surgery. The utility of the automated seizure detection and computerized mathematical models for increasing yield of non-invasive localization is discussed. This paper also describes the sensitivity, specificity, and predictive value of EEG for seizure recurrence after withdrawal of medications following seizure freedom with medical and surgical therapy.

Subclinical rhythmic electrographic discharges of adults in a patient with migraine

Subclinical rhythmic electrographic discharges of adults (SREDA) is a distinctive electroencephalogram (EEG) pattern seen in adults and is thought to have little clinical significance. These patterns are generally considered nonspecific. We evaluated a 45-year-old female patient with migraine in whom SREDA seemed to have a temporal relation to the migraine symptoms of the patient.

Artifact: recording EEG in special care units

Artifacts may be obtained during routine recording but are more common in special care units (SCUs) outside of the EEG laboratory, where complex electrical currents are present that create a "hostile" environment. Special care units include the epilepsy monitoring unit, neurologic intensive care unit, and operating room, where artifact is present in virtually every recording, increasing with prolonged use. Nonepileptic attacks treated as epileptic seizures have been incorrectly diagnosed and treated due to a misinterpreted EEG. The recent emergence of continuous EEG as a neurophysiologic surrogate for brain function in the neurologic intensive care unit and operating room has also brought a greater amount and new types of EEG artifact. The artifacts encountered in special care units during continuous EEG are becoming more complex and may have adverse therapeutic implications. Our knowledge of artifact needs to parallel our growth in technology to avoid the pitfalls that may be incurred during visual analysis of the EEG.

Counterpoint to "what is an epileptic seizure?" by D'Ambrosio and Miller

D'Ambrosio and Miller argue that brief (i.e., one to a few seconds), rhythmic electrographic events accompanied by behavioral arrest, which they have observed in rats after lateral fluid percussion (i.e., in an animal model of traumatic brain injury), should be considered seizures in this model of posttraumatic epilepsy (1). A counter argument is that these events are not characteristic of the seizures seen clinically in posttraumatic epilepsy or in other forms of acquired epilepsy. Furthermore, several types of brief, rhythmic activity can be recorded in the electroencephalogram (EEG) of animals and humans without epilepsy. One cannot exclude the possibility that such events represent normal electrical activity, which may (or even may not) occur more often after brain injury. Thus, caution is required. In this counterpoint to "What Is an Epileptic Seizure?" by D'Ambrosio and Miller, the assertion is made that experimental studies on animal models of acquired epilepsy that claim electrographic events to be seizures, when the possibility exists that they may not be seizures characteristic of human acquired epilepsy, could be counterproductive, since research resources could be focused on animal models that may not actually demonstrate acquired epilepsy.