Interictal EEG and the diagnosis of epilepsy

Computer-assisted analysis of routine EEG to identify hidden biomarkers of epilepsy: A systematic review

Background

Computational analysis of routine electroencephalogram (rEEG) could improve the accuracy of epilepsy diagnosis. We aim to systematically assess the diagnostic performances of computed biomarkers for epilepsy in individuals undergoing rEEG.

Methods

We searched MEDLINE, EMBASE, EBM reviews, IEEE Explore and the grey literature for studies published between January 1961 and December 2022. We included studies reporting a computational method to diagnose epilepsy based on rEEG without relying on the identification of interictal epileptiform discharges or seizures. Diagnosis of epilepsy as per a treating physician was the reference standard. We assessed the risk of bias using an adapted QUADAS-2 tool.

Results

We screened 10 166 studies, and 37 were included. The sample size ranged from 8 to 192 (mean=54). The computed biomarkers were based on linear (43%), non-linear (27%), connectivity (38%), and convolutional neural networks (10%) models. The risk of bias was high or unclear in all studies, more commonly from spectrum effect and data leakage. Diagnostic accuracy ranged between 64% and 100%. We observed high methodological heterogeneity, preventing pooling of accuracy measures.

Conclusion

The current literature provides insufficient evidence to reliably assess the diagnostic yield of computational analysis of rEEG.

Significance

We provide guidelines regarding patient selection, reference standard, algorithms, and performance validation.

Development of spontaneous recurrent seizures accompanied with increased rates of interictal spikes and decreased hippocampal delta and theta activities following extended kindling in mice

Interictal epileptiform discharges refer to aberrant brain electrographic signals between seizures and feature intermittent interictal spikes (ISs), sharp waves, and/or abnormal rhythms. Recognition of these epileptiform activities by electroencephalographic (EEG) examinations greatly aids epilepsy diagnosis and localization of the seizure onset zone. ISs are a major form of interictal epileptiform discharges recognized in animal models of epilepsy. Progressive changes in IS waveforms, IS rates, and/or associated fast ripple oscillations have been shown to precede the development of spontaneous recurrent seizures (SRS) in various animal models. IS expressions in the kindling model of epilepsy have been demonstrated but IS changes during the course of SRS development in extended kindled animals remain to be detailed. We hence addressed this issue using a mouse model of kindling-induced SRS. Adult C57 black mice received twice daily hippocampal stimulations until SRS occurrence, with 24-h EEG monitoring performed following 50, 80, and ≥ 100 stimulations and after observation of SRS. In the stimulated hippocampus, increases in spontaneous ISs rates, but not in IS waveforms nor IS-associated fast ripples, along with decreased frequencies of hippocampal delta and theta rhythms, were observed before SRS onset. Comparable increases in IS rates were further observed in the unstimulated hippocampus, piriform cortex, and entorhinal cortex, but not in the unstimulated parietal cortex and dorsomedial thalamus. These data provide original evidence suggesting that increases in hippocampal IS rates, together with reductions in hippocampal delta and theta rhythms are closely associated with development of SRS in a rodent kindling model.

Localizing hidden Interictal Epileptiform Discharges with simultaneous intracerebral and scalp high-density EEG recordings

BACKGROUND

Scalp EEG is one of the main tools in the clinical evaluation of epilepsy. In some cases intracranial Interictal Epileptiform Discharges (IEDs) are not visible from the scalp. Recent studies have shown the feasibility of revealing them in the EEG if their timings are extracted from simultaneous intracranial recordings, but their potential for the localization of the epileptogenic zone is not yet well defined.

NEW METHOD

We recorded simultaneous high-density EEG (HD-EEG) and stereo-electroencephalography (SEEG) during interictal periods in 8 patients affected by drug-resistant focal epilepsy. We identified IEDs in the SEEG and systematically analyzed the time-locked signals on the EEG by means of evoked potentials, topographical analysis and Electrical Source Imaging (ESI). The dataset has been standardized and is being publicly shared.

RESULTS

Our results showed that IEDs that were not clearly visible at single-trials could be uncovered by averaging, in line with previous reports. They also showed that their topographical voltage distributions matched the position of the SEEG electrode where IEDs had been identified, and that ESI techniques can reconstruct it with an accuracy of ∼2 cm. Finally, the present dataset provides a reference to test the accuracy of different methods and parameters.

COMPARISON WITH EXISTING METHODS

Our study is the first to systematically compare ESI methods on simultaneously recorded IEDs, and to share a public resource with in-vivo data for their evaluation.

CONCLUSIONS

Simultaneous HD-EEG and SEEG recordings can unveil hidden IEDs whose origins can be reconstructed using topographical and ESI analyses, but results depend on the selected methods and parameters.

Modeling the Interictal Epileptic State for Therapeutic Development with Tetanus Toxin

Focal forms of epilepsy can result from a wide range of insults and can vary from focal symptoms to generalized convulsions. Most drugs that have been developed for epilepsy focus on the prevention of seizures. On Electroencephalography (EEG), seizures are characterized by a repetitive buildup of epileptic waveforms that can spread across the brain. Brain regions that produce seizures generate far more frequent 'interictal' spikes seen between seizures, and in animal models, these spikes occur prior to the development of seizures. Interictal spiking by itself has been shown to have significant adverse clinical effects on cognition and behavior in both patients and animal models. While the exact relationships between interictal spiking and seizures are not well defined, interictal spikes serve as an important biomarker that, for some forms of epilepsy, can serve as a surrogate biomarker and as a druggable target. While there are many animal models of seizures for drug development, here we review models of interictal spiking, focusing on tetanus toxin, to study the relationship between interictal spiking, seizures, cognition, and behavior. Studies on human cortical regions with frequent interictal spiking have identified potential therapeutic targets; therefore, having a highly consistent model of spiking will be invaluable not only for unraveling the initial stages of the pathological cascade leading to seizure development but also for testing novel therapeutics. This review offers a succinct overview of the use of tetanus toxin animal models for studying and therapeutic development for interictal spiking.

Children with ADHD and EEG abnormalities at baseline assessment, risk of epileptic seizures and maintenance on methylphenidate three years later

PURPOSE

This study aimed to assess the incidence of EEG abnormalities (EEG-ab) in children diagnosed with ADHD, investigate the risk of epileptic seizures (SZ) and maintenance on methylphenidate (MPH) over a three-year period.

METHODS

A total of 517 ADHD children aged 6-14 years were included. Baseline assessments included the identification of EEG-ab, ADHD inattentive subtype (ADHD-I), comorbid epilepsy, the use of antiepileptic drugs (AEDs) and the use of MPH. At the 3-year follow-up, assessments included the presence of EEG-ab, maintenance on MPH, AED usage, SZ risk in cases with EEG-epileptiform abnormalities (EEG-epi-ab), compared with control ADHD cases without EEG-epi-ab matched for age and gender.

RESULTS

EEG-ab were identified in 273 (52.8%) cases. No statistically significant differences were observed between the EEG-ab and EEG-non-ab groups in terms of age, gender, ADHD-I type or initial use of MPH. EEG non-epileptiform abnormalities (EEG-non-epi-ab) were found in 234 out of 478 (49%) cases without EEG-epi-ab. Notably, EEG-non-epi-ab occurred more frequently in the group of 39 cases with EEG-epi-ab (30/39 (76.9%) vs. 9/39, (21.3%), a subset selected for 3-year follow-up. At 3-year-follow-up no statistically significant difference was found in maintenance on MPH in ADHD cases with and without EEG-epi-ab. Nobody of ADHD cases without comorbid epilepsy or with comorbid epilepsy with achieved SZ freedom developed new SZ. Only 3 children with drug resistant epilepsy experienced SZs, without increase in SZ frequency. The disappearance rate of EEG-epi-ab was higher than that EEG-non-epi-ab (71.8% vs. 33.3%).

CONCLUSIONS

Children with and without EEG-ab exhibited similar patterns of MPH use (initial use, positive response, and maintenance on MPH). The presence of comorbid epilepsy and EEG-ab, with or without EEG-epi-ab, was not associated with an increased risk of SZ despite the use of MPH.

Characteristics and treatment of midlife-onset epilepsy: A 24-year single-center, retrospective study

OBJECTIVE

This study aimed to analyze the clinical characteristics, etiology, and treatment of midlife-onset epilepsy in a real-world setting at a single center in China.

METHODS

The clinical data of patients who attended the epilepsy clinic of the Department of Neurology, First Medical Center of Chinese PLA General Hospital from February 1999 to March 2023 were retrospectively analyzed. The clinical characteristics, etiology, and risk factors for midlife-onset epilepsy over the past 24 years were analyzed.

RESULTS

Of the 969 patients with onset at 45-64 years of age, 914 were diagnosed with epilepsy with at least two unprovoked seizures 24 h apart. Of those, 99.7% (911) were of focal origin. The median duration from the initial seizure to follow-up treatment was 2 months (interquartile range [IQR]: 1.0-6.0 months). Before commencing treatment, 30.2% (207/683) of patients experienced more than two seizures. A structural etiology was found in 66.3% (606/914) of patients. Cerebrovascular disease (CVD) and traumatic brain injury (TBI) accounted for 19.9% (182/914) and 16.6% (152/914) of the cases, respectively. Logistic regression analysis showed that patients with abnormal imaging (odds ratio [OR] 2.04; 95% confidence interval [CI] 1.25-3.32; p = .004), focal seizures (OR 2.98; 95%CI 1.82-4.87; p < .001), and seizure clusters (OR 2.40; 95%CI 1.21-4.73; p = .01) had poor drug responses. Treatment outcomes were generally better in patients with epilepsy after CVD (OR .49; 95%CI .28-.85; p = .01). Treatment initiation after two seizures (OR .70; 95%CI .42-1.15; p = .16) or 6 months after the first seizure (OR 1.17; 95%CI .66-2.09; p = .58) did not result in poor drug effectiveness.

SIGNIFICANCE

Midlife-onset epilepsy is typically of focal etiology, with CVD being the most common cause, and tends to respond well to medication. The median duration from the initial seizure to follow-up treatment was 2 months. Over 30% of patients experienced more than two seizures before commencing treatment, but this did not affect subsequent outcomes.

Spike-induced cytoarchitectonic changes in epileptic human cortex are reduced via MAP2K inhibition

Interictal spikes are electroencephalographic discharges that occur at or near brain regions that produce epileptic seizures. While their role in generating seizures is not well understood, spikes have profound effects on cognition and behaviour, depending on where and when they occur. We previously demonstrated that spiking areas of human neocortex show sustained MAPK activation in superficial cortical Layers I-III and are associated with microlesions in deeper cortical areas characterized by reduced neuronal nuclear protein staining and increased microglial infiltration. Based on these findings, we chose to investigate additional neuronal populations within microlesions, specifically inhibitory interneurons. Additionally, we hypothesized that spiking would be sufficient to induce similar cytoarchitectonic changes within the rat cortex and that inhibition of MAPK signalling, using a MAP2K inhibitor, would not only inhibit spike formation but also reduce these cytoarchitectonic changes and improve behavioural outcomes. To test these hypotheses, we analysed tissue samples from 16 patients with intractable epilepsy who required cortical resections. We also utilized a tetanus toxin-induced animal model of interictal spiking, designed to produce spikes without seizures in male Sprague-Dawley rats. Rats were fitted with epidural electrodes, to permit EEG recording for the duration of the study, and automated algorithms were implemented to quantify spikes. After 6 months, animals were sacrificed to assess the effects of chronic spiking on cortical cytoarchitecture. Here, we show that microlesions may promote excitability due to a significant reduction of inhibitory neurons that could be responsible for promoting interictal spikes in superficial layers. Similarly, we found that the induction of epileptic spikes in the rat model produced analogous changes, including reduced neuronal nuclear protein, calbindin and parvalbumin-positive neurons and increased microglia, suggesting that spikes are sufficient for inducing these cytoarchitectonic changes in humans. Finally, we implicated MAPK signalling as a driving force producing these pathological changes. Using CI-1040 to inhibit MAP2K, both acutely and after spikes developed, resulting in fewer interictal spikes, reduced microglial activation and less inhibitory neuron loss. Treated animals had significantly fewer high-amplitude, short-duration spikes, which correlated with improved spatial memory performance on the Barnes maze. Together, our results provide evidence for a cytoarchitectonic pathogenesis underlying epileptic cortex, which can be ameliorated through both early and delayed MAP2K inhibition. These findings highlight the potential role for CI-1040 as a pharmacological treatment that could prevent the development of epileptic activity and reduce cognitive impairment in both patients with epilepsy and those with non-epileptic spike-associated neurobehavioural disorders.

A randomized controlled educational pilot trial of interictal epileptiform discharge identification for neurology residents

OBJECTIVE

To assess the effectiveness of an educational program leveraging technology-enhanced learning and retrieval practice to teach trainees how to correctly identify interictal epileptiform discharges (IEDs).

METHODS

This was a bi-institutional prospective randomized controlled educational trial involving junior neurology residents. The intervention consisted of three video tutorials focused on the six IFCN criteria for IED identification and rating 500 candidate IEDs with instant feedback either on a web browser (intervention 1) or an iOS app (intervention 2). The control group underwent no educational intervention ("inactive control"). All residents completed a survey and a test at the onset and offset of the study. Performance metrics were calculated for each participant.

RESULTS

Twenty-one residents completed the study: control (n = 8); intervention 1 (n = 6); intervention 2 (n = 7). All but two had no prior EEG experience. Intervention 1 residents improved from baseline (mean) in multiple metrics including AUC (.74; .85; p < .05), sensitivity (.53; .75; p < .05), and level of confidence (LOC) in identifying IEDs/committing patients to therapy (1.33; 2.33; p < .05). Intervention 2 residents improved in multiple metrics including AUC (.81; .86; p < .05) and LOC in identifying IEDs (2.00; 3.14; p < .05) and spike-wave discharges (2.00; 3.14; p < .05). Controls had no significant improvements in any measure.

SIGNIFICANCE

This program led to significant subjective and objective improvements in IED identification. Rating candidate IEDs with instant feedback on a web browser (intervention 1) generated greater objective improvement in comparison to rating candidate IEDs on an iOS app (intervention 2). This program can complement trainee education concerning IED identification.

Factors Affecting the Activation and Appearances of Epileptiform Abnormalities in Routine Electroencephalography by Different Provocation Methods

OBJECTIVE

The objective of this study was to observe the effects of various clinical factors on the activation and appearance of epileptiform abnormalities (EAs) in routine electroencephalography (rEEG) by different provocation methods.

METHODS

This observational study involved a review of 136 patients presented for EEG recording due to various indications and their EEG showing EAs during various provocation methods.

RESULTS

Generalized spike-wave discharges (GSWDs) were the most frequent activated epileptiform pattern observed in, 81 (59.1%) recordings. This pattern was seen mainly in females 49 (P = 0.00), in patients with generalized seizures 48 (P = 0.00), in prolonged EEG records 3 (P = 0.03), and in both genetic 35 (P = 0.00) and lesional epilepsies 21 (P = 0.00). Focal sharp waves with bilateral synchrony (FSWSBS) were the most activated ictal pattern (P = 0.00). Ictal EAs after hyperventilation (HV) (P = 0.03) and intermittent photic stimulation (IPS) (P = 0.01) were mainly observed in patients with uncontrolled seizures (P = 0.00), and immune-mediated epilepsy (P = 0.02). Females sex (odds ratio [OR]: 1.33, confidence interval [CI]: 0.6-2.6; P = 0.25), bilateral tonic-clonic seizures (OR: 1.17, CI: 0.5-2.4; P = 0.31) and lesional epilepsies (OR: 1.45, CI: 0.7-2.9; P = 0.20) had risk of activation of EAs by provocation methods; however this risk was not statistically significant. While sleep deprivation (SD) (OR: 6.33, CI: 2.2-18.2; P = 0.00), nonrapid eye movement sleep (NREM) (OR: 2.41, CI: 1.0-5.4; P = 0.00), and prolong EEG recording (OR: 1.91, CI: 0.9-3.9; P = 0.04) were leading to a statistically significant risk of activation and appearances of EAs due to provocation.

CONCLUSION

Different provocation methods can activate and augment the variety of EEG patterns of diverse clinical significance. Detection of activated ictal EAs is dependent on various patient factors, including seizure control, and the provocation method applied. Further larger prospective cohort studies with adequate sample sizes are warranted.

The role of trained and untrained dogs in the detection and warning of seizures

Seizure unpredictability plays a major role in disability and decreased quality of life in people with epilepsy. Dogs have been used to assist people with disabilities and have shown promise in detecting seizures. There have been reports of trained seizure-alerting dogs (SADs) successfully detecting when a seizure is occurring or indicating imminent seizures, allowing patients to take preventative measures. Untrained pet dogs have also shown the ability to detect seizures and provide comfort and protection during and after seizures. Dogs' exceptional olfactory abilities and sensitivity to human cues could contribute to their seizure-detection capabilities. This has been supported by studies in which dogs have distinguished between epileptic seizure and non-seizure sweat samples, probably though the detection of volatile organic compounds (VOCs). However, the existing literature has limitations, with a lack of well-controlled, prospective studies and inconsistencies in reported timings of alerting behaviours. More research is needed to standardize reporting and validate the results. Advances in VOC profiling could aid in distinguishing seizure types and developing rapid and unbiased seizure detection methods. In conclusion, using dogs in epilepsy management shows considerable promise, but further research is needed to fully validate their effectiveness and potential as valuable companions for people with epilepsy.

Differentiation of subclinical and clinical electrographic events in long-term electroencephalographic recordings

OBJECTIVE

With the advent of ultra-long-term recordings for monitoring of epilepsies, the interpretation of results of isolated electroencephalographic (EEG) recordings covering only selected brain regions attracts considerable interest. In this context, the question arises of whether detected ictal EEG patterns correspond to clinically manifest seizures or rather to purely electrographic events, that is, subclinical events.

METHODS

EEG patterns from 268 clinical seizures and 252 subclinical electrographic events from 50 patients undergoing video-EEG monitoring were analyzed. Features extracted included predominant frequency band, duration, association with rhythmic muscle artifacts, spatial extent, and propagation patterns. Classification using logistic regression was performed based on data from the whole dataset of 10-20 system EEG recordings and from a subset of two temporal electrode contacts.

RESULTS

Correct separation of clinically manifest and purely electrographic events based on 10-20 system EEG recordings was possible in up to 83.8% of events, depending on the combination of features included. Correct classification based on two-channel recordings was only slightly inferior, achieving 78.6% accuracy; 74.4% and 74.8%, respectively, of events could be correctly classified when using duration alone with either electrode set, although classification accuracies were lower for some subgroups of seizures, particularly focal aware seizures and epileptic arousals.

SIGNIFICANCE

A correct classification of subclinical versus clinical EEG events was possible in 74%-83% of events based on full EEG recordings, and in 74%-78% when considering only a subset of two electrodes, matching the channel number available from new implantable diagnostic devices. This is a promising outcome, suggesting that ultra-long-term low-channel EEG recordings may provide sufficient information for objective seizure diaries. Intraindividual optimization using high numbers of ictal events may further improve separation, provided that supervised learning with external validation is feasible.

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.

Latencies to the first interictal epileptiform discharges recorded by the electroencephalography in different epileptic patients

PURPOSE

Interictal epileptiform discharges (IEDs) captured in electroencephalography (EEG) have a high diagnostic value for epileptic patients. Extending the recording time may increase the possibility of obtaining IEDs. The purpose of our research was to determine how long it took for various epileptic individuals to receive their first IEDs.

METHODS

We retrospectively analyzed patients who were diagnosed with epilepsy and had no anti-seizure medications (ASMs) between September 2018 and March 2019 in the neurology department of the First Affiliated Hospital of Xi'an Jiaotong University. Each individual underwent a 24-h long-term video electroencephalographic monitoring (VEM) procedure. Clinical information including age, gender, age of seizure onset, frequency of seizures, the interval between last seizure and VEM, and results of neuroimaging were gathered. We also calculated the times from the start of the VEM to the first definite IEDs.

RESULTS

A total of 241 patients were examined, including 191 with focal-onset epilepsy and 50 with generalized epilepsy. In individuals with focal-onset epilepsy, the median latency to the first IED was 63.0 min (IQR 19.0-299.0 min), as compared to 30.0 min (IQR 12.5-62.0 min) in patients with generalized epilepsy (p < 0.001). The latency to the first IED is significantly related to the age of seizure onset (HR = 0.988, p = 0.049), the interval between last seizure and VEM (HR = 0.998, p = 0.013). But it is not correlated with seizure frequency, gender and age.

CONCLUSIONS

IEDs were discovered during 24-h EEG monitoring in 222/241(92.1%) of the epilepsy patients that were included. Compared to focal-onset epilepsy, generalized epilepsy demonstrated a much shorter latency to IED. Patients with late-onset epilepsy or those without recent episodes may require longer EEG monitoring periods.

Complementary structural and functional abnormalities to localise epileptogenic tissue

BACKGROUND

When investigating suitability for epilepsy surgery, people with drug-refractory focal epilepsy may have intracranial EEG (iEEG) electrodes implanted to localise seizure onset. Diffusion-weighted magnetic resonance imaging (dMRI) may be acquired to identify key white matter tracts for surgical avoidance. Here, we investigate whether structural connectivity abnormalities, inferred from dMRI, may be used in conjunction with functional iEEG abnormalities to aid localisation of the epileptogenic zone (EZ), improving surgical outcomes in epilepsy.

METHODS

We retrospectively investigated data from 43 patients (42% female) with epilepsy who had surgery following iEEG. Twenty-five patients (58%) were free from disabling seizures (ILAE 1 or 2) at one year. Interictal iEEG functional, and dMRI structural connectivity abnormalities were quantified by comparison to a normative map and healthy controls. We explored whether the resection of maximal abnormalities related to improved surgical outcomes, in both modalities individually and concurrently. Additionally, we suggest how connectivity abnormalities may inform the placement of iEEG electrodes pre-surgically using a patient case study.

FINDINGS

Seizure freedom was 15 times more likely in patients with resection of maximal connectivity and iEEG abnormalities (p = 0.008). Both modalities separately distinguished patient surgical outcome groups and when used simultaneously, a decision tree correctly separated 36 of 43 (84%) patients.

INTERPRETATION

Our results suggest that both connectivity and iEEG abnormalities may localise epileptogenic tissue, and that these two modalities may provide complementary information in pre-surgical evaluations.

FUNDING

This research was funded by UKRI, CDT in Cloud Computing for Big Data, NIH, MRC, Wellcome Trust and Epilepsy Research UK.

Ultrafast review of ambulatory EEGs with deep learning

OBJECTIVE

Interictal epileptiform discharges (IED) are hallmark biomarkers of epilepsy which are typically detected through visual analysis. Deep learning has shown potential in automating IED detection, which could reduce the burden of visual analysis in clinical practice. This is particularly relevant for ambulatory electroencephalograms (EEGs), as these entail longer review times.

METHODS

We applied a previously trained neural network to an independent dataset of 100 ambulatory EEGs (average duration 20.6 h). From these, 42 EEGs contained IEDs, 25 were abnormal without IEDs and 33 were normal. The algorithm flagged 2 second epochs that it considered IEDs. The EEGs were provided to an expert, who used NeuroCenter EEG to review the recordings. The expert concluded if each recording contained IEDs, and was timed during the process.

RESULTS

The conclusion of the reviewer was the same as the EEG report in 97% of the recordings. Three EEGs contained IEDs that were not detected based on the flagged epochs. Review time for the 100 EEGs was approximately 4 h, with half of the recordings taking <2 minutes to review.

CONCLUSIONS

Our network can be used to reduce time spent on visual analysis in the clinic by 50-75 times with high reliability.

SIGNIFICANCE

Given the large time reduction potential and high success rate, this algorithm can be used in the clinic to aid in visual analysis.

Adhesive Wearable Sensors for Electroencephalography from Hairy Scalp

Electroencephalography has garnered interest for applications in mobile healthcare, human-machine interfaces, and Internet of Things. Conventional electroencephalography relies on wet and dry electrodes. Despite favorable interface impedance of wet electrodes and skin, the application of a large amount of gel at their interface with skin limits the electroencephalography spatial resolution, increases the risk of shorting between electrodes, and makes them unsuited for long-term mobile recording. In contrast, dry electrodes are better suited for long-term recordings but susceptible to motion artifacts. In addition, both wet and dry electrodes are non-adhesive to the hairy scalp and mechanical support, or chemical adhesives are used to hold them in place. Herein, a conical microstructure array (CMSA) based sensor made of carbon nanotube-polydimethylsiloxane composite is reported. The CMSA sensor is fabricated using the innovative, cost-effective, and scalable method of viscosity-controlled dip-pull process. The sensor adheres to the hairy scalp by generating negative pressure in its conical microstructures when it is pressed against scalp. Aided by the application of a trace amount of gel, CMSA sensor establishes good electrical contact with the skin, enabling its applications in mobile electroencephalography over extended periods. Notably, the signal quality of CMSA sensors is comparable to that of medical-grade wet gel electrodes.

The long-term outcomes of patients with negative prolonged ambulatory electroencephalography tests: A cross-sectional follow-up study

Background

Ambulatory electroencephalography (AEEG) recording is an essential aid for detecting interictal discharges and providing a clinical diagnosis. This study aimed to describe long-term outcomes among a cohort of patients who yielded negative results on AEEG at the time of assessment and identify factors associated with contemporary quality of life (QOL) and ultimate epilepsy diagnosis.

Methods

This cross-sectional telephone follow-up study was conducted in June-November 2021 at the Neurology Department in a metropolitan hospital in Sydney, Australia.

Results

In total, 47 of 105 eligible (45%) participants were enrolled. Overall, 21 (45%) participants had been diagnosed with epilepsy at a 12-year follow-up. Taking anti-seizure medication, having experienced a seizure event, and having marriage and education-related characteristics were associated with an epilepsy diagnosis. QOL was found to be associated with age, employment status and history of experience of a seizure event. QOL and an epilepsy diagnosis were not shown to be statistically related.

Conclusions

Nearly half of the participants had received an epilepsy diagnosis at long-term follow-up, despite having tested negative on AEEG at the time of assessment. Prolonged AEEG testing is an important tool to aid the diagnostic process. However, clinical examination, including accurate history taking, is vital in establishing an epilepsy diagnosis.

Machine-learning for the prediction of one-year seizure recurrence based on routine electroencephalography

Predicting seizure recurrence risk is critical to the diagnosis and management of epilepsy. Routine electroencephalography (EEG) is a cornerstone of the estimation of seizure recurrence risk. However, EEG interpretation relies on the visual identification of interictal epileptiform discharges (IEDs) by neurologists, with limited sensitivity. Automated processing of EEG could increase its diagnostic yield and accessibility. The main objective was to develop a prediction model based on automated EEG processing to predict one-year seizure recurrence in patients undergoing routine EEG. We retrospectively selected a consecutive cohort of 517 patients undergoing routine EEG at our institution (training set) and a separate, temporally shifted cohort of 261 patients (testing set). We developed an automated processing pipeline to extract linear and non-linear features from the EEGs. We trained machine learning algorithms on multichannel EEG segments to predict one-year seizure recurrence. We evaluated the impact of IEDs and clinical confounders on performances and validated the performances on the testing set. The receiver operating characteristic area-under-the-curve for seizure recurrence after EEG in the testing set was 0.63 (95% CI 0.55-0.71). Predictions were still significantly above chance in EEGs with no IEDs. Our findings suggest that there are changes other than IEDs in the EEG signal embodying seizure propensity.

Investigation of the effect and availability of ketamine on electroencephalography in cats with temporal lobe epilepsy

In recent years, electroencephalography (EEG) in veterinary medicine has become important not only in the diagnosis of epilepsy, but also in determining the epileptogenic focus. In cats, sedation and immobilization, usually with medetomidine or dexmedetomidine, are necessary to place the electrodes and to obtain stable scalp EEG recordings. In this study, we hypothesized that, for cats with temporal lobe epilepsy (TLE), ketamine, a sedative/anesthetic and N-methyl-D-aspartate (NMDA) antagonist that activates the limbic system and is also used to treat refractory status epilepticus in dogs, would induce sufficient sedation and immobilization for EEG, as well as induce interictal epileptiform discharges (IEDs) that are more pronounced than those induced with medetomidine. We obtained EEG recordings from TLE cats and healthy cats administered either ketamine or medetomidine alone (study 1) or ketamine after medetomidine sedation (study 2). In study 1, the frequency of IEDs showed no statistically significant difference between ketamine and medetomidine in both TLE and healthy cats. Seizures were observed in 75% (9/12) cats of the TLE group with ketamine alone. When ketamine was administered after sedation with medetomidine (study 2), 3/18 cats in the TLE group developed generalized tonic-clonic seizure and 1/18 cats showed subclinical seizure activity. However, no seizures were observed in all healthy cats in both study 1 and study 2. Slow wave activity at 2-4 Hz was observed in many individuals after ketamine administration regardless studies and groups, and quantitative analysis in study 2 showed a trend toward increased delta band activities in both groups. While there was no significant difference in the count of IEDs between medetomidine and ketamine, ketamine caused seizures in cats with TLE similar to their habitual seizure type and with a higher seizure frequency. Our results suggest that ketamine may activate epileptiform discharges during EEG recordings. However, caution should be used for cats with TLE.

Early predictors of remission in children and adolescents with new-onset epilepsy: A prospective study

PURPOSE

This study aims to identify predictive factors of a two-year remission (2YR) in a cohort of children and adolescents with new-onset seizures based on baseline clinical characteristics, initial EEG and brain MRI findings.

METHODS

A prospective cohort of 688 patients with new onset seizures, initiated on treatment with antiseizure medication was evaluated. 2YR was defined as achieving at least two years of seizure freedom during the follow-up period. Multivariable analysis was performed and recursive partition analysis was utilized to develop a decision tree.

RESULTS

The median age at seizure onset was 6.7 years, and the median follow-up was 7.4 years. 548 (79.7%) patients achieved a 2YR during the follow up period. Multivariable analysis found that presence and degree of intellectual and developmental delay (IDD), epileptogenic lesion on brain MRI and a higher number of pretreatment seizures were significantly associated with a lower probability of achieving a 2YR. Recursive partition analysis showed that the absence of IDD was the most important predictor of remission. An epileptogenic lesion was a significant predictor of non-remission only in patients without evidence of IDD, and a high number of pretreatment seizures was a predictive factor in children without IDD and in the absence of an epileptogenic lesion.

CONCLUSION

Our results indicate that it is possible to identify patients at risk of not achieving a 2YR based on variables obtained at the initial evaluation. This could allow for a timely selection of patients who require close follow-up, consideration for neurosurgical intervention, or investigational treatments trials.

EEG datasets for seizure detection and prediction- A review

Electroencephalogram (EEG) datasets from epilepsy patients have been used to develop seizure detection and prediction algorithms using machine learning (ML) techniques with the aim of implementing the learned model in a device. However, the format and structure of publicly available datasets are different from each other, and there is a lack of guidelines on the use of these datasets. This impacts the generatability, generalizability, and reproducibility of the results and findings produced by the studies. In this narrative review, we compiled and compared the different characteristics of the publicly available EEG datasets that are commonly used to develop seizure detection and prediction algorithms. We investigated the advantages and limitations of the characteristics of the EEG datasets. Based on our study, we identified 17 characteristics that make the EEG datasets unique from each other. We also briefly looked into how certain characteristics of the publicly available datasets affect the performance and outcome of a study, as well as the influences it has on the choice of ML techniques and preprocessing steps required to develop seizure detection and prediction algorithms. In conclusion, this study provides a guideline on the choice of publicly available EEG datasets to both clinicians and scientists working to develop a reproducible, generalizable, and effective seizure detection and prediction algorithm.

Computer-assisted analysis of routine electroencephalogram to identify hidden biomarkers of epilepsy: protocol for a systematic review

INTRODUCTION

The diagnosis of epilepsy frequently relies on the visual interpretation of the electroencephalogram (EEG) by a neurologist. The hallmark of epilepsy on EEG is the interictal epileptiform discharge (IED). This marker lacks sensitivity: it is only captured in a small percentage of 30 min routine EEGs in patients with epilepsy. In the past three decades, there has been growing interest in the use of computational methods to analyse the EEG without relying on the detection of IEDs, but none have made it to the clinical practice. We aim to review the diagnostic accuracy of quantitative methods applied to ambulatory EEG analysis to guide the diagnosis and management of epilepsy.

METHODS AND ANALYSIS

The protocol complies with the recommendations for systematic reviews of diagnostic test accuracy by Cochrane. We will search MEDLINE, EMBASE, EBM reviews, IEEE Explore along with grey literature for articles, conference papers and conference abstracts published after 1961. We will include observational studies that present a computational method to analyse the EEG for the diagnosis of epilepsy in adults or children without relying on the identification of IEDs or seizures. The reference standard is the diagnosis of epilepsy by a physician. We will report the estimated pooled sensitivity and specificity, and receiver operating characteristic area under the curve (ROC AUC) for each marker. If possible, we will perform a meta-analysis of the sensitivity and specificity and ROC AUC for each individual marker. We will assess the risk of bias using an adapted QUADAS-2 tool. We will also describe the algorithms used for signal processing, feature extraction and predictive modelling, and comment on the reproducibility of the different studies.

ETHICS AND DISSEMINATION

Ethical approval was not required. Findings will be disseminated through peer-reviewed publication and presented at conferences related to this field.

PROSPERO REGISTRATION NUMBER

CRD42022292261.

EEG as an indispensable tool during and after the COVID-19 pandemic: A review of tribulations and successes

During the coronavirus disease 2019 (COVID-19) pandemic, elective and non-emergent tests and procedures were delayed or suspended in lieu of diverting resources to more emergent treatment of critically ill patients and to avoid the spread and contraction of COVID-19. Further, the workforce was stretched thin, and healthcare facilities saw high turnover rates for full-time and contract employees, which strained the system and reduced the ability to provide clinical services. One of the casualties of these changes was electroencephalography (EEG) procedures, which have been performed less frequently throughout the world since the pandemic. Whether considered routine or emergent, the deferral of EEG studies can cause downstream effects, including a delay in diagnosis and initiation of treatment for epilepsy and non-epileptic seizures resulting in a higher risk of morbidity and mortality. Despite these limitations, the importance and utility of EEG and EEG technologists have been reinforced with the development of COVID-related neurological complications, including encephalopathy and seizures, which require EEG for diagnosis and treatment. Since the pandemic, reliance on remote telemonitoring has further highlighted the value and ease of using EEG. There has also been a heightened interest in rapid EEG devices that non-technologist professionals can attach quickly, allowing minimum patient contact to avoid exposure to COVID-19 and taking advantage of remote EEG monitoring. This review discusses the acute and potential long-term effects of the COVID-19 pandemic on the use and performance of EEG.

Improving automated diagnosis of epilepsy from EEGs beyond IEDs

Objective.Clinical diagnosis of epilepsy relies partially on identifying interictal epileptiform discharges (IEDs) in scalp electroencephalograms (EEGs). This process is expert-biased, tedious, and can delay the diagnosis procedure. Beyond automatically detecting IEDs, there are far fewer studies on automated methods to differentiate epileptic EEGs (potentially without IEDs) from normal EEGs. In addition, the diagnosis of epilepsy based on a single EEG tends to be low. Consequently, there is a strong need for automated systems for EEG interpretation. Traditionally, epilepsy diagnosis relies heavily on IEDs. However, since not all epileptic EEGs exhibit IEDs, it is essential to explore IED-independent EEG measures for epilepsy diagnosis. The main objective is to develop an automated system for detecting epileptic EEGs, both with or without IEDs. In order to detect epileptic EEGs without IEDs, it is crucial to include EEG features in the algorithm that are not directly related to IEDs.Approach.In this study, we explore the background characteristics of interictal EEG for automated and more reliable diagnosis of epilepsy. Specifically, we investigate features based on univariate temporal measures (UTMs), spectral, wavelet, Stockwell, connectivity, and graph metrics of EEGs, besides patient-related information (age and vigilance state). The evaluation is performed on a sizeable cohort of routine scalp EEGs (685 epileptic EEGs and 1229 normal EEGs) from five centers across Singapore, USA, and India.Main results.In comparison with the current literature, we obtained an improved Leave-One-Subject-Out (LOSO) cross-validation (CV) area under the curve (AUC) of 0.871 (Balanced Accuracy (BAC) of 80.9%) with a combination of three features (IED rate, and Daubechies and Morlet wavelets) for the classification of EEGs with IEDs vs. normal EEGs. The IED-independent feature UTM achieved a LOSO CV AUC of 0.809 (BAC of 74.4%). The inclusion of IED-independent features also helps to improve the EEG-level classification of epileptic EEGs with and without IEDs vs. normal EEGs, achieving an AUC of 0.822 (BAC of 77.6%) compared to 0.688 (BAC of 59.6%) for classification only based on the IED rate. Specifically, the addition of IED-independent features improved the BAC by 21% in detecting epileptic EEGs that do not contain IEDs.Significance.These results pave the way towards automated detection of epilepsy. We are one of the first to analyze epileptic EEGs without IEDs, thereby opening up an underexplored option in epilepsy diagnosis.

Localizing epileptogenic network from SEEG using non-linear correlation, mutual information and graph theory analysis

The key challenge in epilepsy surgery is precise localization and removal of the epileptogenic zone (EZ) from the brain. Localization of the epileptogenic network by visual analysis of intracranial EEG is extremely difficult. In this retrospective study, we used interictal connectivity and graph theory analysis on intracranial EEG to better delineate the epileptogenic zone. Patients who underwent surgery for drug-refractory mesial temporal and neocortical epilepsy were included. Computational measures, such as h2 nonlinear correlation and mutual information, were used to estimate the interdependency of intracranial EEGs. We observed that the Out-Degree, Out-Strength, and Betweenness centrality (graph properties) were the best predictors of EZ. From the results, we also found that graph properties with a normalized value above 0.75 were found to be a useful measure to localize the EZ with a sensitivity of 87.88 and a specificity of 87.13. Our results also validate that frequently occurring types of interictal fast discharges (IFD) with connectivity measures and graph properties can better localize the EZ. We foresee graph theory analysis of interictal intracranial EEG data can help precise localization of EZ for cortical resection as well as in minimally invasive radiofrequency ablation of epileptogenic hubs. Further, prospective validation is required for clinical use.

Implementation of a Morphological Filter for Removing Spikes from the Epileptic Brain Signals to Improve Identification Ripples

Epilepsy is a very common disease affecting at least 1% of the population, comprising a number of over 50 million people. As many patients suffer from the drug-resistant version, the number of potential treatment methods is very small. However, since not only the treatment of epilepsy, but also its proper diagnosis or observation of brain signals from recordings are important research areas, in this paper, we address this very problem by developing a reliable technique for removing spikes and sharp transients from the baseline of the brain signal using a morphological filter. This allows much more precise identification of the so-called epileptic zone, which can then be resected, which is one of the methods of epilepsy treatment. We used eight patients with 5 KHz data set and depended upon the Staba 2002 algorithm as a reference to detect the ripples. We found that the average sensitivity and false detection rate of our technique are significant, and they are ∼94% and ∼14%, respectively.

Computerized application for epilepsy in China: Does the era of artificial intelligence comes?

Epilepsy, one of the most common neurological diseases in China, is notorious for its spontaneous, unprovoked and recurrent seizures. The etiology of epilepsy varies among individual patients, including congenital gene mutation, traumatic injury, infections, etc. This heterogeneity partly hampered the accurate diagnosis and choice of appropriate treatments. Encouragingly, great achievements have been achieved in computational science, making it become a key player in medical fields gradually and bringing new hope for rapid and accurate diagnosis as well as targeted therapies in epilepsy. Here, we historically review the advances of computerized applications in epilepsy-especially those tremendous findings achieved in China-for different purposes including seizure prediction, localization of epileptogenic zone, post-surgical prognosis, etc. Special attentions are paid to the great progress based on artificial intelligence (AI), which is more "sensitive", "smart" and "in-depth" than human capacities. At last, we give a comprehensive discussion about the disadvantages and limitations of current computerized applications for epilepsy and propose some future directions as further stepping stones to embrace "the era of AI" in epilepsy.

Neuronal synchrony abnormalities associated with subclinical epileptiform activity in early-onset Alzheimer's disease

Since the first demonstrations of network hyperexcitability in scientific models of Alzheimer's disease, a growing body of clinical studies have identified subclinical epileptiform activity and associated cognitive decline in patients with Alzheimer's disease. An obvious problem presented in these studies is lack of sensitive measures to detect and quantify network hyperexcitability in human subjects. In this study we examined whether altered neuronal synchrony can be a surrogate marker to quantify network hyperexcitability in patients with Alzheimer's disease. Using magnetoencephalography (MEG) at rest, we studied 30 Alzheimer's disease patients without subclinical epileptiform activity, 20 Alzheimer's disease patients with subclinical epileptiform activity and 35 age-matched controls. Presence of subclinical epileptiform activity was assessed in patients with Alzheimer's disease by long-term video-EEG and a 1-h resting MEG with simultaneous EEG. Using the resting-state source-space reconstructed MEG signal, in patients and controls we computed the global imaginary coherence in alpha (8-12 Hz) and delta-theta (2-8 Hz) oscillatory frequencies. We found that Alzheimer's disease patients with subclinical epileptiform activity have greater reductions in alpha imaginary coherence and greater enhancements in delta-theta imaginary coherence than Alzheimer's disease patients without subclinical epileptiform activity, and that these changes can distinguish between Alzheimer's disease patients with subclinical epileptiform activity and Alzheimer's disease patients without subclinical epileptiform activity with high accuracy. Finally, a principal component regression analysis showed that the variance of frequency-specific neuronal synchrony predicts longitudinal changes in Mini-Mental State Examination in patients and controls. Our results demonstrate that quantitative neurophysiological measures are sensitive biomarkers of network hyperexcitability and can be used to improve diagnosis and to select appropriate patients for the right therapy in the next-generation clinical trials. The current results provide an integrative framework for investigating network hyperexcitability and network dysfunction together with cognitive and clinical correlates in patients with Alzheimer's disease.

Cognitive features of adult focal epilepsy with unknown etiology revealed by the trail making test

PURPOSE

The purpose of this study was to investigate whether the Trail Making Test (TMT) can clarify cognitive dysfunction in focal epilepsy with unknown etiology.

METHODS

Trail Making Test data were obtained from patients with focal epilepsy with no structural abnormalities on magnetic resonance imaging, history or coexistence of central nerve system diseases, intellectual disability, psychiatric disorders, or medications that might interfere with cognitive function. We performed multiple regression analyses with TMT scores as dependent variables and clinical features as independent variables.

RESULTS

We enrolled 125 patients in the study. The statistical analyses revealed that taking fewer antiseizure medications, having a longer duration of education, exhibiting left non-temporal epileptic discharge, and exhibiting right temporal epileptic discharge were associated with shorter time to complete the TMT-A and TMT-B. Older age at the time of last seizure was associated with longer time to complete the TMT-B. In addition, a longer active seizure period was associated with longer time to complete the TMT-A subtracted from time to complete the TMT-B.

CONCLUSIONS

This study indicated that the TMT can be used for assessing the cumulative effects of seizures and the effects of polypharmacy on cognitive function in patients with focal epilepsy. Furthermore, our results indicated that the visuospatial cognitive ability associated with the TMT may depend on the site of epileptic focus of non-lesional focal epilepsy.

Altered adult neurogenesis and gliogenesis in patients with mesial temporal lobe epilepsy

The hippocampus is the most common seizure focus in people. In the hippocampus, aberrant neurogenesis plays a critical role in the initiation and progression of epilepsy in rodent models, but it is unknown whether this also holds true in humans. To address this question, we used immunofluorescence on control healthy hippocampus and surgical resections from mesial temporal lobe epilepsy (MTLE), plus neural stem-cell cultures and multi-electrode recordings of ex vivo hippocampal slices. We found that a longer duration of epilepsy is associated with a sharp decline in neuronal production and persistent numbers in astrogenesis. Further, immature neurons in MTLE are mostly inactive, and are not observed in cases with local epileptiform-like activity. However, immature astroglia are present in every MTLE case and their location and activity are dependent on epileptiform-like activity. Immature astroglia, rather than newborn neurons, therefore represent a potential target to continually modulate adult human neuronal hyperactivity.

Prevalence of depression, risk factors, and quality of life in patients with epilepsy in a remote area of western Rajasthan

BACKGROUND

Depression is a common psychiatric disorder in patients with epilepsy. This study aimed to determine the prevalence and related risk factors for depression among people with epilepsy and their quality of life.

METHODS

Hospital-based cross-sectional study was carried out among 352 individuals with epilepsy from April 2020 to September 2020. Purposive sampling was used to recruit participants over a specified period. The Beck's Depression Inventory-II (BDI-II) was used to measure depression severity, the Perceived Stress Scale (PSS) to assess stress level, the Oslo 3 Social Support Scale (OSSS-3) to assess social support, and the WHOQOL-BREF scale to estimate quality of life (QoL). The statistical analysis was carried out using SPSS version 20. Logistic regression analysis was done to determine the risk factors for depression.

RESULTS

A total of 352 study participants were considered in the study. The prevalence of depression was found to be 41.19%. In the final multivariate analysis, the independent variables that influenced depression were socioeconomic status (OR 2.75, CI 1.21-5.41), frequency of seizures in the previous year (OR 2.17, CI 1.08-5.26), duration of illness (OR 3.49, CI 1.03-7.52), and poor social support (OR 6.37, CI 1.85-9.48) at p-value < 0.05. When compared to patients without depression, the average mean score (SD) in all four domains was lower in physical 39.01 (4.61), psychological 43.93 (8.13), social 52.89 (10.44), and environmental domains 47.14 (6.99) in patients with depression in BREF quality-of-life scale. There was a statistically significant difference in the physical, psychological, and social domains (p-value < 0.05).

CONCLUSION

In people with epilepsy, depression was quite common. Patients that were depressed had a lower QoL. Clinicians should pay special attention to people with epilepsy who lack social support, have low socioeconomic status, longer duration of illness, and have more seizure frequency. Qualified clinicians should do early depression-focused screenings for people with epilepsy.

Minimum standards for inpatient long-term video-EEG monitoring: A clinical practice guideline of the international league against epilepsy and international federation of clinical neurophysiology

The objective of this clinical practice guideline is to provide recommendations on the indications and minimum standards for inpatient long-term video-electroencephalographic monitoring (LTVEM). The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology develop guidelines aligned with the Epilepsy Guidelines Task Force. We reviewed published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement. We found limited high-level evidence aimed at specific aspects of diagnosis for LTVEM performed to evaluate patients with seizures and nonepileptic events (see Table S1). For classification of evidence, we used the Clinical Practice Guideline Process Manual of the American Academy of Neurology. We formulated recommendations for the indications, technical requirements, and essential practice elements of LTVEM to derive minimum standards used in the evaluation of patients with suspected epilepsy using GRADE (Grading of Recommendations, Assessment, Development, and Evaluation). Further research is needed to obtain evidence about long-term outcome effects of LTVEM and establish its clinical utility.

Minimum standards for inpatient long-term video-electroencephalographic monitoring: A clinical practice guideline of the International League Against Epilepsy and International Federation of Clinical Neurophysiology

The objective of this clinical practice guideline is to provide recommendations on the indications and minimum standards for inpatient long-term video-electroencephalographic monitoring (LTVEM). The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology develop guidelines aligned with the Epilepsy Guidelines Task Force. We reviewed published evidence using the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) statement. We found limited high-level evidence aimed at specific aspects of diagnosis for LTVEM performed to evaluate patients with seizures and nonepileptic events. For classification of evidence, we used the Clinical Practice Guideline Process Manual of the American Academy of Neurology. We formulated recommendations for the indications, technical requirements, and essential practice elements of LTVEM to derive minimum standards used in the evaluation of patients with suspected epilepsy using GRADE (Grading of Recommendations Assessment, Development, and Evaluation). Further research is needed to obtain evidence about long-term outcome effects of LTVEM and to establish its clinical utility.

Psychiatrists should investigate their patients less

Psychiatrists often order investigations such as blood tests, neuroimaging and electroencephalograms for their patients. Rationales include ruling out 'organic' causes of psychiatric presentations, providing baseline parameters before starting psychotropic medications, and screening for general cardiometabolic health. Hospital protocols often recommend an extensive panel of blood tests on admission to a psychiatric ward. In this Against the Stream article, we argue that many of these investigations are at best useless and at worst harmful: the yield of positive findings that change clinical management is extremely low; special investigations are a poor substitute for a targeted history and examination; and incidental findings may cause anxiety and further unwarranted investigation. Cognitive and cultural reasons why over-investigation continues are discussed. We conclude by encouraging a more targeted approach guided by a thorough bedside clinical assessment.

Towards Automatic Identification of Epileptic Recordings in Long-term EEG Monitoring

Electroencephalogram (EEG) is a crucial tool in the diagnosis and management of epilepsy. The process of analyzing EEG is time consuming leading to the development of seizure detection algorithms to aid its analysis. This approach is limited since it requires seizures to occur during monitoring periods and can often lead to misdiagnosis in cases where seizure occurrence is rare. For such cases, it has been shown that the interictal periods in EEG signals, which is the predominant state in long-term monitoring, can be useful for the diagnosis of epilepsy. This paper presents an algorithm, using the information in interictal periods, to discriminate between long-term EEG recordings of epilepsy patients and healthy subjects. It extracts several time and frequency-time domain features from the signals and classifies them using an ensemble classifier, achieving 100% sensitivity and 98.7% specificity in classifying 267 recordings from 105 subjects. The results demonstrate the feasibility of this approach to reliably identify EEG recordings of epilepsy subjects automatically which can be highly useful to facilitate screening and diagnosis of epilepsy, especially in those parts of the world where there is a lack of trained personnel for interpreting EEG signals.

Advances in Epilepsy Surgery

BACKGROUND

A large number of patients have epilepsy that is intractable and adversely affects a child's lifelong experience with addition societal burden that is disabling and expensive. The last two decades have seen a major explosion of new antiseizure medication options. Despite these advances, children with epilepsy continue to have intractable seizures. An option that has been long available but little used is epilepsy surgery to control intractable epilepsy.

METHODS

This article is a review of the literature as well as published opinions.

RESULTS

Epilepsy surgery in pediatrics is an underused modality to effectively treat children with epilepsy. Adverse effects of medication should be weighed against risks of surgery as well as risks of nonefficacy.

CONCLUSIONS

We discuss an approach to selecting the appropriate pediatric patient for consideration, a detailed evaluation including necessary evaluation, and the creation of an algorithm to approach patients with both generalized and focal epilepsy. We then discuss surgical options available including outcome data. New modalities are also addressed including high-frequency ultrasound and co-registration techniques including magnetic resonance imaging-guided laser therapy.

Longitudinal impact of cannabidiol on EEG measures in subjects with treatment-resistant epilepsy

OBJECTIVE

To assess the longitudinal impact of highly purified cannabidiol (CBD) on the electroencephalogram (EEG) of children and adults.

METHODS

Participants received an EEG prior to starting CBD, after approximately 12 weeks of CBD (FU1) and after approximately one year of CBD therapy (FU2). Longitudinal changes in five EEG measures (background frequency, focal slowing, reactivity, frequency of interictal, and ictal discharges) were examined following CBD exposure. Data were compared between pediatric and adult groups at two follow-up time points and within groups over time. Population-averaged models with generalized estimation equations or linear mixed effects models were used to analyze data where appropriate. Correlation analysis was used to assess any association between changes in seizure frequency and changes in EEG interictal discharge (IED) frequency. An alpha level of 5% was used to assess statistical significance.

RESULTS

At FU1, the adult group showed significant decrease in IED/minute (IDR 0.07, 95% CI [0.04, 0.14], P < 0.001); a nonsignificant decrease was observed among children (IDR 0.87, 95% CI [0.47, 0.64], P = 0.67). The difference in changes over time between participant groups was significant after adjusting for last CBD dose (IDR 11.8, 95% CI [4.86, 28.65], P < 0.0001). At FU2 both groups showed significant reduction from baseline after controlling for last CBD dose. This decrease was more pronounced in children (IDR 15.38, 95% CI [4.93, 47.99], P < 0.001). There was no significant correlation between changes in seizure frequency and EEG IED frequency at each timepoint (P = 0.542, 0.917 and 0.989 from baseline to FU1, FU1 to FU2 and baseline to FU2, respectively).

SIGNIFICANCE

This longitudinal EEG study shows that highly-purified plant-derived CBD has positive effects on interictal epileptiform discharge frequency but no effects on other EEG measures. The effect of CBD does not appear to be dose or treatment-duration dependent.

Chloral hydrate as a sedating agent for neurodiagnostic procedures in children

BACKGROUND

This is an updated version of a Cochrane Review published in 2017. Paediatric neurodiagnostic investigations, including brain neuroimaging and electroencephalography (EEG), play an important role in the assessment of neurodevelopmental disorders. The use of an appropriate sedative agent is important to ensure the successful completion of the neurodiagnostic procedures, particularly in children, who are usually unable to remain still throughout the procedure.

OBJECTIVES

To assess the effectiveness and adverse effects of chloral hydrate as a sedative agent for non-invasive neurodiagnostic procedures in children.

SEARCH METHODS

We searched the following databases on 14 May 2020, with no language restrictions: the Cochrane Register of Studies (CRS Web) and MEDLINE (Ovid, 1946 to 12 May 2020). CRS Web includes randomised or quasi-randomised controlled trials from PubMed, Embase, ClinicalTrials.gov, the World Health Organization International Clinical Trials Registry Platform, the Cochrane Central Register of Controlled Trials (CENTRAL), and the specialised registers of Cochrane Review Groups including Cochrane Epilepsy.

SELECTION CRITERIA

Randomised controlled trials that assessed chloral hydrate agent against other sedative agent(s), non-drug agent(s), or placebo.

DATA COLLECTION AND ANALYSIS

Two review authors independently evaluated studies identified by the search for their eligibility, extracted data, and assessed risk of bias. Results were expressed in terms of risk ratio (RR) for dichotomous data and mean difference (MD) for continuous data, with 95% confidence intervals (CIs).

MAIN RESULTS

We included 16 studies with a total of 2922 children. The methodological quality of the included studies was mixed. Blinding of the participants and personnel was not achieved in most of the included studies, and three of the 16 studies were at high risk of bias for selective reporting. Evaluation of the efficacy of the sedative agents was also underpowered, with all the comparisons performed in small studies. Fewer children who received oral chloral hydrate had sedation failure compared with oral promethazine (RR 0.11, 95% CI 0.01 to 0.82; 1 study; moderate-certainty evidence). More children who received oral chloral hydrate had sedation failure after one dose compared to intravenous pentobarbital (RR 4.33, 95% CI 1.35 to 13.89; 1 study; low-certainty evidence), but there was no clear difference after two doses (RR 3.00, 95% CI 0.33 to 27.46; 1 study; very low-certainty evidence). Children with oral chloral hydrate had more sedation failure compared with rectal sodium thiopental (RR 1.33, 95% CI 0.60 to 2.96; 1 study; moderate-certainty evidence) and music therapy (RR 17.00, 95% CI 2.37 to 122.14; 1 study; very low-certainty evidence). Sedation failure rates were similar between groups for comparisons with oral dexmedetomidine, oral hydroxyzine hydrochloride, oral midazolam and oral clonidine. Children who received oral chloral hydrate had a shorter time to adequate sedation compared with those who received oral dexmedetomidine (MD -3.86, 95% CI -5.12 to -2.6; 1 study), oral hydroxyzine hydrochloride (MD -7.5, 95% CI -7.85 to -7.15; 1 study), oral promethazine (MD -12.11, 95% CI -18.48 to -5.74; 1 study) (moderate-certainty evidence for three aforementioned outcomes), rectal midazolam (MD -95.70, 95% CI -114.51 to -76.89; 1 study), and oral clonidine (MD -37.48, 95% CI -55.97 to -18.99; 1 study) (low-certainty evidence for two aforementioned outcomes). However, children with oral chloral hydrate took longer to achieve adequate sedation when compared with intravenous pentobarbital (MD 19, 95% CI 16.61 to 21.39; 1 study; low-certainty evidence), intranasal midazolam (MD 12.83, 95% CI 7.22 to 18.44; 1 study; moderate-certainty evidence), and intranasal dexmedetomidine (MD 2.80, 95% CI 0.77 to 4.83; 1 study, moderate-certainty evidence). Children who received oral chloral hydrate appeared significantly less likely to complete neurodiagnostic procedure with child awakening when compared with rectal sodium thiopental (RR 0.95, 95% CI 0.83 to 1.09; 1 study; moderate-certainty evidence). Chloral hydrate was associated with a higher risk of the following adverse events: desaturation versus rectal sodium thiopental (RR 5.00, 95% 0.24 to 102.30; 1 study), unsteadiness versus intranasal dexmedetomidine (MD 10.21, 95% CI 0.58 to 178.52; 1 study), vomiting versus intranasal dexmedetomidine (MD 10.59, 95% CI 0.61 to 185.45; 1 study) (low-certainty evidence for aforementioned three outcomes), and crying during administration of sedation versus intranasal dexmedetomidine (MD 1.39, 95% CI 1.08 to 1.80; 1 study, moderate-certainty evidence). Chloral hydrate was associated with a lower risk of the following: diarrhoea compared with rectal sodium thiopental (RR 0.04, 95% CI 0.00 to 0.72; 1 study), lower mean diastolic blood pressure compared with sodium thiopental (MD 7.40, 95% CI 5.11 to 9.69; 1 study), drowsiness compared with oral clonidine (RR 0.44, 95% CI 0.30 to 0.64; 1 study), vertigo compared with oral clonidine (RR 0.15, 95% CI 0.01 to 2.79; 1 study) (moderate-certainty evidence for aforementioned four outcomes), and bradycardia compared with intranasal dexmedetomidine (MD 0.17, 95% CI 0.05 to 0.59; 1 study; high-certainty evidence). No other adverse events were significantly associated with chloral hydrate, although there was an increased risk of combined adverse events overall (RR 7.66, 95% CI 1.78 to 32.91; 1 study; low-certainty evidence).

AUTHORS' CONCLUSIONS

The certainty of evidence for the comparisons of oral chloral hydrate against several other methods of sedation was variable. Oral chloral hydrate appears to have a lower sedation failure rate when compared with oral promethazine. Sedation failure was similar between groups for other comparisons such as oral dexmedetomidine, oral hydroxyzine hydrochloride, and oral midazolam. Oral chloral hydrate had a higher sedation failure rate when compared with intravenous pentobarbital, rectal sodium thiopental, and music therapy. Chloral hydrate appeared to be associated with higher rates of adverse events than intranasal dexmedetomidine. However, the evidence for the outcomes for oral chloral hydrate versus intravenous pentobarbital, rectal sodium thiopental, intranasal dexmedetomidine, and music therapy was mostly of low certainty, therefore the findings should be interpreted with caution. Further research should determine the effects of oral chloral hydrate on major clinical outcomes such as successful completion of procedures, requirements for an additional sedative agent, and degree of sedation measured using validated scales, which were rarely assessed in the studies included in this review. The safety profile of chloral hydrate should be studied further, especially for major adverse effects such as oxygen desaturation.

Slow oscillations open susceptible time windows for epileptic discharges

Objective

In patients with epilepsy, interictal epileptic discharges are a diagnostic hallmark of epilepsy and represent abnormal, so‐called “irritative” activity that disrupts normal cognitive functions. Despite their clinical relevance, their mechanisms of generation remain poorly understood. It is assumed that brain activity switches abruptly, unpredictably, and supposedly randomly to these epileptic transients. We aim to study the period preceding these epileptic discharges, to extract potential proepileptogenic mechanisms supporting their expression.

Methods

We used multisite intracortical recordings from patients who underwent intracranial monitoring for refractory epilepsy, the majority of whom had a mesial temporal lobe seizure onset zone. Our objective was to evaluate the existence of proepileptogenic windows before interictal epileptic discharges. We tested whether the amplitude and phase synchronization of slow oscillations (.5–4 Hz and 4–7 Hz) increase before epileptic discharges and whether the latter are phase‐locked to slow oscillations. Then, we tested whether the phase‐locking of neuronal activity (assessed by high‐gamma activity, 60–160 Hz) to slow oscillations increases before epileptic discharges to provide a potential mechanism linking slow oscillations to interictal activities.

Results

Changes in widespread slow oscillations anticipate upcoming epileptic discharges. The network extends beyond the irritative zone, but the increase in amplitude and phase synchronization is rather specific to the irritative zone. In contrast, epileptic discharges are phase‐locked to widespread slow oscillations and the degree of phase‐locking tends to be higher outside the irritative zone. Then, within the irritative zone only, we observe an increased coupling between slow oscillations and neuronal discharges before epileptic discharges.

Significance

Our results show that epileptic discharges occur during vulnerable time windows set up by a specific phase of slow oscillations. The specificity of these permissive windows is further reinforced by the increased coupling of neuronal activity to slow oscillations. These findings contribute to our understanding of epilepsy as a distributed oscillopathy and open avenues for future neuromodulation strategies aiming at disrupting proepileptic mechanisms.

Large-scale collaboration in ENIGMA-EEG: A perspective on the meta-analytic approach to link neurological and psychiatric liability genes to electrophysiological brain activity

BACKGROUND AND PURPOSE

The ENIGMA-EEG working group was established to enable large-scale international collaborations among cohorts that investigate the genetics of brain function measured with electroencephalography (EEG). In this perspective, we will discuss why analyzing the genetics of functional brain activity may be crucial for understanding how neurological and psychiatric liability genes affect the brain.

METHODS

We summarize how we have performed our currently largest genome-wide association study of oscillatory brain activity in EEG recordings by meta-analyzing the results across five participating cohorts, resulting in the first genome-wide significant hits for oscillatory brain function located in/near genes that were previously associated with psychiatric disorders. We describe how we have tackled methodological issues surrounding genetic meta-analysis of EEG features. We discuss the importance of harmonizing EEG signal processing, cleaning, and feature extraction. Finally, we explain our selection of EEG features currently being investigated, including the temporal dynamics of oscillations and the connectivity network based on synchronization of oscillations.

RESULTS

We present data that show how to perform systematic quality control and evaluate how choices in reference electrode and montage affect individual differences in EEG parameters.

CONCLUSION

The long list of potential challenges to our large-scale meta-analytic approach requires extensive effort and organization between participating cohorts; however, our perspective shows that these challenges are surmountable. Our perspective argues that elucidating the genetic of EEG oscillatory activity is a worthwhile effort in order to elucidate the pathway from gene to disease liability.

A deep learning framework with multi-perspective fusion for interictal epileptiform discharges detection in scalp electroencephalogram

Objective.Interictal epileptiform discharges (IEDs) are an important and widely accepted biomarker used in the diagnosis of epilepsy based on scalp electroencephalography (EEG). Because the visual detection of IEDs has various limitations, including high time consumption and high subjectivity, a faster, more robust, and automated IED detector is strongly in demand.Approach.Based on deep learning, we proposed an end-to-end framework with multi-scale morphologic features in the time domain and correlation in sensor space to recognize IEDs from raw scalp EEG.Main Results.Based on a balanced dataset of 30 patients with epilepsy, the results of the five-fold (leave-6-patients-out) cross-validation shows that our model achieved state-of-the-art detection performance (accuracy: 0.951, precision: 0.973, sensitivity: 0.938, specificity: 0.968, F1 score: 0.954, AUC: 0.973). Furthermore, our model maintained excellent IED detection rates in an independent test on three datasets.Significance.The proposed model could be used to assist neurologists in clinical EEG interpretation of patients with epilepsy. Additionally, this approach combines multi-level output and correlation among EEG sensors and provides new ideas for epileptic biomarker detection in scalp EEG.

Anterior thalamic stimulation improves working memory precision judgments

BACKGROUND

The anterior nucleus of thalamus (ANT) has been suggested as an extended hippocampal system. The circuit of ANT and hippocampus has been widely demonstrated to be associated with memory function. Both lesions to each region and disrupting inter-regional information flow can induce working memory impairment. However, the role of this circuit in working memory precision remains unknown.

OBJECTIVE

To test the role of the hippocampal-anterior thalamic pathway in working memory precision, we delivered intracranially electrical stimulation to the ANT. We hypothesize that ANT stimulation can improve working memory precision.

METHODS

Presurgical epilepsy patients with depth electrodes in ANT and hippocampus were recruited to perform a color-recall working memory task. Participants were instructed to point out the color they were supposed to recall by clicking a point on the color wheel, while the intracranial EEG data were synchronously recorded. For randomly selected half trials, a bipolar electrical stimulation was delivered to the ANT electrodes.

RESULTS

We found that compared to non-stimulation trials, working memory precision judgements were significantly improved for stimulation trials. ANT electrical stimulation significantly increased spectral power of gamma (30-100 Hz) oscillations and decreased interictal epileptiform discharges (IED) in the hippocampus. Moreover, the increased gamma power during the pre-stimulus and retrieval period predicted the improvement of working memory precision judgements.

CONCLUSION

ANT electrical stimulation can improve working memory precision judgements and modulate hippocampal gamma activity, providing direct evidence on the role of the human hippocampal-anterior thalamic axis in working memory precision.

Time-Frequency Decomposition of Scalp Electroencephalograms Improves Deep Learning-Based Epilepsy Diagnosis

Epilepsy diagnosis based on Interictal Epileptiform Discharges (IEDs) in scalp electroencephalograms (EEGs) is laborious and often subjective. Therefore, it is necessary to build an effective IED detector and an automatic method to classify IED-free versus IED EEGs. In this study, we evaluate features that may provide reliable IED detection and EEG classification. Specifically, we investigate the IED detector based on convolutional neural network (ConvNet) with different input features (temporal, spectral, and wavelet features). We explore different ConvNet architectures and types, including 1D (one-dimensional) ConvNet, 2D (two-dimensional) ConvNet, and noise injection at various layers. We evaluate the EEG classification performance on five independent datasets. The 1D ConvNet with preprocessed full-frequency EEG signal and frequency bands (delta, theta, alpha, beta) with Gaussian additive noise at the output layer achieved the best IED detection results with a false detection rate of 0.23/min at 90% sensitivity. The EEG classification system obtained a mean EEG classification Leave-One-Institution-Out (LOIO) cross-validation (CV) balanced accuracy (BAC) of 78.1% (area under the curve (AUC) of 0.839) and Leave-One-Subject-Out (LOSO) CV BAC of 79.5% (AUC of 0.856). Since the proposed classification system only takes a few seconds to analyze a 30-min routine EEG, it may help in reducing the human effort required for epilepsy diagnosis.

Epilepsy: A Clinical Overview

The diagnosis and treatment of seizures and epilepsy is a common task of the physician. Approximately 1 in 10 people will have a seizure during their lifetime. Epilepsy is the tendency to have unprovoked seizures. Epilepsy is the fourth most common neurological disorder and affects 1 in 26 people in the United States and 65 million people worldwide. Evaluation of a patient presenting with a seizure involves excluding an underlying neurologic or medical condition, classifying the seizure type and determining if the patient has epilepsy. Proper treatment requires accurate diagnosis of the epilepsy type and syndrome and use of a medication that is effective and without adverse effects. Most patients can achieve complete seizure control with medication, but if medication is unsuccessful, surgical treatment can be an option. Special situations in the care of people with epilepsy include status epilepticus, women with epilepsy, the older adult, and safety issues.

Monitoring the frequency and duration of epileptic seizures: "A journey through time"

Seizure monitoring plays an undeniably important role in diagnosing and managing epileptic seizures. Establishing the frequency and duration of seizures is crucial for assessing the burden of this chronic neurological disease, selecting treatment methods, determining how frequently these methods are applied, and informing short and long-term therapeutic decisions. Over the years, seizure monitoring tools and methods have evolved and become increasingly sophisticated; from home seizure diaries to EEG monitoring to cutting-edge responsive neurostimulation systems. In this article, the various methods of seizure monitoring are reviewed.

Characterizing the electrophysiological abnormalities in visually reviewed normal EEGs of drug-resistant focal epilepsy patients

Routine scalp EEG is essential in the clinical diagnosis and management of epilepsy. However, a normal scalp EEG (based on expert visual review) recorded from a patient with epilepsy can cause delays in diagnosis and clinical care delivery. Here, we investigated whether normal EEGs might contain subtle electrophysiological clues of epilepsy. Specifically, we investigated (i) whether there are indicators of abnormal brain electrophysiology in normal EEGs of epilepsy patients, and (ii) whether such abnormalities are modulated by the side of the brain generating seizures in focal epilepsy. We analysed awake scalp EEG recordings of age-matched groups of 144 healthy individuals and 48 individuals with drug-resistant focal epilepsy who had normal scalp EEGs. After preprocessing, using a bipolar montage of eight channels, we extracted the fraction of spectral power in the alpha band (8-13 Hz) relative to a wide band of 0.5-40 Hz within 10-s windows. We analysed the extracted features for (i) the extent to which people with drug-resistant focal epilepsy differed from healthy subjects, and (ii) whether differences within the drug-resistant focal epilepsy patients were related to the hemisphere generating seizures. We then used those differences to classify whether an EEG is likely to have been recorded from a person with drug-resistant focal epilepsy, and if so, the epileptogenic hemisphere. Furthermore, we tested the significance of these differences while controlling for confounders, such as acquisition system, age and medications. We found that the fraction of alpha power is generally reduced (i) in drug-resistant focal epilepsy compared to healthy controls, and (ii) in right-handed drug-resistant focal epilepsy subjects with left hemispheric seizures compared to those with right hemispheric seizures, and that the differences are most prominent in the frontal and temporal regions. The fraction of alpha power yielded area under curve values of 0.83 in distinguishing drug-resistant focal epilepsy from healthy and 0.77 in identifying the epileptic hemisphere in drug-resistant focal epilepsy patients. Furthermore, our results suggest that the differences in alpha power are greater when compared with differences attributable to acquisition system differences, age and medications. Our findings support that EEG-based measures of normal brain function, such as the normalized spectral power of alpha activity, may help identify patients with epilepsy even when an EEG does not contain any epileptiform activity, recorded seizures or other abnormalities. Although alpha abnormalities are unlikely to be disease-specific, we propose that such abnormalities may provide a higher pre-test probability for epilepsy when an individual being screened for epilepsy has a normal EEG on visual assessment.

The influence of the abundance and morphology of epileptiform discharges on diagnostic accuracy: How many spikes you need to spot in an EEG

OBJECTIVE

The operational definition of interictal epileptiform discharges (IEDs) of the International Federation of Clinical Neurophysiology (IFCN) described six morphological criteria. Our objective was to assess the impact of pattern-repetition in the EEG-recording, on the diagnostic accuracy of using the IFCN criteria. For clinical implementation, specificity over 95% was set as target.

METHODS

Interictal EEG-recordings of 20-minutes, containing sharp-transients, from 60 patients (30 with epilepsy and 30 with non-epileptic paroxysmal events) were evaluated by three experts, who first marked IEDs solely based on expert opinion, and then, independently from the first session evaluated the presence of the IFCN criteria for each sharp-transient. The gold standard was derived from long-term video-EEG recordings of the patientś habitual paroxysmal episodes.

RESULTS

Presence of at least one discharge fulfilling five criteria provided a specificity of 100% (sensitivity: 70%). For discharges fulfilling fewer criteria, a higher number of discharges was needed to keep the specificity over 95% (5 discharges, when only 3 criteria were fulfilled). A sequential combination of these sets of criteria and thresholds provided a specificity of 97% and sensitivity of 80%.

CONCLUSIONS

Pattern-repetition and IED morphology influence diagnostic accuracy.

SIGNIFICANCE

Systematic application of these criteria will improve quality of clinical EEG interpretation.