Ictal EEG Research Articles

Ictal and interictal epileptic networks of 34 patients with Hypothalamic Hamartoma on scalp electroencephalography.

To investigate ictal and interictal cortical involvement in epilepsy associated with hypothalamic hamartoma. We conducted a retrospective study of 34 patients with epilepsy and hypothalamic hamartoma, using data from long-term video-EEG-monitoring. We analyzed onset and propagation of ictal and interictal scalp EEG and visualized the resulting networks of cortical involvement. According to clinical and EEG criteria we grouped patients in: (1) focal disease, (2) focal advanced disease, (3) extended disease. We compared networks between these groups and different seizure types. Eight patients underwent several video-EEGs, and we analyzed all to investigate epilepsy progression. Epileptic activity mainly involved frontal and temporal cortex regions. Involvement of frontal regions was more common in advanced stages of the disease, and strong fronto-temporal connections were observed in the ictal networks of patients in intermediate stages (25.0% (left) and 35.7% (right) of seizures with EEG correlate). Occurrence and timing of EEG-correlate significantly depended on the seizure type (Chi-2-test, p<<0.001). In patients with several EEGs, seizure activity increased by +0.67 seizures/day/month (mean). There were significant differences between patients with normal and impaired cognitive function, with the latter showing pronounced ictal involvement of fronto-temporal cortex areas (p<0.001). Overall, in epilepsy due to hypothalamic hamartoma, cortical involvement focuses on frontal and temporal regions and varies systematically with the stage of the disease, different seizure types and presence of impaired cognitive function. We propose that these data may help improve our general understanding of epileptogenesis and potentially provide insights for the surgical therapy of hypothalamic hamartomas.

Patterns of ictal surface EEG in occipital seizures: A simultaneous scalp and intracerebral recording study

ObjectiveTo describe the ictal scalp EEG patterns of occipital seizures (OS) and their spatiotemporal correlations with intracerebral occipital ictal discharges derived from simultaneous SEEG-EEG recordings. MethodsPatients with SEEG confirmed OS (14 OS from 8 patients) were selected from an epilepsy surgery center and were monitored 3–10 days using simultaneous scalp EEG and SEEG recordings. ResultsOn scalp EEG, the most common onset patterns were background activity suppression (28.6 %) and high amplitude slow wave corresponding to intracerebral DC-shift (28.6 %) and occurred with a median delay of 0 s after intra-cerebral onset. The initial discharge involved occipital electrodes in only 50 % of the seizures (7/14) with additional basal temporal (8/14) or parietal electrodes (5/14). The onset was ipsilateral to the intra-cerebral onset zone in 71.4 % of seizures and bilateral in the remaining (28.6 %). The most common propagation pattern was either unilateral (50 %) or bilateral (50 %) and a rhythmic slow activity (66.7 %). Different OS subtypes display distinct scalp EEG patterns. ConclusionScalp EEG accurately determines intra-cerebral seizure onset time in OS and has good lateralizing value. However, initial scalp modification does not always involves occipital electrodes and the second modification is well lateralizing in only 50 % of seizures. SignificanceThis study describes will help clinicians to better identify OS during video EEG and better plan intra-cerebral explorations for epilepsy surgery.

Clinical utility of ultra long-term subcutaneous electroencephalographic monitoring in drug-resistant epilepsies: a "real world" pilot study.

This study was undertaken to assess the clinical utility, safety, and tolerability in epilepsy patients of ultra long-term monitoring with a novel subcutaneous electroencephalographic (EEG) device (sqEEG). Five patients with drug-resistant focal epilepsy were implanted (one patient bilaterally) with sqEEG. In phase 1, we assessed sqEEG sensitivity for seizure recording by recording seizures simultaneously with scalp EEG in the epilepsy monitoring unit (EMU). sqEEG was scored either visually (v-sqEEG) or by using a semiautomatic algorithm (EpiSight; E-sqEEG). In phase 2, the patients were monitored as outpatients for 3-6 months. sqEEG data were analyzed monthly, evaluating concordance of data obtained by v-sqEEG, E-sqEEG, and patients' diaries. v-sqEEG data were used to guide treatment adjustments. sqEEG-related side effects were assessed throughout the study. In phase 1, v-sqEEG detected all seizures recorded in the EMU in all patients, whereas E-sqEEG was as effective in three patients. In the other two patients, E-sqEEG detected only a proportion or none of the seizures, respectively. Sensitivity of E-sqEEG depended on the ictal EEG features. In phase 2, a 100% concordance between E-sqEEG and v-sqEEG in seizure detection was observed for the same three patients as in phase 1. In the other two patients (one implanted bilaterally), effectiveness of E-sqEEG in detecting seizure as compared to v-sqEEG ranged from 0% to 83%. v-sqEEG showed that all patients reported in their diaries fewer seizures than they actually suffered. In four of five patients, v-sqEEG showed that the treatment adjustments had been ineffective or associated with a seizure increment. The only side effect was an infection at the implantation site in one patient. The sqEEG system could collect reliable information on seizure activity, thus providing clinically relevant information. Sensitivity of EpiSight in detecting seizures varied across patients, depending on the ictal EEG features. sqEEG ultra long-term monitoring was feasible and well tolerated.

Left hemispheric epilepsy is more frequent and has worse clinical course that right hemispheric epilepsy

IntroductionFew studies demonstrated that focal epilepsy (FE) with left hemispheric (LH) seizure onset is more frequent than with right hemispheric (RH) seizure onset. In addition, patients with LH seizure onset show worse clinical course compared to those with RH seizure onset. The aim of our study was to investigate both issues in a great cohort of FE patients. MethodsIn the retrospective study, we reviewed the clinical and paraclinical data of 682 patients with exclusively LH or RH seizure onset. We ascertained the laterality of seizure onset mainly by ictal and postictal semiology and ictal EEG findings. In the absence of ictal data, the basis of lateralization was the evidence of unilateral structural brain abnormality together with a corresponding interictal EEG finding. The endpoint of analysis of the clinical course was the presence/absence of five-year remission on drug treatment in the first ten years of treatment. ResultsOut of the 682 patients, 378 (55.4 per cent) had LH and 304 (44.6 per cent) had RH seizure onset. The difference was statistically significant (p = 0.04). Out of them, 213 LH and 156 RH patients were eligible to evaluate prognosis. Five-years-remission was attained by 71 patients (33.3 per cent) in the LH, and 65 (41.7 per cent) in the RH group. The difference was statistically significant (p = 0.05). ConclusionWe demonstrated the LH dominance of seizure onset and the worse clinical course of the patients with LH seizure onset. The findings are manifestations of the lateralized epileptic propensity of the brain. The dissimilar clinical course of the patient with LH and RH seizure onset may shape the general prognostic scheme in FE patients.

Dreams interrupted: characteristics of REM sleep-associated seizures and status epilepticus.

Seizures are rare in rapid eye movement sleep (REM). However; seizures sometimes occur in REM, and a small number of focal epilepsy patients display their maximum rate of interictal epileptiform discharges in REM. We sought to systematically identify and characterize seizures in REM. We reviewed all admissions to the Epilepsy Monitoring Unit (EMU) at the Winnipeg Health Sciences Centre over 12-months in 2014-2015. American Academy of Sleep Medicine sleep-stage scoring was initially applied in the standard 30-second epochs. Then, to capture sudden changes in sleep-wake state on shorter timescales that are associated with seizure formation and propagation, we re-scored ictal and peri-ictal EEG epochs every 1 second. Patients found to have seizures in REM were subject to chart review spanning three years pre- and post-admission. REM seizures occurred in 3/63 EMU patients. Notably, one patient exhibited continuous epileptiform activity, consistent with focal nonconvulsive electrographic status epilepticus, throughout REM cycles for each night of her admission. Otherwise, discrete REM seizures constituted a small fraction of the other patients' total seizures (range 5.0-8.3%), occurred shortly after REM onset from non-REM 2, and were manifest as minor epileptic arousals. Our results confirm that REM seizures are rare, while highlighting outliers who widen the known spectrum of heterogeneous sleep effects on seizures/epilepsy. We also report the first case of paradoxical status epilepticus in REM.

The significance of multimodality approach in the management of non-lesional drug-resistant focal parietal lobe epilepsies.

Due to extensive connectivity of the parietal lobe, non-lesional drug-resistant (DRE) parietal lobe epilepsies (PLEs) are difficult to localize and often imitate other epilepsies. Therefore, patients with PLEs have low rates of seizure freedom following epilepsy surgery. Previous studies have highlighted the need to combine EEG and semiology for more accurate localization of PLEs. As sophisticated tools for localization become more available, the use of multiple different neuroimaging and neurophysiologic diagnostic tests may more readily identify PLE. We hereby report a unique case of a complex localization in a non-lesional PLE, which was initially falsely localized to frontal lobe. This case underscores the utility of voxel-based morphometry (VBM) in identifying an epileptogenic lesion on a non-lesional MRI and the significance of multimodality approach including PET, magnetoencephalopathy (MEG), interictal and ictal EEG, semiology and cortical stimulation for accurate localization of PLEs. Understanding epilepsy through multimodality approach in this fashion can help with accurate localization especially in difficulty to localize and deceptive non-lesional PLEs. PLAIN LANGUAGE SUMMARY: Parietal lobe epilepsies are hard to pinpoint in the brain and can mimic other types of epilepsy, especially when brain MRIs appear normal. As sophisticated tools for locating epilepsies in the brain become more available, using multiple diagnostic tests may help identify parietal lobe epilepsies more easily. We describe a unique case of a parietal lobe epilepsy patient with normal brain MRI whose epilepsy was initially misidentified as being in the frontal lobe. Using various advanced diagnostic tests, we accurately found the epilepsy's true location in the parietal lobe and successfully treated the patient with surgery.

The value of ictal scalp EEG in focal epilepsies surgery: a retrospective analysis.

To describe the association between preoperative ictal scalp electroencephalogram (EEG) results and surgical outcomes in patients with focal epilepsies. The data of consecutive patients with focal epilepsies who received surgical treatments at our center from January 2012 to December 2021 were retrospectively analyzed. Our data showed that 44.2% (322/729) of patients had ictal EEG recorded on video EEG monitoring during preoperative evaluation, of which 60.6% (195/322) had a concordant ictal EEG results. No significant difference of surgery outcomes between patients with and without ictal EEG was discovered. Among MRI-negative patients, those with concordant ictal EEG had a significantly better outcome than those without ictal EEG (75.7% vs. 43.8%, p = 0.024). Further logistic regression analysis showed that concordant ictal EEG was an independent predictor for a favorable outcome (OR = 4.430, 95%CI 1.175-16.694, p = 0.028). Among MRI-positive patients, those with extra-temporal lesions and discordant ictal EEG results had a worse outcome compared to those without an ictal EEG result (44.7% vs. 68.8%, p = 0.005). Further logistic regression analysis showed that discordant ictal EEG was an independent predictor of worse outcome (OR = 0.387, 95%CI 0.186-0.807, p = 0.011) in these patients. Furthermore, our data indicated that the number of seizures was not associated with the concordance rates of the ictal EEG, nor the surgical outcomes. The value of ictal scalp EEG for epilepsy surgery varies widely among patients. A concordant ictal EEG predicts a good surgical outcome in MRI-negative patients, whereas a discordant ictal EEG predicts a poor postoperative outcome in lesional extratemporal lobe epilepsy.

Are the seizures under control or unnoticed? Electroclinical evaluation of epilepsy with eyelid myoclonia

PurposeIn this study, patients with epilepsy with eyelid myoclonia (E-EM) were evaluated according to their EEG findings, seizure outcomes, and their consistency with the final ictal EEG findings. We also investigated the possible prognostic factors. MethodsPatients with E-EM and at least two years of follow-up in our clinic were included in the study. We analyzed the presence of eyelid myoclonia, absence and myoclonic seizures, and generalized tonic-clonic seizures for the prior two years and then verified with the latest ictal EEG features. Video-EEGs were analyzed according to the background activity, the existence of generalized spike-wave discharge or polyspike-wave complexes, focal spike-wave discharge, photoparoxysmal responses, and fast activity. Results21 patients were involved in this study. In six patients, the seizures were undetected on the first EEGs, whereas they were detected on subsequent ones. The seizures were captured on the first EEGs of six patients; however, they disappeared on subsequent ones. Only one patient had seizures detected on every EEG. The consistency of the seizures was variable in eight patients. At the final follow-up, seizures were reported as being under control for more than two years in 12 patients, according to patients and their parents’ reports. However, ictal EEG findings were detected in six of these patients. No electroclinical feature was associated with seizure freedom. ConclusionThis study provides further evidence that seizure freedom in E-EM patients is overestimated. The patients and their parents may not be aware of the seizures. Therefore, video-EEG monitorization is essential during follow-up.

Lateralizing value and clinicoradiological features of asymmetric last clonic jerks in temporal and extratemporal epilepsy

Seizure semiology and electroencephalograph (EEG) are very important for determining seizure type, hemisphere lateralization, or localization. Clinical symptoms of focal seizures, as well as findings at the onset or end of a focal to bilateral tonic–clonic seizure (FBTCS), are highly informative for lateralization. This study aimed to investigate the relationship of asymmetric last clonic jerk in patients with temporal or extratemporal lobe epilepsy with pathologies, localization, lateralization, or other semiological findings detected in neuroimaging or neuro psychometric tests and its positive predictive value for the detection of hemisphere lateralization based on seizure onset ictal EEG activation. 44 patients with asymmetric last clonic jerks (aLCJ) who were followed up in our VEM unit were randomized 1:1 with epilepsy patients without. In patients with ipsilateral automatism and contralateral posture or gustatory and olfactory hallucinations aLCJ was less or absent. In patients with unilateral tonic activity, aLCJ was more common. The positive predictive value of aLCJ for ictal EEG activation lateralization was 86.36%. In conclusion, asymmetric last clonic beat is valuable for lateralization of FBTCS and should be considered. Its presence strongly and reliably lateralizes to the side of seizure onset.

Seizure detection using nonlinear measures over EEG frequency bands and deep learning classifiers

Epilepsy is a brain disorder that causes patients to suffer from convulsions, which affects their behavior and way of life. Epilepsy can be detected with electroencephalograms (EEGs), which record brain neural activity. Traditional approaches for detecting epileptic seizures from an EEG signal are time-consuming and annoying. To supersede these traditional methods, a myriad of automated seizure detection frameworks based on machine learning techniques have recently been developed. Feature extraction and classification are the two essential phases for seizure detection. The classifier assigns the proper class label after feature extraction lowers the input pattern space while maintaining useful features. This paper proposes a new feature extraction method based on calculating nonlinear features from the most relevant EEG frequency bands. The EEG signal is first decomposed into smaller time segments from which a vector of nonlinear features is computed and supplied to machine learning (ML) and deep learning (DL) classifiers. Experiments on the Bonn dataset reveals an accuracy of 99.7% reached in classifying normal and ictal EEG signals; and an accuracy of 98.8% in the discrimination of ictal and interictal EEG signals. Furthermore, a performance of 100% is achieved on the Hauz Khas dataset. The classification results of the proposed approach were compared to those from published state of the art techniques. Our results are equivalent to or better than some recent studies appeared in the literature.

Correlation Analysis of Multi-Scale Ictal EEG Signals in Juvenile Myoclonic Epilepsy.

To explore the time-frequency structure and cross-scale coupling of electroencephalography (EEG) signals during seizure in juvenile myoclonic epilepsy (JME), correlations between different leads, as well as dynamic evolution in epileptic discharge, progression and end of seizure were examined. EEG data were obtained for 10 subjects with JME and 10 normal controls and were decomposed using gauss continuous wavelet transform (CWT). The phase amplitude coupling (PAC) relationship between the 11th (4.57 Hz) and 17th (0.4 Hz) scale was investigated. Correlations were examined between the 11th and 17th scale EEG signals in different leads during seizure, using multi-scale cross correlation analysis. The time-frequency structure of JME subjects showed strong rhythmic activity in the 11th and 17th scales and a close PAC was identified. Correlation analysis revealed that the ictal JME correlation first increased in the anterior head early in seizure and gradually expanded to the posterior head. PAC was exhibited between the 11th and 17th scales during JME seizure. The results revealed that the correlation in the anterior leads was higher than the posterior leads. In the perictal period, the 17th scale EEG signal preceded the 11th scale signal and remained for some time after a seizure. This suggests that the 17th scale signal may play an important role in JME seizure.

Prognostic value of scalp EEG ictal patterns in epilepsy surgery of hippocampal sclerosis.

Temporal lobe epilepsy associated with hippocampal sclerosis (TLE-HS) is a surgically treatable epileptic syndrome. While the core of pre-surgical evaluations rely on video-EEG, recent studies question the necessity of recorded seizures denying a possible role of ictal EEG in surgical decision. This study aims to retrospectively assess the prognostic value of EEG ictal patterns in TLE-HS, in order to identify which patients need further investigations before offering surgery. We included TLE-HS patients who underwent surgery with at least one captured seizure during non-invasive pre-surgical video-EEG recordings.They were classified in "mesial" and "lateral/mixed", according to the ictal EEG patterns, defined by the frequency of the discharge (mesial ≥ 5Hz, lateral < 5Hz). Seizure outcome was assessed by Engel's Class. Statistical analyses were performed to evaluate associations between EEG patterns and post-surgical outcomes. Sixty-nine exhibited a mesial pattern, forty- two displayed lateral/mixed patterns. Mesial pattern group had a significantly higher rate of postsurgical seizure freedom (82.7% vs. 28.6%). Gender, age of onset, age at surgery, duration of epilepsy, seizure frequency, and lateralization did not influence the outcome. Mesial pattern significantly correlated with favorable outcomes (p < 0.001), suggesting its potential predictive value. This retrospective study proposes ictal EEG patterns as possible predictors of postoperative prognosis in TLE-HS. A mesial pattern correlates with better outcomes, indicating a potentially more circumscribed epileptogenic zone. Patients with lateral/mixed patterns may benefit from additional investigations to delineate the epileptogenic zone. Further studies are warranted to validate and extend these findings.

Electroclinical Features of Infantile Epileptic Spasms Syndrome.

Epileptic spasms are a unique, age-dependent manifestation of epilepsies in infancy and early childhood, commonly occurring as part of infantile epileptic spasms syndrome. Developmental stagnation and subsequent decline may occur in children with epileptic spasms, partly due to the abundant high-amplitude interictal epileptiform and slow wave abnormalities. Early recognition and treatment of epileptic spasms, along with the reversal of the electroencephalography (EEG) findings, are critical for improving outcomes. Recognizing hypsarrhythmia and its variations is key to confirming the diagnosis. The various patterns of hypsarrhythmia are not etiology specific, but could indicate the severity of the disease. Several scoring systems have been proposed to improve the inter-rater reliability of recognizing hypsarrhythmia and to assess EEG progress in response to treatment. Ictal patterns during spasms are brief and composed of slow waves, sharp transients, fast activity, and voltage attenuation, either in isolation or more commonly as a combination of these waveforms. Ictal patterns are commonly diffuse, but may be lateralized to one hemisphere in children with structural etiology. A subset of patients with epileptic spasms has a surgically remediable etiology, with readily identifiable lesions on neuroimaging in most cases. Asymmetry in epileptic spasms, concurrent focal seizures, and asymmetric interictal and ictal EEG findings may be present, but a lack of focality in electrophysiological findings is not uncommon. Intracranial EEG features of epileptic spasms have been described, but the utility of intracranial EEG monitoring in surgical candidates with overt focal epileptogenic lesions on magnetic resonance imaging is questionable, and surgery could be performed using noninvasive data.

EEG based automated detection of seizure using machine learning approach and traditional features

The detection of epileptic seizures is key for neurologists to initiate the right treatment at the earliest. However, the traditional methods are dependent on manual diagnosis which are error-prone. Hence, the current article presents an automated machine learning-based approach with competing results to detect seizures and its types using traditional features. Towards this goal, the present paper explores the performance of two fractal parameters namely Higuchi and, Kat’z fractal dimension, along with power spectral density and spectral entropy to detect EEG signals with seizure and non-seizure characteristics. To validate the performance via cross-validation technique, the features are extracted and experimented from three publically available databases such as Bern-Barcelona (focal, non-focal), Khas (preictal, interictal, ictal), and Bonn. Thereafter evaluated with different machine learning algorithms like Random Forest classifier, Ada Boost classifier, Gradient Boosting classifier, Extra Tree classifier, SVM classifier, and Naive Bayes classifier. The experiments showed 100% F1 score and accuracy in classifying focal and non-focal signals for random forest and extra tree classifier methods. To the best of our knowledge, this is the first paper reporting this score for the Bern-Barcelona dataset. The same features are also experimented for the detection of interictal, ictal and preictal scenarios. In the process, 100% accuracy is obtained in classifying interictal and ictal EEG signals, and an accuracy of 94% is attained in classifying interictal-preictal signals. This score is 14% higher than the current state-of-the-art methods using the same database. The features are also used in the Bonn database, where the proposed approach gave an accuracy of 100% same as the state-of-the-art methods.

Postictal recovery of orientation in person, place and time relates to restoration of cortical activity after electroconvulsive therapy

IntroductionMost patients show temporary impairments in clinical orientation (i.e., orientation in person, place, and time) after electroconvulsive therapy (ECT)-induced seizures. It is unclear whether postictal reorientation is related to electroencephalography (EEG) restoration. This tentative relationship may shed light on mechanistic aspects of reorientation after ECT.ObjectivesTo study whether postictal EEG restoration after an ECT-induced seizure is related to recovery of clinical orientation in the cognitive domains person, place and time.MethodsWe performed a longitudinal study in ECT patients and collected continuous postictal EEGs. Postictal EEG restoration was estimated by the evolution of the normalized alpha/delta ratio (ADR). Recovery of orientation in the cognitive domains of person, place, and time was assessed using the Reorientation Time (ROT) questionnaire. In each cognitive domain, a linear mixed model was fitted to investigate the relationship between ROT and postictal EEG restoration. In these models, other (ECT-)parameters including seizure duration, use of benzodiazepines and electrode placement were included.ResultsIn total, 272 ictal and postictal EEG recordings of 32 patients were included. In all domains, longer ROT was associated with slower postictal EEG recovery. Longer seizure duration and use of benzodiazepines were related to longer ROT in all domains. Increased total charge of the ECT-stimulus was associated with increased ROT in place and age was positively associated with ROT in time.Image:Image 2:ConclusionsWe show a relationship between restoration of the postictal EEG and clinical reorientation in person, place and time after ECT-induced seizures. This indicates that clinical reorientation probably depends on gradual cortical synaptic recovery. Increased seizure duration and the use of benzodiazepines were also related to increased ROT values. Longer seizures and use of benzodiazepines may induce longer postictal synaptic depression.Disclosure of InterestNone Declared

Semiology and Neurophysiology of Clonic Seizures: A Report of 39 Patients.

Clonic seizures are currently defined as repetitive and rhythmic myoclonic contractions of a specific body part, producing twitching movements at a frequency of 0.2-5 Hz. There are few studies in the literature that have reported a detailed analysis of the semiology, neurophysiology, and lateralizing value of clonic seizures. In this article, we aim to report our findings from a retrospective review of 39 patients. We identified 39 patients (48 seizures) from our center who had been admitted with clonic seizures between 2016 and 2022. We performed a retrospective review of their video-EEG recordings for semiology and ictal EEG findings. Seventeen patients also had simultaneous surface-EMG (sEMG) electrodes placed on affected body parts, which were analyzed as well. The most common initial affected body parts were face, arm, and hand. In most of the cases, seizures propagated from lower face to upper face and distal hand to proximal arm. The most common seizure-onset zone was the perirolandic region, and the most common EEG seizure pattern was paroxysmal rhythmic monomorphic activity. The lateralizing value for EEG seizure onset to contralateral hemisphere in unilateral clonic seizures (n = 39) was 100%. All seizures recorded with sEMG electrodes demonstrated synchronous brief tetanic contractions of agonists and antagonists, alternating with synchronous silent periods. Arrhythmic clonic seizures were associated with periodic epileptiform discharges on the EEG, whereas rhythmic clonic seizures were associated with paroxysmal rhythmic monomorphic activity. Overall, the most common etiology was cerebrovascular injuries, followed by tumors. Clonic seizures are characterized by synchronized brief tetanic contractions of agonist and antagonistic muscles alternating with synchronized silent periods, giving rise to the visible twitching. The most common seizure onset zone is in the perirolandic region, which is consistent with the symptomatogenic zone being in the primary motor area. The lateralizing value of unilateral clonic seizures for seizure onset in the contralateral hemisphere is 100%.

Evaluation of the Relation between Ictal EEG Features and XAI Explanations.

Epilepsy is a neurological disease with one of the highest rates of incidence worldwide. Although EEG is a crucial tool for its diagnosis, the manual detection of epileptic seizures is time consuming. Automated methods are needed to streamline this process; although there are already several works that have achieved this, the process by which it is executed remains a black box that prevents understanding of the ways in which machine learning algorithms make their decisions. A state-of-the-art deep learning model for seizure detection and three EEG databases were chosen for this study. The developed models were trained and evaluated under different conditions (i.e., three distinct levels of overlap among the chosen EEG data windows). The classifiers with the best performance were selected, then Shapley Additive Explanations (SHAPs) and Local Interpretable Model-Agnostic Explanations (LIMEs) were employed to estimate the importance value of each EEG channel and the Spearman's rank correlation coefficient was computed between the EEG features of epileptic signals and the importance values. The results show that the database and training conditions may affect a classifier's performance. The most significant accuracy rates were 0.84, 0.73, and 0.64 for the CHB-MIT, Siena, and TUSZ EEG datasets, respectively. In addition, most EEG features displayed negligible or low correlation with the importance values. Finally, it was concluded that a correlation between the EEG features and the importance values (generated by SHAP and LIME) may have been absent even for the high-performance models.

Epileptic spasms in clusters without hypsarrhythmia in infancy and childhood: A single age-dependent type of epilepsy or well-defined epileptic syndrome?

ObjectiveIn this study, we present the electroclinical features and outcomes of 92 patients with epileptic spasms (ES) in clusters without modified or classical hypsarrhythmia that started in either in infancy or in childhood; we compared both groups in terms of electroclinical features, etiology, treatment, evolution, and outcome. MethodsBetween June 2000 and July 2022, 92 patients met the electroclinical diagnostic criteria of ES in clusters without hypsarrhythmia. Patients with ES associated with other epileptic encephalopathies including West Syndrome, as well as those with the specific etiology of ES and developmental and epileptic encephalopathy associated with CDKL5 were excluded. ResultsThe patients were divided into two groups based on the age at ES onset: those with ES onset before (Group 1) and those with ES onset after 2 years of age (Group 2). The features of ES and the type of associated seizures before and after ES onset, as well as the interictal and ictal EEG and electromyography findings were similar in both groups. The etiologies were mainly structural (40.2%), genetic (11.9%), and unknown (44.6%) in majority of the patients in both groups. Thirty-one patients were seizure-free, while in the remaining patients the seizures continued. Nine patients (9.8%) with unilateral structural lesions underwent surgery with good results. The neurological abnormalities and developmental findings prior to ES onset depended on the underlying etiology. ConclusionOur series of patients may represent a well-defined epileptic syndrome or type of epilepsy with onset in infancy or childhood characterized by ES in clusters without hypsarrhythmia associated with focal and generalized seizures and EEG paroxysms without neurological deterioration.

Electroclinical Landscape of Infantile Epileptic Spasms Syndrome.

To elucidate the electroclinical characteristics of infantile epileptic spasms syndrome (IESS) and to determine any potential association among these with underlying etiologies and response to therapy. Sixty-eight, treatment-naive children with IESS underwent long-term video electroencephalogram (EEG) recording, which was used to characterize the semiology, ictal, and inter-ictal EEG patterns. Children were further followed up to assess electroclinical predictors of etiologies and short-term therapeutic response. Of 68 children enrolled (69% boys), the median age at enrollment was 10.5 mo (IQR-8). Eighty-eight percent of children had flexor spasms, followed by mixed (7%) and extensor (4.4%). Asymmetrical spasms were noted in 17.6% children, and all of them had underlying structural etiology. Two children had the status of epileptic spasms. In the present cohort, authors recognized five distinct ictal EEG correlates of epileptic spasms; the frontocentral dominant slow wave was the most prevalent (32%), followed by the generalized slow-wave complex with superimposed fast rhythm in 29.4%. The occipital dominant slow wave complex was a peculiar pattern in 16%. The major underlying etiologies were hypoxic-ischemic brain injuries (36.7%) and neonatal hypoglycemic brain injuries (22%). Besides asymmetric spasms, authors could not identify any significant association among electroclinical characteristics, underlying etiologies and response to therapy in this study. The electroclinical landscape of IESS is peculiar and diverse in developing countries. The presence of asymmetrical spasms indicated underlying structural etiology.

Learn how to interpret and use intracranial EEG findings.

Epilepsy surgery is the therapy of choice for many patients with drug-resistant focal epilepsy. Recognizing and describing ictal and interictal patterns with intracranial electroencephalography (EEG) recordings is important in order to most efficiently leverage advantages of this technique to accurately delineate the seizure-onset zone before undergoing surgery. In this seminar in epileptology, we address learning objective "1.4.11 Recognize and describe ictal and interictal patterns with intracranial recordings" of the International League against Epilepsy curriculum for epileptologists. We will review principal considerations of the implantation planning, summarize the literature for the most relevant ictal and interictal EEG patterns within and beyond the Berger frequency spectrum, review invasive stimulation for seizure and functional mapping, discuss caveats in the interpretation of intracranial EEG findings, provide an overview on special considerations in children and in subdural grids/strips, and review available quantitative/signal analysis approaches. To be as practically oriented as possible, we will provide a mini atlas of the most frequent EEG patterns, highlight pearls for its not infrequently challenging interpretation, and conclude with two illustrative case examples. This article shall serve as a useful learning resource for trainees in clinical neurophysiology/epileptology by providing a basic understanding on the concepts of invasive intracranial EEG.