Clinical utility of EEG in diagnosing and monitoring epilepsy in adults

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.

Novel and reappraised wide-band EEG findings in migraineurs: Its correlation with several clinical variables

OBJECTIVE

Cortical spreading depolarization is one possible pathogenesis of migraine, of which slow neurophysiological change is barely recorded in conventional EEG settings. Using wide-band EEG conditions, we reappraised the features of EEG in migraineurs, including subdelta-band EEG changes.

METHODS

This retrospective study included 144 patients with migraine. We delineated EEG of focal delta slow (FDS) (1-4 Hz) by time constant (TC) 0.3 s and focal subdelta slow (FSDS) (< 1 Hz) by TC 2 s. Relationships between clinical variables and EEG findings were evaluated.

RESULTS

Of 144 patients, 39 had aura and 105 did not. FSDS and FDS were observed in 38 and 58 patients, respectively. No EEG was recorded during the aura. In multivariate analysis with the phase of migraine, family history, age, and percentage of sleep during EEG recording, the phase of migraine was related to the occurrence of FSDS (postdrome vs interictal, prodrome, and headache respectively (OR = 49.00 [95% CI = 3.89-616.66], 46.28 [2.99-715.78], 32.79 [2.23-481.96], p = 0.0026, 0.0061, 0.011). FDS was clinically unremarkable for differential evaluation.

CONCLUSIONS

Wide-band EEG abnormality in migraineurs, i.e., FSDS, can be affected by migraine phase.

SIGNIFICANCE

Wide-band EEG finding could be a biomarker related to clinical variables in migraines.

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.

Duration of N1 sleep is a factor for excessive daytime sleepiness in epilepsy patients with interictal epileptiform discharges: A polysomnographic study

Purpose

This study aimed to identify the occurrence of excessive daytime sleepiness (EDS) in epilepsy patients with interictal epileptiform discharges and to explore the impact of interictal sleep architecture and sleep-related events on EDS.

Methods

This study included 101 epilepsy patients with interictal epileptiform discharges (IED) and 100 control patients who underwent simultaneous polysomnography and video ambulatory electroencephalography for >7 h throughout a single night. Multiple sleep latency tests were used to assess EDS. Comorbid EDS was present in 25 and 11 patients in the IED epilepsy and control groups, respectively. In addition, univariate and multivariate logistic regression analyses were performed to explore the factors influencing EDS.

Results

The epilepsy group had a higher prevalence of comorbid EDS and shorter R sleep duration. Univariate logistic regression analysis indicated that an increased risk of EDS may be associated with prolonged N1 sleep duration, higher arousal index, lower mean saturation (mSaO2), higher oxygen desaturation index (ODI), and duration of wake after sleep onset (WASO). Multivariate logistic regression analysis revealed that N1 sleep duration was significantly correlated with EDS.

Conclusion

In epilepsy patients with IED, the arousal index, mSaO2, ODI, and duration of WASO were weakly correlated with EDS, and the duration of N1 sleep demonstrated a significant positive correlation with EDS, which requires further research.

Generalizability of electroencephalographic interpretation using artificial intelligence: An external validation study

OBJECTIVE

The automated interpretation of clinical electroencephalograms (EEGs) using artificial intelligence (AI) holds the potential to bridge the treatment gap in resource-limited settings and reduce the workload at specialized centers. However, to facilitate broad clinical implementation, it is essential to establish generalizability across diverse patient populations and equipment. We assessed whether SCORE-AI demonstrates diagnostic accuracy comparable to that of experts when applied to a geographically different patient population, recorded with distinct EEG equipment and technical settings.

METHODS

We assessed the diagnostic accuracy of a "fixed-and-frozen" AI model, using an independent dataset and external gold standard, and benchmarked it against three experts blinded to all other data. The dataset comprised 50% normal and 50% abnormal routine EEGs, equally distributed among the four major classes of EEG abnormalities (focal epileptiform, generalized epileptiform, focal nonepileptiform, and diffuse nonepileptiform). To assess diagnostic accuracy, we computed sensitivity, specificity, and accuracy of the AI model and the experts against the external gold standard.

RESULTS

We analyzed EEGs from 104 patients (64 females, median age = 38.6 [range = 16-91] years). SCORE-AI performed equally well compared to the experts, with an overall accuracy of 92% (95% confidence interval [CI] = 90%-94%) versus 94% (95% CI = 92%-96%). There was no significant difference between SCORE-AI and the experts for any metric or category. SCORE-AI performed well independently of the vigilance state (false classification during awake: 5/41 [12.2%], false classification during sleep: 2/11 [18.2%]; p = .63) and normal variants (false classification in presence of normal variants: 4/14 [28.6%], false classification in absence of normal variants: 3/38 [7.9%]; p = .07).

SIGNIFICANCE

SCORE-AI achieved diagnostic performance equal to human experts in an EEG dataset independent of the development dataset, in a geographically distinct patient population, recorded with different equipment and technical settings than the development dataset.

vEpiNet: A multimodal interictal epileptiform discharge detection method based on video and electroencephalogram data

To enhance deep learning-based automated interictal epileptiform discharge (IED) detection, this study proposes a multimodal method, vEpiNet, that leverages video and electroencephalogram (EEG) data. Datasets comprise 24 931 IED (from 484 patients) and 166 094 non-IED 4-second video-EEG segments. The video data is processed by the proposed patient detection method, with frame difference and Simple Keypoints (SKPS) capturing patients' movements. EEG data is processed with EfficientNetV2. The video and EEG features are fused via a multilayer perceptron. We developed a comparative model, termed nEpiNet, to test the effectiveness of the video feature in vEpiNet. The 10-fold cross-validation was used for testing. The 10-fold cross-validation showed high areas under the receiver operating characteristic curve (AUROC) in both models, with a slightly superior AUROC (0.9902) in vEpiNet compared to nEpiNet (0.9878). Moreover, to test the model performance in real-world scenarios, we set a prospective test dataset, containing 215 h of raw video-EEG data from 50 patients. The result shows that the vEpiNet achieves an area under the precision-recall curve (AUPRC) of 0.8623, surpassing nEpiNet's 0.8316. Incorporating video data raises precision from 70% (95% CI, 69.8%-70.2%) to 76.6% (95% CI, 74.9%-78.2%) at 80% sensitivity and reduces false positives by nearly a third, with vEpiNet processing one-hour video-EEG data in 5.7 min on average. Our findings indicate that video data can significantly improve the performance and precision of IED detection, especially in prospective real clinic testing. It suggests that vEpiNet is a clinically viable and effective tool for IED analysis in real-world applications.

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.

Neuronal avalanches in temporal lobe epilepsy as a noninvasive diagnostic tool investigating large scale brain dynamics

The epilepsy diagnosis still represents a complex process, with misdiagnosis reaching 40%. We aimed at building an automatable workflow, helping the clinicians in the diagnosis of temporal lobe epilepsy (TLE). We hypothesized that neuronal avalanches (NA) represent a feature better encapsulating the rich brain dynamics compared to classically used functional connectivity measures (Imaginary Coherence; ImCoh). We analyzed large-scale activation bursts (NA) from source estimation of resting-state electroencephalography. Using a support vector machine, we reached a classification accuracy of TLE versus controls of 0.86 ± 0.08 (SD) and an area under the curve of 0.93 ± 0.07. The use of NA features increase by around 16% the accuracy of diagnosis prediction compared to ImCoh. Classification accuracy increased with larger signal duration, reaching a plateau at 5 min of recording. To summarize, NA represents an interpretable feature for an automated epilepsy identification, being related with intrinsic neuronal timescales of pathology-relevant regions.

A scalable platform for acquisition of high-fidelity human intracranial EEG with minimal clinical burden

Human neuroscience research has been significantly advanced by neuroelectrophysiological studies from people with refractory epilepsy-the only routine clinical intervention that acquires multi-day, multi-electrode human intracranial electroencephalography (iEEG). While a sampling rate below 2 kHz is sufficient for manual iEEG review by epileptologists, computational methods and research studies may benefit from higher resolution, which requires significant technical development. At adult and pediatric Stanford hospitals, research ports of commercial clinical acquisition systems were configured to collect 10 kHz iEEG of up to 256 electrodes simultaneously with the clinical data. The research digital stream was designed to be acquired post-digitization, resulting in no loss in clinical signal quality. This novel framework implements a near-invisible research platform to facilitate the secure, routine collection of high-resolution iEEG that minimizes research hardware footprint and clinical workflow interference. The addition of a pocket-sized router in the patient room enabled an encrypted tunnel to securely transmit research-quality iEEG across hospital networks to a research computer within the hospital server room, where data was coded, de-identified, and uploaded to cloud storage. Every eligible patient undergoing iEEG clinical evaluation at both hospitals since September 2017 has been recruited; participant recruitment is ongoing. Over 350+ terabytes (representing 1000+ days) of neuroelectrophysiology were recorded across 200+ participants of diverse demographics. To our knowledge, this is the first report of such a research integration within a hospital setting. It is a promising approach to promoting equitable participant enrollment and building comprehensive data repositories with consistent, high-fidelity specifications towards new discoveries in human neuroscience.

Attention-based deep convolutional neural network for classification of generalized and focal epileptic seizures

Epilepsy affects over 50 million people globally. Electroencephalography is critical for epilepsy diagnosis, but manual seizure classification is time-consuming and requires extensive expertise. This paper presents an automated multi-class seizure classification model using EEG signals from the Temple University Hospital Seizure Corpus ver. 1.5.2. 11 features including time-based correlation, time-based eigenvalues, power spectral density, frequency-based correlation, frequency-based eigenvalues, sample entropy, spectral entropy, logarithmic sum, standard deviation, absolute mean, and ratio of Daubechies D4 wavelet transformed coefficients were extracted from 10-second sliding windows across channels. The model combines multi-head self-attention mechanism with a deep convolutional neural network (CNN) to classify seven subtypes of generalized and focal epileptic seizures. The model achieved 0.921 weighted accuracy and 0.902 weighted F1 score in classifying focal onset non-motor, generalized onset non-motor, simple partial, complex partial, absence, tonic, and tonic-clonic seizures. In comparison, a CNN model without multi-head attention achieved 0.767 weighted accuracy. Ablation studies were conducted to validate the importance of transformer encoders and attention. The promising classification results demonstrate the potential of deep learning for handling EEG complexity and improving epilepsy diagnosis. This seizure classification model could enable timely interventions when translated into clinical practice.

Recent advances in clinical electroencephalography

PURPOSE OF REVIEW

Clinical electroencephalography (EEG) is a conservative medical field. This explains likely the significant gap between clinical practice and new research developments. This narrative review discusses possible causes of this discrepancy and how to circumvent them. More specifically, we summarize recent advances in three applications of clinical EEG: source imaging (ESI), high-frequency oscillations (HFOs) and EEG in critically ill patients.

RECENT FINDINGS

Recently published studies on ESI provide further evidence for the accuracy and clinical utility of this method in the multimodal presurgical evaluation of patients with drug-resistant focal epilepsy, and opened new possibilities for further improvement of the accuracy. HFOs have received much attention as a novel biomarker in epilepsy. However, recent studies questioned their clinical utility at the level of individual patients. We discuss the impediments, show up possible solutions and highlight the perspectives of future research in this field. EEG in the ICU has been one of the major driving forces in the development of clinical EEG. We review the achievements and the limitations in this field.

SUMMARY

This review will promote clinical implementation of recent advances in EEG, in the fields of ESI, HFOs and EEG in the intensive care.

Towards the automated detection of interictal epileptiform discharges with magnetoencephalography

BACKGROUND

The analysis of clinical magnetoencephalography (MEG) in patients with epilepsy traditionally relies on visual identification of interictal epileptiform discharges (IEDs), which is time consuming and dependent on subjective criteria.

NEW METHOD

Here, we explore the ability of Independent Components Analysis (ICA) and Hidden Markov Modeling (HMM) to automatically detect and localize IEDs. We tested our pipelines on resting-state MEG recordings from 10 school-aged children with (multi)focal epilepsy.

RESULTS

In focal epilepsy patients, both pipelines successfully detected visually identified IEDs, but also revealed unidentified low-amplitude IEDs. Success was more mitigated in patients with multifocal epilepsy, as our automated pipeline missed IED activity associated with some foci-an issue that could be alleviated by post-hoc manual selection of epileptiform ICs or HMM states.

COMPARISON WITH EXISTING METHODS

We compared our results with visual IED detection by an experienced clinical magnetoencephalographer, getting heightened sensitivity and requiring minimal input from clinical practitioners.

CONCLUSIONS

IED detection based on ICA or HMM represents an efficient way to identify IED localization and timing. The development of these automatic IED detection algorithms provide a step forward in clinical MEG practice by decreasing the duration of MEG analysis and enhancing its sensitivity.

EEG brain network variability is correlated with other pathophysiological indicators of critical patients in neurology intensive care unit

Continuous electroencephalogram (cEEG) plays a crucial role in monitoring and postoperative evaluation of critical patients with extensive EEG abnormalities. Recently, the temporal variability of dynamic resting-state functional connectivity has emerged as a novel approach to understanding the pathophysiological mechanisms underlying diseases. However, little is known about the underlying temporal variability of functional connections in critical patients admitted to neurology intensive care unit (NICU). Furthermore, considering the emerging field of network physiology that emphasizes the integrated nature of human organisms, we hypothesize that this temporal variability in brain activity may be potentially linked to other physiological functions. Therefore, this study aimed to investigate network variability using fuzzy entropy in 24-hour dynamic resting-state networks of critical patients in NICU, with an emphasis on exploring spatial topology changes over time. Our findings revealed both atypical flexible and robust architectures in critical patients. Specifically, the former exhibited denser functional connectivity across the left frontal and left parietal lobes, while the latter showed predominantly short-range connections within anterior regions. These patterns of network variability deviating from normality may underlie the altered network integrity leading to loss of consciousness and cognitive impairment observed in these patients. Additionally, we explored changes in 24-hour network properties and found simultaneous decreases in brain efficiency, heart rate, and blood pressure between approximately 1 pm and 5 pm. Moreover, we observed a close relationship between temporal variability of resting-state network properties and other physiological indicators including heart rate as well as liver and kidney function. These findings suggest that the application of a temporal variability-based cEEG analysis method offers valuable insights into underlying pathophysiological mechanisms of critical patients in NICU, and may present novel avenues for their condition monitoring, intervention, and treatment.

Early prediction of drug-resistant epilepsy using clinical and EEG features based on convolutional neural network

OBJECTIVE

Machine learning utilization in electroencephalogram (EEG) analysis and epilepsy care is fast evolving. Thus, we aim to develop and validate two one-dimensional convolutional neural network (CNN) algorithms for predicting drug-resistant epilepsy (DRE) in patients with newly-diagnosed epilepsy based on EEG and clinical features.

METHODS

We included a total of 1010 EEG signal epochs and 15 clinical features from 101 patients with epilepsy. Each patient had 10 epochs of EEG signal data, with each signal recorded for 90 s. The ratio of development set and validation set was 80:20, and ten-fold cross validation was performed. First, a CNN algorithm was used to extract EEG features automatically. Then, Two one-dimensional CNNs were crafted.. Accuracy, specificity, precision, sensitivity, F1-score, kappa statistics, mean square error (MSE) and area under the curve (AUC) were calculated to evaluate the classifiers performance.

RESULTS

The clinical-EEG model showed good performance and clinical practical value, with the accuracy, specificity, precision, sensitivity, F1-score, kappa statistics, best MSE and AUC in test set were 0.99, 0.72, 0.82, 0.96, 0.89, 0.83, 32.00, 0.81, respectively, and the accuracy in validation set was 0.84. In the EEG model, the accuracy, specificity, precision, sensitivity, F1-score, kappa statistics, best MSE and AUC in test set were 0.99, 0.59, 0.82, 0.90, 0.86, 0.72, 181.76, 0.76, respectively, and the accuracy in validation set was 0.81.

CONCLUSION

We constructed a clinical-EEG model showed good potential for predicting DRE in patients with newly-diagnosed epilepsy, which could help identify patients at high risk of developing DRE at earlier stages.

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.

Incidence and risk factors associated with seizures in cerebral amyloid angiopathy

BACKGROUND AND PURPOSE

Cerebral amyloid angiopathy (CAA) is a common cause of intracranial hemorrhage (ICH), which is a risk factor for seizures. The incidence and risk factors of seizures associated with a heterogeneous cohort of CAA patients have not been studied.

METHODS

We conducted a retrospective study of patients with CAA treated at Mayo Clinic Florida between 1 January 2015 and 1 January 2021. CAA was defined using the modified Boston criteria version 2.0. We analyzed electrophysiological and clinical features, and comorbidities including lobar ICH, nontraumatic cortical/convexity subarachnoid hemorrhage (cSAH), superficial siderosis, and inflammation (CAA with inflammation [CAA-ri]). Cognition and mortality were secondary outcomes. Univariate and multivariate analyses were performed to determine risk of seizures relative to clinical presentation.

RESULTS

Two hundred eighty-four patients with CAA were identified, with median follow-up of 35.7 months (interquartile range = 13.5-61.3 months). Fifty-six patients (19.7%) had seizures; in 21 (37.5%) patients, seizures were the index feature leading to CAA diagnosis. Seizures were more frequent in females (p = 0.032) and patients with lobar ICH (p = 0.002), cSAH (p = 0.030), superficial siderosis (p < 0.001), and CAA-ri (p = 0.005), and less common in patients with microhemorrhage (p = 0.006). After controlling for age and sex, lobar ICH (odds ratio [OR] = 2.1, 95% confidence interval [CI] = 1.1-4.2), CAA-ri (OR = 3.8, 95% CI = 1.4-10.3), and superficial siderosis (OR = 3.7, 95% CI = 1.9-7.0) were independently associated with higher odds of incident seizures.

CONCLUSIONS

Seizures are common in patients with CAA and are independently associated with lobar ICH, CAA-ri, and superficial siderosis. Our results may be applied to optimize clinical monitoring and management for patients with CAA.

From basic sciences and engineering to epileptology: A translational approach

Collaborative efforts between basic scientists, engineers, and clinicians are enabling translational epileptology. In this article, we summarize the recent advances presented at the International Conference for Technology and Analysis of Seizures (ICTALS 2022): (1) novel developments of structural magnetic resonance imaging; (2) latest electroencephalography signal-processing applications; (3) big data for the development of clinical tools; (4) the emerging field of hyperdimensional computing; (5) the new generation of artificial intelligence (AI)-enabled neuroprostheses; and (6) the use of collaborative platforms to facilitate epilepsy research translation. We highlight the promise of AI reported in recent investigations and the need for multicenter data-sharing initiatives.

Impact of ambulatory EEG in the management of patients with epilepsy in resource-limited Latin American populations

Objective

Ambulatory electroencephalography (AEEG) monitoring allows for prolonged recordings in normal environments, such as patients' homes, and is recognized as a cost-effective alternative to inpatient long-term video-EEG primarily in resource-limited countries. We aim to describe the impact of AEEG on the assessment of patients with suspected or confirmed epilepsy in two independent Latin-American populations with limited resources.

Methods

We included 63 patients who had undergone an AEEG due to confirmed/suspected epilepsy. Clinical (demographic, current antiseizure medication and indication) and electroencephalographic (duration of the study, result, and impact on clinical decision-making) were reviewed and compared.

Results

The main indication for an AEEG was the differentiation of seizures from non-epileptic events with 57% of patients. It was categorized as positive in 36 patients and did have an impact on the clinical decision-making process in 57% of patients. AEEG captured clinical events in 35 patients (20 epileptic and 15 non-epileptic).

Conclusions

AEEG proves to be a valuable tool in resource-limited settings for assessing suspected or confirmed epilepsy cases, with a significant impact on clinical decisions.

Significance

Our study provides valuable insights into the use of AEEG in under-resourced regions, shedding light on the challenges and potential benefits of this tool in clinical practice.

Nonlinear and chaos features over EMD/VMD decomposition methods for ictal EEG signals detection

The detection and identification of epileptic seizures attracted considerable relevance for the neurophysiologists. In order to accomplish the detection of epileptic seizures or equivalently ictal EEG states, this paper proposes the use of nonlinear and chaos features not computed over the raw EEG signals as it was commonly experienced, but instead over intrinsic mode functions (IMFs) extracted subsequently to the application of newly time-frequency signal decomposition methods on the basis of empirical mode decomposition (EMD) and variational mode decomposition (VMD) methods. The first step within the proposed methodology is to excerpt the various components of the IMFs by EMD and VMD decomposition methods on time EEG segments. The Hjorth parameters, the Hurst exponent, the Recurrence Quantification Analysis (RQA), the detrended fluctuation analysis (DFA), the Largest Lyapunov Exponent (LLE), The Higuchi and Katz fractal dimensions (HFD and KFD), seven nonlinear and chaos features computed over the IMFs were investigated and their classification performances evaluated using the k-nearest neighbor (KNN) and the multilayer perceptron neural network (MLPNN) classifiers. Furthermore, the combination of the best nonlinear features has also been examined in terms of sensitivity, specificity and overall classification accuracy. The publicly available Bonn EEG dataset has been has been employed to validate the efficiency of the proposed method for detecting ictal EEG signals from normal or interictal EEG segments. Among the several experiments involved in the current study, the ultimate results establish that the overall classification accuracy can achieve 100%, 99.45%, 99.8%, 99.8%, 98.6% and 99.1% for six different epileptic seizure detection case problems studied, confirming the ability of the proposed methodology in helping the clinic practitioners in the epilepsy detection care units to classify seizure events with a great confidence.

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.

Acceptability by End-users of a Standardized Structured Format for Reporting EEG

The report of the electroencephalogram (EEG) results has traditionally been made using free-text formats with a huge variation in descriptions due to several factors. Recently, the International Federation of Clinical Neurophysiology (IFCN) endorsed the use of the Standardized Computer-based Organized Reporting of EEG (SCORE). This system has many advantages, but only some concerns have been investigated so far. This study aimed to assess the end-users acceptability of this proposed EEG report format. A 16-item electronic survey was sent to physicians who use EEG services of a medical diagnosis clinic. Physicians had been receiving the EEG reports in free-text formats from the same three board-certified electroencephalographers for the past three years. In January 2019, the report changed to the SCORE format. The survey assessed five main topics: physician information and historical use of EEG; personal preferences; comparative aspects of the formats; impact of the new format on clinical decision-making; and satisfaction. Thirty-two of 52 have responded to the survey (61%). On average, 81% of the responders have received enough reports with the new format to reliably complete the survey. Every responder prefers the standardized compared to the free-text format. Twenty-five responders like the inclusion of the head model, and interestingly, five suggest including another legend to differentiate "slow activity" from "other abnormal activity". Virtually all responders would recommend the new format, but one-third read only the conclusion. Our findings suggest high acceptability of this standardized report format. Despite the limitations of this study, we hope these findings contribute to the improvement and expansion of standardized EEG reporting systems.

Integration of interictal EEG source localization in presurgical epilepsy evaluation - A single-center prospective study

OBJECTIVE

To investigate cost in working hours for initial integration of interictal EEG source localization (ESL) into clinical practice of a tertiary epilepsy center, and to examine concordance of results obtained with three different ESL pipelines.

METHODS

This prospective study covered the first year of using ESL in the Epilepsy-Center Berlin-Brandenburg. Patients aged ≥14 years with drug-resistant focal epilepsy referred for noninvasive presurgical evaluation were included. Interictal ESL was based on low-density EEG and individual head models. Source maxima were obtained from two freely available software packages and one commercial provider. One physician and computer scientist documented their working hours for setting up and processing ESL. Additionally, a survey was conducted among epilepsy centers in Germany to assess the current role of ESL in presurgical evaluation.

RESULTS

Of 40 patients included, 22 (55%) had enough interictal spikes for ESL. The physician's working times decreased from median 4.7 hours [interquartile range 3.9-6.4] in the first third of cases to 2.0 hours [1.9-2.4] in the remaining two thirds; P < 0.01. In addition, computer scientist and physician spent a total of 35.5 and 33.0 working hours on setting up the digital infrastructure, and on training and testing. Sublobar agreement between all three pipelines was 20%, mean measurement of agreement (kappa) 0.13. Finally, the survey revealed that 53% of epilepsy centers in Germany currently use ESL for presurgical evaluation.

SIGNIFICANCE

This study provides information regarding expected effort and costs for integration of ESL into an epilepsy surgery program. Low result agreement across different ESL pipelines calls for further standardization.

Additional overnight video EEG for the diagnosis of epilepsy: Experiences from Western Kenya

Objective

The prolonged video EEG monitoring is widely used for the diagnosis and management of epilepsy, especially during the presurgical evaluation. The routine practice in neurology is to order a prolonged recording like an overnight EEG when the initial routine EEG is normal or unrevealing. Only few studies have evaluated this sequential approach and we aimed in this study to evaluate the added diagnostic value of a relatively brief video EEG monitoring especially in developing nations where the history of seizure semiology may be harder to obtain, and the video EEG monitoring technology is scarce.

Methods

This study analyzed retrospectively 167 overnight video EEG records in one of the secondary healthcare facilities in Western Kenya between March 2018 and March 2021. The indications were mainly further diagnosis and seizure classification. All the patients had an unrevealing routine EEG and 162 of them were normal.

Results

Additional epileptiform discharges were recorded in 91 of those 162 with initial normal routine EEG. Further classification of seizure was achieved in 67 patients among 112 with initially unclassified seizure before the overnight recording. The improvement of 68% (97 out of 143 patients without a prior epilepsy diagnosis) for the diagnosis of epilepsy in those patients without initial final diagnosis is comparable to other similar studies but mostly with a longer duration of recording. The diagnosis was changed or at least improved in 142 (85%) patients out of the 167 patients who underwent the overnight video EEG. The treatment modification was immediately considered in 116 after the prolonged recording.

Conclusions

Adding an overnight video EEG to an unrevealing routine EEG can significantly increase the likelihood of detecting additional epileptiform discharges in patients with epilepsy, thereby improving diagnostic yield and aiding in treatment adjustment for all patients suspected of having epilepsy.

Significance

The sequential approach of adding a prolonged video EEG monitoring even as brief as an overnight video EEG to an unrevealing routine EEG has a very significant impact in further classification of seizure and diagnosis of epilepsy especially in a resource limited set up.

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.

At home EEG monitoring technologies for people with epilepsy and intellectual disabilities: A scoping review

BACKGROUND

Conducting electroencephalography in people with intellectual disabilities (PwID) can be challenging, but the high proportion of PwID who experience seizures make it an essential part of their care. To reduce hospital-based monitoring, interventions are being developed to enable high-quality EEG data to be collected at home. This scoping review aims to summarise the current state of remote EEG monitoring research, potential benefits and limitations of the interventions, and inclusion of PwID in this research.

METHODS

The review was structured using the PRISMA extension for Scoping Reviews and the PICOS framework. Studies that evaluated a remote EEG monitoring intervention in adults with epilepsy were retrieved from the PubMed, MEDLINE, Embase, CINAHL, Web of Science, and ClinicalTrials.gov databases. A descriptive analysis provided an overview of the study and intervention characteristics, key results, strengths, and limitations.

RESULTS

34,127 studies were retrieved and 23 were included. Five types of remote EEG monitoring were identified. Common benefits included producing useful results of comparable quality to inpatient monitoring and patient experience. A common limitation was the challenge of capturing all seizures with a small number of localised electrodes. No randomised controlled trials were included, few studies reported sensitivity and specificity, and only three considered PwID.

CONCLUSIONS

Overall, the studies demonstrated the feasibility of remote EEG interventions for out-of-hospital monitoring and their potential to improve data collection and quality of care for patients. Further research is needed on the effectiveness, benefits, and limitations of remote EEG monitoring compared to in-patient monitoring, especially for PwID.

Automated Interpretation of Clinical Electroencephalograms Using Artificial Intelligence

Importance

Electroencephalograms (EEGs) are a fundamental evaluation in neurology but require special expertise unavailable in many regions of the world. Artificial intelligence (AI) has a potential for addressing these unmet needs. Previous AI models address only limited aspects of EEG interpretation such as distinguishing abnormal from normal or identifying epileptiform activity. A comprehensive, fully automated interpretation of routine EEG based on AI suitable for clinical practice is needed.

Objective

To develop and validate an AI model (Standardized Computer-based Organized Reporting of EEG-Artificial Intelligence [SCORE-AI]) with the ability to distinguish abnormal from normal EEG recordings and to classify abnormal EEG recordings into categories relevant for clinical decision-making: epileptiform-focal, epileptiform-generalized, nonepileptiform-focal, and nonepileptiform-diffuse.

Design, Setting, and Participants

In this multicenter diagnostic accuracy study, a convolutional neural network model, SCORE-AI, was developed and validated using EEGs recorded between 2014 and 2020. Data were analyzed from January 17, 2022, until November 14, 2022. A total of 30 493 recordings of patients referred for EEG were included into the development data set annotated by 17 experts. Patients aged more than 3 months and not critically ill were eligible. The SCORE-AI was validated using 3 independent test data sets: a multicenter data set of 100 representative EEGs evaluated by 11 experts, a single-center data set of 9785 EEGs evaluated by 14 experts, and for benchmarking with previously published AI models, a data set of 60 EEGs with external reference standard. No patients who met eligibility criteria were excluded.

Main Outcomes and Measures

Diagnostic accuracy, sensitivity, and specificity compared with the experts and the external reference standard of patients' habitual clinical episodes obtained during video-EEG recording.

Results

The characteristics of the EEG data sets include development data set (N = 30 493; 14 980 men; median age, 25.3 years [95% CI, 1.3-76.2 years]), multicenter test data set (N = 100; 61 men, median age, 25.8 years [95% CI, 4.1-85.5 years]), single-center test data set (N = 9785; 5168 men; median age, 35.4 years [95% CI, 0.6-87.4 years]), and test data set with external reference standard (N = 60; 27 men; median age, 36 years [95% CI, 3-75 years]). The SCORE-AI achieved high accuracy, with an area under the receiver operating characteristic curve between 0.89 and 0.96 for the different categories of EEG abnormalities, and performance similar to human experts. Benchmarking against 3 previously published AI models was limited to comparing detection of epileptiform abnormalities. The accuracy of SCORE-AI (88.3%; 95% CI, 79.2%-94.9%) was significantly higher than the 3 previously published models (P < .001) and similar to human experts.

Conclusions and Relevance

In this study, SCORE-AI achieved human expert level performance in fully automated interpretation of routine EEGs. Application of SCORE-AI may improve diagnosis and patient care in underserved areas and improve efficiency and consistency in specialized epilepsy centers.

Diagnostic Accuracy of Ambulatory EEG vs Routine EEG in Patients With First Single Unprovoked Seizure

Background and Objective

To evaluate the diagnostic accuracy of the ambulatory EEG (aEEG) at detecting interictal epileptiform discharges (IEDs)/seizures compared with routine EEG (rEEG) and repetitive/second rEEG in patients with a first single unprovoked seizure (FSUS). We also evaluated the association between IED/seizures on aEEG and seizure recurrence within 1 year of follow-up.

Methods

We prospectively evaluated 100 consecutive patients with FSUS at the provincial Single Seizure Clinic. They underwent 3 sequential EEG modalities: first rEEG, second rEEG, and aEEG. Clinical epilepsy diagnosis was ascertained based on the 2014 International League Against Epilepsy definition by a neurologist/epileptologist at the clinic. An EEG-certified epileptologist/neurologist interpreted all 3 EEGs. All patients were followed up for 52 weeks until they had either second unprovoked seizure or maintained single seizure status. Accuracy measures (sensitivity, specificity, negative and positive predictive values, and likelihood ratios), receiver operating characteristic (ROC) analysis, and area under the curve (AUC) were used to evaluate the diagnostic accuracy of each EEG modality. Life tables and the Cox proportional hazard model were used to estimate the probability and association of seizure recurrence.

Results

Ambulatory EEG captured IED/seizures with a sensitivity of 72%, compared with 11% for the first rEEG and 22% for the second rEEG. The diagnostic performance of the aEEG was statistically better (AUC: 0.85) compared with the first rEEG (AUC: 0.56) and second rEEG (AUC: 0.60). There were no statistically significant differences between the 3 EEG modalities regarding specificity and positive predictive value. Finally, IED/seizure on the aEEG was associated with more than 3 times the hazard of seizure recurrence.

Discussion

The overall diagnostic accuracy of aEEG at capturing IED/seizures in people presenting with FSUS was higher than the first and second rEEGs. We also found that IED/seizures on the aEEG were associated with an increased risk of seizure recurrence.

Classification of Evidence

This study provides Class I evidence supporting that, in adults with First Single Unprovoked Seizure (FSUS), 24-h ambulatory EEG has increased sensitivity when compared with routine and repeated EEG.

Epileptic seizures detection and the analysis of optimal seizure prediction horizon based on frequency and phase analysis

Changes in the frequency composition of the human electroencephalogram are associated with the transitions to epileptic seizures. Cross-frequency coupling (CFC) is a measure of neural oscillations in different frequency bands and brain areas, and specifically phase-amplitude coupling (PAC), a form of CFC, can be used to characterize these dynamic transitions. In this study, we propose a method for seizure detection and prediction based on frequency domain analysis and PAC combined with machine learning. We analyzed two databases, the Siena Scalp EEG database and the CHB-MIT database, and used the frequency features and modulation index (MI) for time-dependent quantification. The extracted features were fed to a random forest classifier for classification and prediction. The seizure prediction horizon (SPH) was also analyzed based on the highest-performing band to maximize the time for intervention and treatment while ensuring the accuracy of the prediction. Under comprehensive consideration, the results demonstrate that better performance could be achieved at an interval length of 5 min with an average accuracy of 85.71% and 95.87% for the Siena Scalp EEG database and the CHB-MIT database, respectively. As for the adult database, the combination of PAC analysis and classification can be of significant help for seizure detection and prediction. It suggests that the rarely used SPH also has a major impact on seizure detection and prediction and further explorations for the application of PAC are needed.

Electroencephalographic (EEG) Biomarkers in Genetic Neurodevelopmental Disorders

Collectively, neurodevelopmental disorders are highly prevalent, but more than a third of neurodevelopmental disorders have an identifiable genetic etiology, each of which is individually rare. The genes associated with neurodevelopmental disorders are often involved in early brain development, neuronal signaling, or synaptic plasticity. Novel treatments for many genetic neurodevelopmental disorders are being developed, but disease-relevant clinical outcome assessments and biomarkers are limited. Electroencephalography (EEG) is a promising noninvasive potential biomarker of brain function. It has been used extensively in epileptic disorders, but its application in neurodevelopmental disorders needs further investigation. In this review, we explore the use of EEG in 3 of the most prevalent genetic neurodevelopmental disorders-Angelman syndrome, Rett syndrome, and fragile X syndrome. Quantitative analyses of EEGs, such as power spectral analysis or measures of connectivity, can quantify EEG signatures seen on qualitative review and potentially correlate with phenotypes. In both Angelman syndrome and Rett syndrome, increased delta power on spectral analysis has correlated with clinical markers of disease severity including developmental disability and seizure burden, whereas spectral power analysis on EEG in fragile X syndrome tends to demonstrate abnormalities in gamma power. Further studies are needed to establish reliable relationships between quantitative EEG biomarkers and clinical phenotypes in rare genetic neurodevelopmental disorders.

Rates of event capture of home video EEG

OBJECTIVE

Recording electrographic and behavioral information during epileptic and other paroxysmal events is important during video electroencephalography (EEG) monitoring. This study was undertaken to measure the event capture rate of an home service operating across Australia using a shoulder-worn EEG device and telescopic pole-mounted camera.

METHODS

Neurologist reports were accessed retrospectively. Studies with confirmed events were identified and assessed for event capture by recording modality, whether events were reported or discovered, and physiological state.

RESULTS

6,265 studies were identified, of which 2,788 (44.50%) had events. A total of 15,691 events were captured, of which 77.89% were reported. The EEG amplifier was active for 99.83% of events. The patient was in view of the camera for 94.90% of events. 84.89% of studies had all events on camera, and 2.65% had zero events on camera (mean = 93.66%, median = 100.00%). 84.42% of events from wakefulness were reported, compared to 54.27% from sleep.

CONCLUSIONS

Event capture was similar to previously reported rates from home studies, with higher capture rates on video. Most patients have all events captured on camera.

SIGNIFICANCE

Home monitoring is capable of high rates of event capture, and the use of wide-angle cameras allows for all events to be captured in the majority of studies.

Long-Term Pharmacological and Psychosocial Outcomes of Adolescent-Onset Epilepsy: A Single-Center Experience

Purpose: Despite the high incidence of epilepsy during adolescence, little attention has been paid to the outcomes and prognosis of adolescent-onset epilepsy. The aim of this study was to investigate the long-term pharmacological and psychosocial outcomes of adolescent-onset epilepsy.Methods: From 1993 to 2019, the electronic medical records of adolescent-onset epilepsy patients were retrieved from Asan Medical Center Children’s Hospital. Seizure outcomes were evaluated based on the seizure-free period at last contact. Possible predictors of remission, relapse, and intractability were investigated. Neuropsychiatric comorbidities, socioeconomic status, and transition outcomes were also assessed. Results: In total, 137 patients were enrolled in this study. The median age at diagnosis of epilepsy was 14 years and the mean duration of therapy was 13.0 years. During follow-up, 93 patients (67.9%) achieved terminal remission, of which 27 cases (19.7%) resolved. Relapse after withdrawal of medication occurred in 74 patients (54.0%), and the presence of electroencephalographic abnormalities (odds ratio [OR], 8.23; 95% confidence interval [CI], 1.39 to 48.87; P=0.020), poor adherence (OR, 4.84; 95% CI, 2.13 to 11.02; P=0.000), and history of febrile seizures (OR, 4.10; 95% CI, 1.21 to 13.93; P=0.024) were risk factors for relapse. Neurodevelopmental and psychological comorbidities were documented in 17 (12.4%) and 12 (8.8%) patients, respectively. Thirty-six (26.3%) patients transferred to adult clinics, at a mean age of 21.9 years. Conclusion: This study showed overall favorable seizure outcomes with a high rate of remission, but with frequent relapse after withdrawal.

Neurotechnology and international security: Predicting commercial and military adoption of brain-computer interfaces (BCIs) in the United States and China

In the past decade, international actors have launched "brain projects" or "brain initiatives." One of the emerging technologies enabled by these publicly funded programs is brain-computer interfaces (BCIs), which are devices that allow communication between the brain and external devices like a prosthetic arm or a keyboard. BCIs are poised to have significant impacts on public health, society, and national security. This research presents the first analytical framework that attempts to predict the dissemination of neurotechnologies to both the commercial and military sectors in the United States and China. While China started its project later with less funding, we find that it has other advantages that make earlier adoption more likely. We also articulate national security risks implicit in later adoption, including the inability to set international ethical and legal norms for BCI use, especially in wartime operating environments, and data privacy risks for citizens who use technology developed by foreign actors.

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.

Routine and sleep EEG: Minimum recording standards of the International Federation of Clinical Neurophysiology and the International League Against Epilepsy

This article provides recommendations on the minimum standards for recording routine ("standard") and sleep electroencephalography (EEG). The joint working group of the International Federation of Clinical Neurophysiology (IFCN) and the International League Against Epilepsy (ILAE) developed the standards according to the methodology suggested for epilepsy-related clinical practice guidelines by the Epilepsy Guidelines Working Group. We reviewed the published evidence using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement. The quality of evidence for sleep induction methods was assessed by the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) method. A tool for Quality Assessment of Diagnostic Studies (QUADAS-2) was used to assess the risk of bias in technical and methodological studies. Where high-quality published evidence was lacking, we used modified Delphi technique to reach expert consensus. The GRADE system was used to formulate the recommendations. The quality of evidence was low or moderate. We formulated 16 consensus-based recommendations for minimum standards for recording routine and sleep EEG. The recommendations comprise the following aspects: indications, technical standards, recording duration, sleep induction, and provocative methods.

Routine and sleep EEG: Minimum recording standards of the International Federation of Clinical Neurophysiology and the International League Against Epilepsy

This article provides recommendations on the minimum standards for recording routine ("standard") and sleep electroencephalography (EEG). The joint working group of the International Federation of Clinical Neurophysiology (IFCN) and the International League Against Epilepsy (ILAE) developed the standards according to the methodology suggested for epilepsy-related clinical practice guidelines by the Epilepsy Guidelines Working Group. We reviewed the published evidence using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement. The quality of evidence for sleep induction methods was assessed by the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) method. A tool for Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) was used to assess the risk of bias in technical and methodological studies. Where high-quality published evidence was lacking, we used modified Delphi technique to reach expert consensus. The GRADE system was used to formulate the recommendations. The quality of evidence was low or moderate. We formulated 16 consensus-based recommendations for minimum standards for recording routine and sleep EEG. The recommendations comprise the following aspects: indications, technical standards, recording duration, sleep induction, and provocative methods.

Prognostic interictal electroencephalographic biomarkers and models to assess antiseizure medication efficacy for clinical practice: A scoping review

Antiseizure medication (ASM) is the primary treatment for epilepsy. In clinical practice, methods to assess ASM efficacy (predict seizure freedom or seizure reduction), during any phase of the drug treatment lifecycle, are limited. This scoping review identifies and appraises prognostic electroencephalographic (EEG) biomarkers and prognostic models that use EEG features, which are associated with seizure outcomes following ASM initiation, dose adjustment, or withdrawal. We also aim to summarize the population and context in which these biomarkers and models were identified and described, to understand how they could be used in clinical practice. Between January 2021 and October 2022, four databases, references, and citations were systematically searched for ASM studies investigating changes to interictal EEG or prognostic models using EEG features and seizure outcomes. Study bias was appraised using modified Quality in Prognosis Studies criteria. Results were synthesized into a qualitative review. Of 875 studies identified, 93 were included. Biomarkers identified were classed as qualitative (visually identified by wave morphology) or quantitative. Qualitative biomarkers include identifying hypsarrhythmia, centrotemporal spikes, interictal epileptiform discharges (IED), classifying the EEG as normal/abnormal/epileptiform, and photoparoxysmal response. Quantitative biomarkers were statistics applied to IED, high-frequency activity, frequency band power, current source density estimates, pairwise statistical interdependence between EEG channels, and measures of complexity. Prognostic models using EEG features were Cox proportional hazards models and machine learning models. There is promise that some quantitative EEG biomarkers could be used to assess ASM efficacy, but further research is required. There is insufficient evidence to conclude any specific biomarker can be used for a particular population or context to prognosticate ASM efficacy. We identified a potential battery of prognostic EEG biomarkers, which could be combined with prognostic models to assess ASM efficacy. However, many confounders need to be addressed for translation into clinical practice.

Clinical Features, Neuropathology, and Surgical Outcome in Patients With Refractory Epilepsy and Brain Somatic Variants in the SLC35A2 Gene

BACKGROUND AND OBJECTIVES

The SLC35A2 gene, located at chromosome Xp11.23, encodes for a uridine diphosphate-galactose transporter. We describe clinical, genetic, neuroimaging, EEG, and histopathologic findings and assess possible predictors of postoperative seizure and cognitive outcome in 47 patients with refractory epilepsy and brain somatic SLC35A2 gene variants.

METHODS

This is a retrospective multicenter study where we performed a descriptive analysis and classical hypothesis testing. We included the variables of interest significantly associated with the outcomes in the generalized linear models.

RESULTS

Two main phenotypes were associated with brain somatic SLC35A2 variants: (1) early epileptic encephalopathy (EE, 39 patients) with epileptic spasms as the predominant seizure type and moderate to severe intellectual disability and (2) drug-resistant focal epilepsy (DR-FE, 8 patients) associated with normal/borderline cognitive function and specific neuropsychological deficits. Brain MRI was abnormal in all patients with EE and in 50% of those with DR-FE. Histopathology review identified mild malformation of cortical development with oligodendroglial hyperplasia in epilepsy in 44/47 patients and was inconclusive in 3. The 47 patients harbored 42 distinct mosaic SLC35A2 variants, including 14 (33.3%) missense, 13 (30.9%) frameshift, 10 (23.8%) nonsense, 4 (9.5%) in-frame deletions/duplications, and 1 (2.4%) splicing variant. Variant allele frequencies (VAFs) ranged from 1.4% to 52.6% (mean VAF: 17.3 ± 13.5). At last follow-up (35.5 ± 21.5 months), 30 patients (63.8%) were in Engel Class I, of which 26 (55.3%) were in Class IA. Cognitive performances remained unchanged in most patients after surgery. Regression analyses showed that the probability of achieving both Engel Class IA and Class I outcomes, adjusted by age at seizure onset, was lower when the duration of epilepsy increased and higher when postoperative EEG was normal or improved. Lower brain VAF was associated with improved postoperative cognitive outcome in the analysis of associations, but this finding was not confirmed in regression analyses.

DISCUSSION

Brain somatic SLC35A2 gene variants are associated with 2 main clinical phenotypes, EE and DR-FE, and a histopathologic diagnosis of MOGHE. Additional studies will be needed to delineate any possible correlation between specific genetic variants, mutational load in the epileptogenic tissue, and surgical outcomes.

Predictors of surgical outcomes in patients with drug-resistant temporal lobe epilepsy

Aim. To identify predictors of surgical outcomes in patients with drug-resistant temporal lobe epilepsy in a multivariate model.

Materials and methods. Aretrospective study included 69 patients with drug-resistant temporal lobe epilepsy who underwent microsurgical anterior temporal lobectomy. The study included 31 (45%) men and 38 (55%) women. The median age was 28 (21; 36). Surgical treatment outcomes were assessed at 6, 12, 36, and 60 months after surgical intervention according to the Engel Epilepsy Surgery Outcome Scale. Logistic regression equations were calculated, a ROC curve was constructed, and odds ratio (OR) with 95% confidence interval (CI), sensitivity, specificity, area under the ROC curve (AUC) were calculated.

Results. In all assessed time periods, 88.3–93.0% of patients had outcomes consistent with Engel classes I and II. The distribution of patients by outcome classes did not change statistically significantly over the entire follow-up period. There were the following predictors of high efficacy of surgical treatment at 6 months after surgery: relatively shorter duration of active disease course (OR 0.719, 95%, CI: 0.437–0.966, p < 0.05), absence of status epilepticus (OR 0.048, 95% CI: 0.002–0.472, p < 0.05), absence of subdominant foci of irritative activity (OR 0.123, 95% CI: 0.012–0.845, p < 0.01), presence of mesial temporal sclerosis (OR 1008, 95% CI: 21.59–1310851, p < 0.01), a relatively longer resection margin on the temporal lobe (OR 637.32, 95% CI: 5.43–1960062, p < 0.05), lateralization of epileptogenic zone in subdominant hemisphere (OR 0.103, 95% CI 0.004–0.937, p = 0.0532). AUC was 0.957 (0.917–0.997), p < 0.0001; sensitivity 87.5%, and specificity 82.8%.

Conclusion. Independent predictors of the efficacy of microsurgical anterior temporal lobectomy in patients with drug-resistant temporal lobe epilepsy are the following: shorter duration of active disease course, absence of status epilepticus in the history, absence of subdominant foci, presence of mesial temporal sclerosis, a relatively longer resection margin on the temporal lobe, and lateralization of the epileptogenic zone in the temporal lobe of the subdominant hemisphere.

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.

Epilepsy center characteristics and geographic region influence presurgical testing in the United States

OBJECTIVE

Persons with drug-resistant epilepsy may benefit from epilepsy surgery and should undergo presurgical testing to determine potential candidacy and appropriate intervention. Institutional expertise can influence use and availability of evaluations and epilepsy surgery candidacy. This census survey study aims to examine the influence of geographic region and other center characteristics on presurgical testing for medically intractable epilepsy.

METHODS

We analyzed annual report and supplemental survey data reported in 2020 from 206 adult epilepsy center directors and 136 pediatric epilepsy center directors in the United States. Test utilization data were compiled with annual center volumes, available resources, and US Census regional data. We used Wilcoxon rank-sum, Kruskal-Wallis, and chi-squared tests for univariate analysis of procedure utilization. Multivariable modeling was also performed to assign odds ratios (ORs) of significant variables.

RESULTS

The response rate was 100% with individual element missingness < 11% across 342 observations undergoing univariate analysis. A total of 278 complete observations were included in the multivariable models, and significant regional differences were present. For instance, compared to centers in the South, those in the Midwest used neuropsychological testing (OR = 2.87, 95% confidence interval [CI] = 1.2-6.86; p = .018) and fluorodeoxyglucose-positron emission tomography (OR = 2.74, 95% CI = = 1.14-6.61; p = .025) more commonly. For centers in the Northeast (OR = .46, 95% CI = .23-.93; p = .031) and West (OR = .41, 95% CI = .19-.87; p = .022), odds of performing single-photon emission computerized tomography were lower by nearly 50% compared to those in the South. Center accreditation level, demographics, volume, and resources were also associated with varying individual testing rates.

SIGNIFICANCE

Presurgical testing for drug-resistant epilepsy is influenced by US geographic region and other center characteristics. These findings have potential implications for comparing outcomes between US epilepsy centers and may inject disparities in access to surgical treatment.

Automatic interictal epileptiform discharge (IED) detection based on convolutional neural network (CNN)

Clinical diagnosis of epilepsy significantly relies on identifying interictal epileptiform discharge (IED) in electroencephalogram (EEG). IED is generally interpreted manually, and the related process is very time-consuming. Meanwhile, the process is expert-biased, which can easily lead to missed diagnosis and misdiagnosis. In recent years, with the development of deep learning, related algorithms have been used in automatic EEG analysis, but there are still few attempts in IED detection. This study uses the currently most popular convolutional neural network (CNN) framework for EEG analysis for automatic IED detection. The research topic is transferred into a 4-labels classification problem. The algorithm is validated on the long-term EEG of 11 pediatric patients with epilepsy. The computational results confirm that the CNN-based model can obtain high classification accuracy, up to 87%. The study may provide a reference for the future application of deep learning in automatic IED detection.

From Alpha to Zeta: A Systematic Review of Zeta Waves

PURPOSE

Electroencephalogram is used for prognostication and diagnosis in critically ill patients and is vital in developing clinical management algorithms. Unique waveforms on EEG may distinguish neurological disorders and define a potential for seizures. To better characterize zeta waves, we sought to define their electroclinical spectrum.

METHODS

We performed a systematic review using MEDLINE, Embase, Cochrane Central Register of Controlled Trials and Cochrane Database of Systematic Review [through Ovid], Scopus, Science Citation Index Expanded and Emerging Sources Citation Index [through the Web of Science], and Epistemonikos. Grey literature resources were searched.

RESULTS

Five hundred thirty-seven articles were identified. After excluding duplicates and reviewing titles, abstracts, and bodies and bibliographies of articles, four studies reported 64 cases describing data from patients with zeta waves, with a prevalence of 3 to 4%. Various and often incomplete clinical, neuroimaging, and EEG data were available. 57 patients (89.1%) had a focal cerebral lesion concordant with the location of zeta waves on EEG. 26 patients (40.6%) had clinical seizures, all but one being focal onset. Thirteen patients (20%) had epileptiform activity on EEG. Typically, zeta waves were located in the frontal head regions, often with generalized, frontal, predominant, rhythmic delta activity and associated with focal EEG suppression.

CONCLUSIONS

Zeta waves frequently represent an underlying focal structural lesion. Their presence suggests a heightened risk for seizures. The small number of retrospective cases series in the literature reporting zeta waves might be an underrepresentation. We suggest a need for prospective studies of cEEG in critically ill patients to determine their clinical significance.

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.

Approximate zero-crossing: a new interpretable, highly discriminative and low-complexity feature for EEG and iEEG seizure detection

Objective. Long-term monitoring of people with epilepsy based on electroencephalography (EEG) and intracranial EEG (iEEG) has the potential to deliver key clinical information for personalised epilepsy treatment. More specifically, in outpatient settings, the available solutions are not satisfactory either due to poor classification performance or high complexity to be executed in resource-constrained devices (e.g. wearable systems). Therefore, we hypothesize that obtaining high discriminative features is the main avenue to improve low-complexity seizure-detection algorithms.Approach. Inspired by how neurologists recognize ictal EEG data, and to tackle this problem by targeting resource-constrained wearable devices, we introduce a new interpretable and highly discriminative feature for EEG and iEEG, namely approximate zero-crossing (AZC). We obtain AZC by applying a polygonal approximation to mimic how our brain selects prominent patterns among noisy data and then using a zero-crossing count as a measure of the dominating frequency. By employing Kullback-Leiber divergence, leveraging CHB-MIT and SWEC-ETHZ iEEG datasets, we compare the AZC discriminative power against a set of 56 classical literature features (CLF). Moreover, we assess the performances of a low-complexity seizure detection method using only AZC features versus employing the CLF set.Main results. Three AZC features obtained with different approximation thresholds are among the five with the highest median discriminative power. Moreover, seizure classification based on only AZC features outperforms an equivalent CLF-based method. The former detects 102 and 194 seizures, against 99 and 161 for the latter (CHB-MIT and SWEC-ETHZ, respectively). Moreover, the AZC-based method keeps a similar false-alarm rate (i.e. an average of 2.1 and 1.0, against 2.0 and 0.5, per day).Significance. We propose a new feature and demonstrate its capability in seizure classification for both scalp and intracranial EEG. We envision the use of such a feature to improve outpatient monitoring with resource-constrained devices.

The Potential of Bemegride as an Activation Agent in Electroencephalography in Dogs

The present study investigated the potential of bemegride as a pharmacological activation agent that elicits epileptiform discharges (EDs) in interictal electroencephalogram (EEG) recordings in dogs. Four laboratory dogs with idiopathic epilepsy and four without epilepsy were included. The dogs were anesthetized using sevoflurane during EEG recordings. Bemegride was administered intravenously and repeatedly until EDs were enhanced or induced, or until the maximum dose (20 mg/kg) had been administered. Bemegride activated EDs in all dogs with epilepsy. These EDs predominantly occurred in each dog's spontaneous irritative zones, which were identified without the administration of bemegride. EDs occurred after the administration of bemegride in 50% of dogs without epilepsy. The dose required for activation was significantly lower in dogs with epilepsy (median; 7.3 mg/kg) than in those without (median; 19.7 mg/kg) (p = 0.0294). The only suspected adverse effect associated with the administration of bemegride was vomiting in two dogs after awakening from anesthesia. There were no other adverse effects, including seizures. The present results demonstrated the potential of bemegride as a safe and effective pharmacological activation agent of EDs in anesthetized dogs with epilepsy and provided more options for the diagnosis and therapeutic planning of epilepsy, including presurgical evaluations, in dogs.

Stereotactic biopsy and laser ablation of the ganglioglioma using a thulium laser: a video case report

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Electroencephalogram and heart rate variability features as predictors of responsiveness to vagus nerve stimulation in patients with epilepsy: a systematic review

INTRODUCTION

Vagus nerve stimulation (VNS) is a mainstay treatment in people with medically refractive epilepsy with a growing interest to identify biomarkers that are predictive of VNS efficacy. In this review, we looked at electroencephalography (EEG) and heart rate variability (HRV) parameters as potential biomarkers.

METHODOLOGY

A comprehensive search of several databases limited to the English language and excluding animal studies was conducted. Data was collected from studies that specifically reviewed preoperative EEG and HRV characteristics as predictive factors of VNS outcomes.

RESULTS

Ten out of 1078 collected studies were included in this review, of which EEG characteristics were reported in seven studies; HRV parameters were reported in two studies, and one study reported both. For EEG, studies reported a lower global rate of synchronization in alpha, delta, and gamma waves as predictors of the VNS response. The P300 wave, an evoked response on EEG, had conflicting results. Two studies reported high P300 wave amplitudes in nonresponders and low amplitudes in responders, whereas another study reported high P300 wave amplitudes in responders. For HRV, one study reported high-frequency power as the only parameter to be significantly lower in responders. In contrast, two studies from the same authors showed that HRV parameters were not different between responders and nonresponders.

CONCLUSION

HRV parameters and EEG characteristics including focal seizures and P300 wave have been reported as potential biomarkers for VNS outcomes in people with medically refractive epilepsy. However, the contradictory findings imply a need for validation through clinical trials.

EEG correlates of quality of life and associations with seizure without awareness and depression in patients with epilepsy

Aims

Quality of life (QOL) is an important issue for not only patients with epilepsy but also physicians. Depression has a large impact on QOL. Nonlinear electroencephalogram (EEG) analysis using machine learning (ML) has the potential to improve the accuracy of the diagnosis of epilepsy. Therefore, in this study, we examined EEG nonlinearity, EEG correlates of QOL in patients with epilepsy, and the accuracy of EEG for the interval from seizure without awareness (SA–) and for depression, using ML.

Methods

The Side Effects and Life Satisfaction (SEALS) inventory was used to assess QOL, and the Neurological Disorders Depression Inventory for Epilepsy (NDDI‐E) was used as a screening tool for depression on the date of the EEG recording. EEG with wavelet denoising (WD), the Savitzky–Golay filter, and non‐denoising were created in combination with low‐ and high‐pass filters. These EEG sets were adopted for phase space reconstruction methods. Using a generalized linear mixed‐effects model for SEALS, sample entropy as a measurement of regularity, SA–, seizure with awareness, and depression were examined.

Results

WD and non‐denoising EEG sets in the bilateral posterior temporal‐occipital, centro‐parietal, parieto‐occipital, and Fz–Cz of the 10–20 method were associated with SEALS and demonstrated nonlinearity, and the moderate effects of classification for the interval elapsed from SA– and for depression. When the intervals from SA– were added, the effects of the EEG classification for depression increased.

Conclusion

These findings suggest that EEG regions associated with QOL showing nonlinearity are useful for classifying SA– and depression.

Wavelet denoising and non‐denoising EEG sets in the bilateral posterior temporal‐occipital, centro‐parietal, parieto‐occipital, and Fz‐Cz of the 10‐20 method were associated with the Side Effects and Life Satisfaction inventory and demonstrated nonlinearity, and the moderate effects of classification for the interval elapsed from seizure without awareness and for depression. When the intervals from seizure without awareness were added, the effects of the EEG classification for depression increased.