The misdiagnosis of epilepsy: Appraising risks and managing uncertainty

Quantitative EEG Spectral Features Differentiate Genetic Epilepsies and Predict Neurologic Outcomes

EEG plays an integral part in the diagnosis and management of children with genetic epilepsies. Nevertheless, how quantitative EEG features differ between genetic epilepsies and neurological outcomes remains largely unknown. Here, we aimed to identify quantitative EEG biomarkers in children with epilepsy and a genetic diagnosis in STXBP1 , SCN1A , or SYNGAP1 , and to assess how quantitative EEG features associate with neurological outcomes in genetic epilepsies more broadly. We analyzed individuals with pathogenic variants in STXBP1 (95 EEGs, n =20), SCN1A (154 EEGs, n =68), and SYNGAP1 (46 EEGs, n =21) and a control cohort of individuals without epilepsy or known cerebral disease (847 EEGs, n =806). After removing artifacts and epochs with excess noise or altered state from EEGs, we extracted spectral features. We validated our preprocessing pipeline by comparing automatically-detected posterior dominant rhythm (PDR) to annotations from clinical EEG reports. Next, as a coarse measure of pathological slowing, we compared the alpha-delta bandpower ratio between controls and the different genetic epilepsies. We then trained random forest models to predict a diagnosis of STXBP1 , SCN1A , and SYNGAP1 . Finally, to understand how EEG features vary with neurological outcomes, we trained random forest models to predict seizure frequency and motor function. There was strong agreement between the automatically-calculated PDR and clinical EEG reports ( R 2 =0.75). Individuals with STXBP1 -related epilepsy have a significantly lower alpha-delta ratio than controls ( P< 0.001) across all age groups. Additionally, individuals with a missense variant in STXBP1 have a significantly lower alpha-delta ratio than those with a protein-truncating variant in toddlers ( P< 0.001), children ( P =0.02), and adults ( P< 0.001). Models accurately predicted a diagnosis of STXBP1 (AUC=0.91), SYNGAP1 (AUC=0.82), and SCN1A (AUC=0.86) against controls and from each other in a three-class model (accuracy=0.74). From these models, we isolated highly correlated biomarkers for these respective genetic disorders, including alpha-theta ratio in frontal, occipital, and parietal electrodes with STXBP1 , SYNGAP1 , and SCN1A , respectively. Models were unable to predict seizure frequency (AUC=0.53). Random forest models predicted motor scores significantly better than age-based null models ( P< 0.001), suggesting spectral features contain information pertinent to gross motor function. In summary, we demonstrate that STXBP1 -, SYNGAP1- , and SCN1A -related epilepsies have distinct quantitative EEG signatures. Furthermore, EEG spectral features are predictive of some functional outcome measures in patients with genetic epilepsies. Large-scale retrospective quantitative analysis of clinical EEG has the potential to discover novel biomarkers and to quantify and track individuals' disease progression across development.

Preliminary development and validation of the Indonesian Pediatric Epilepsy Questionnaire (INA-PEPSI) to determine epilepsy and distinguish focal and generalized epilepsy in infants and children with unprovoked seizure in low-resource settings

OBJECTIVE

To outline the preliminary development and validation of a questionnaire for diagnosing epilepsy and distinguishing focal and generalized epilepsy among infants and children in Indonesia, where electroencephalography and pediatric neurologists are generally not available.

METHODS

A 10-question questionnaire comprising of 43 items was developed through literature review and expert panel discussions. Then, the questionnaire was administered by pediatricians to 75 children aged 1 month to 18 years old presenting with >1 episode of unprovoked seizures at an interval of >24 h. Subsequently, the questionnaire was assessed for content validity with item-level and scale-level content validity indices and ratio, construct validity with item-total correlation tests, criterion validity with diagnostic parameter assessments, and inter-rater reliability using Cohen's kappa (κ) and internal consistency with Cronbach's alpha (α) coefficient.

RESULTS

The questionnaire exhibited favorable internal validity and reliability in diagnosing epilepsy and distinguishing focal and generalized epilepsy, with excellent content (both indices and ratio at 1) and construct validity (rcount > rtable at p < 0.001), inter-rater reliability (κ = 0.86 and κ = 0.84), and internal consistency (α = 0.634 and α = 0.806). The questionnaire had a sensitivity and specificity of 96.4% (95%CI 89.1-99.5%) and 95.0% (79.5-99.6%) (area under the curve [AUC] 0.946 [0.900-0.992, p < 0.001]) in diagnosing epilepsy and 80.0% (57.4-95.7%) and 97.4% (89.7-99.2%) (AUC 0.889 [0.783-0.995, p < 0.001]) in distinguishing focal and generalized epilepsy, with a misdiagnosis rate of 4.0%.

SIGNIFICANCE

The questionnaire shows promising potential in diagnosing epilepsy and distinguishing focal and generalized epilepsy. Further external validation studies in larger and more diverse populations are required to confirm our findings.

PLAIN LANGUAGE SUMMARY

The diagnosis of epilepsy in children is challenging, particularly in resource-limited settings such as Indonesia, where advanced diagnostic tests and pediatric neurologists are scarce. The Indonesian Pediatric Epilepsy Questionnaire (INA-PEPSI) is designed to address these limitations by enabling healthcare professionals in Indonesia to diagnose epilepsy and classify its types without relying on advanced diagnostic tools. Although the questionnaire is still in the early stages of development and validation, this study demonstrates that the questionnaire exhibits good overall diagnostic performance in diagnosing epilepsy and distinguishing epilepsy types among Indonesian children.

Prediction begins with diagnosis: Estimating seizure recurrence risk in the First Seizure Clinic

OBJECTIVES

To assess the feasibility of using a seizure recurrence prediction tool in a First Seizure Clinic, considering (1) the accuracy of initial clinical diagnoses and (2) performance of automated computational models in predicting seizure recurrence after first unprovoked seizure (FUS).

METHODS

To assess diagnostic accuracy, we analysed all sustained and revised diagnoses in patients seen at a First Seizure Clinic over 5 years with 6+ months follow-up ('accuracy cohort', n = 487). To estimate prediction of 12-month seizure recurrence after FUS, we used a logistic regression of clinical factors on a multicentre FUS cohort ('prediction cohort', n = 181), and compared performance to a recently published seizure recurrence model.

RESULTS

Initial diagnosis was sustained over 6+ months follow-up in 69% of patients in the 'accuracy cohort'. Misdiagnosis occurred in 5%, and determination of unclassified diagnosis in 9%. Progression to epilepsy occurred in 17%, either following FUS or initial acute symptomatic seizure. Within the 'prediction cohort' with FUS, 12-month seizure recurrence rate was 41% (95% CI [33.8%, 48.5%]). Nocturnal seizure, focal seizure semiology and developmental disability were predictive factors. Our model yielded an Area under the Receiver Operating Characteristic curve (AUC) of 0.60 (95% CI [0.59, 0.64]).

CONCLUSIONS

High clinical accuracy can be achieved at the initial visit to a First Seizure Clinic. This shows that diagnosis will not limit the application of seizure recurrence prediction tools in this context. However, based on the modest performance of currently available seizure recurrence prediction tools using clinical factors, we conclude that data beyond clinical factors alone will be needed to improve predictive performance.

The impact of paediatric epilepsy and co-occurring neurodevelopmental disorders on functional brain networks in wake and sleep

Epilepsy is one of the most common neurological disorders in children. Diagnosing epilepsy in children can be very challenging, especially as it often coexists with neurodevelopmental conditions like autism and ADHD. Functional brain networks obtained from neuroimaging and electrophysiological data in wakefulness and sleep have been shown to contain signatures of neurological disorders, and can potentially support the diagnosis and management of co-occurring neurodevelopmental conditions. In this work, we use electroencephalography (EEG) recordings from children, in restful wakefulness and sleep, to extract functional connectivity networks in different frequency bands. We explore the relationship of these networks with epilepsy diagnosis and with measures of neurodevelopmental traits, obtained from questionnaires used as screening tools for autism and ADHD. We explore differences in network markers between children with and without epilepsy in wake and sleep, and quantify the correlation between such markers and measures of neurodevelopmental traits. Our findings highlight the importance of considering the interplay between epilepsy and neurodevelopmental traits when exploring network markers of epilepsy.

Degenerative Cervical Myelopathy Awareness in Primary Care: UK National Cross-Sectional Survey of General Practitioners

BACKGROUND

Degenerative cervical myelopathy (DCM) is a progressive neurological condition, characterized by spinal cord injury secondary to degenerative changes in the spine. Misdiagnosis in primary care forms part of a complex picture leading to an average diagnostic delay of 2 years. This leads to potentially preventable and permanent disability. A lack of awareness secondary to deficits in postgraduate education may contribute to these delays.

OBJECTIVE

This study aims to assess the awareness of DCM in the setting of general practice.

METHODS

General practitioners completed a quantitative web-based cross-sectional questionnaire. The 17-item questionnaire captured data regarding demographics, subjective awareness, and objective knowledge. The questionnaire was disseminated via professional networks, including via practice managers and senior practice partners. Incentivization was provided via a bespoke DCM fact sheet for those that completed the survey.

RESULTS

A total of 54 general practitioners representing all 4 UK nations responded to the survey. General practitioners most commonly self-assessed that they had "limited awareness" of DCM (n=24, 51%). General practitioners felt most commonly "moderately able" to recognize a case of DCM (n=21, 46%). In total, 13% (n=6) of respondents reported that they would not be at all able to recognize a patient with DCM. Respondents most commonly reported that they were "moderately confident" in their ability to triage a patient with DCM (n=19, 41%). A quarter of respondents reported no prior introduction to DCM throughout their medical training (n=13, 25%). The mean score for knowledge-based questions was 42.6% (SD 3.96%) with the lowest performance observed in patient demographic and clinical recognition items.

CONCLUSIONS

General practitioners lack confidence in the recognition and management of DCM. These findings are consistent with the diagnostic delays previously described in the literature at the primary care level. Further work to develop and implement educational interventions to general practitioner practices is a crucial step to improving patient outcomes in DCM.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

SIGNIFICANCE

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

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.

Seizure or syncope: Is the history-based scale feasible to use in an emergency department setting?

BACKGROUND

This study aimed to assess the efficacy of a screening questionnaire, based on historical criteria, in distinguishing between seizures and syncope in patients experiencing their first episode of transient loss of consciousness (TLOC) in a neurology emergency department.

METHODS

A prospective cohort of 159 patients with initial TLOC episodes underwent clinical observation and answered a nine-question screening questionnaire. The questionnaire's predictive ability was compared to final diagnoses determined through detailed neurology, electrophysiology, and cardiology assessments during a minimum 12-month follow-up. Logistic regression (LR) analysis was performed with final diagnosis as the outcome variable. The calibration and discrimination of the models were assessed.

RESULTS

revealed that the screening score accurately classified 72.33% of patients. Among those with positive screening scores, 65 (67.71%) had seizures compared to 31 (32.29%) with syncope. Introducing a novel risk-scoring model incorporating age and gender, in addition to the screening score, significantly improved performance achieving an accurate classification rate of 81.48%. Among patients with a positive prediction, 63 (80.77%) had seizure, whereas 15 (19.23%) had syncope.

CONCLUSIONS

Employing a structured questionnaire based on common historical criteria is a valuable tool for distinguishing between seizure and syncope in the dynamic setting of the emergency department.

Current challenges in focal epilepsy treatment: An Italian Delphi consensus

BACKGROUND

Epilepsy, a globally prevalent neurological condition, presents distinct challenges in management, particularly for focal-onset types. This study aimed at addressing the current challenges and perspectives in focal epilepsy management, with focus on the Italian reality.

METHODS

Using the Delphi methodology, this research collected and analyzed the level of consensus of a panel of Italian epilepsy experts on key aspects of focal epilepsy care. Areas of focus included patient flow, treatment pathways, controlled versus uncontrolled epilepsy, follow-up protocols, and the relevance of patient-reported outcomes (PROs). This method allowed for a comprehensive assessment of consensus and divergences in clinical opinions and practices.

RESULTS

The study achieved consensus on 23 out of 26 statements, with three items failing to reach a consensus. There was strong agreement on the importance of timely intervention, individualized treatment plans, regular follow-ups at Epilepsy Centers, and the role of PROs in clinical practice. In cases of uncontrolled focal epilepsy, there was a clear inclination to pursue alternative treatment options following the failure of two previous therapies. Divergent views were evident on the inclusion of epilepsy surgery in treatment for uncontrolled epilepsy and the routine necessity of EEG evaluations in follow-ups. Other key findings included concerns about the lack of pediatric-specific research limiting current therapeutic options in this patient population, insufficient attention to the transition from pediatric to adult care, and need for improved communication. The results highlighted the complexities in managing epilepsy, with broad consensus on patient care aspects, yet notable divergences in specific treatment and management approaches.

CONCLUSION

The study offered valuable insights into the current state and complexities of managing focal-onset epilepsy. It highlighted many deficiencies in the therapeutic pathway of focal-onset epilepsy in the Italian reality, while it also underscored the importance of patient-centric care, the necessity of early and appropriate intervention, and individualized treatment approaches. The findings also called for continued research, policy development, and healthcare system improvements to enhance epilepsy management, highlighting the ongoing need for tailored healthcare solutions in this evolving field.

Consultation Requests and Satisfaction with a Telehealth Network for Epilepsy: Longitudinal Analysis of the Epilepsy Network Hessen Evaluation

Background: Telemedicine improves access to specialized medical expertise, as required for paroxysmal disorders. The Epilepsy Network Hessen Evaluation (ENHE) is a pilot cross-sectoral teleconsultation network connecting primary neurologists and pediatricians with epilepsy centers in Hessen, a federal German state. Methods: We prospectively and longitudinally evaluated telehealthcare in the ENHE. Participating physicians rated each consultation for satisfaction and impact on further management. The survey was administered at each consultation and 3 months later. Results: We analyzed 129 consultations involving 114 adult and pediatric patients. Their mean age was 34 years (standard deviation: 26, range: 0.1-91 years), 48% were female, and 34% were children and adolescents. The most common consultation requests were co-evaluation of an electroencephalogram (electroencephalogram [EEG]; 76%) and therapeutic (33%) and differential diagnosis (24%) concerns. Physicians transmitted one paraclinical examination on average (range: 1-4), predominantly EEG (85%), followed by magnetic resonance imaging (17%) and written records (9%). Response rates were 72% for the initial and 67% for the follow-up survey. Across respondents, 99% (n = 92) were satisfied with the ENHE. Overall, 80% of the consultations contributed to the diagnosis, and 90% were considered helpful for treatment, influencing it in 71% of cases. Seizure frequency had decreased more often (96%) than increased (4%) at 3 months. The initial diagnosis was confirmed in 78% of patients. Discussion: In this pilot teleconsultation network for paroxysmal disorders, diagnostic and therapeutic advice was perceived as helpful. Clinical outcomes were largely positive, suggesting tele-epileptology is viable for paroxysmal (seizure) disorders.

Personality disorders in people with epilepsy: a review

Epileptologists and psychiatrists have long observed a correlation between epilepsy and personality disorders (PDs) in their clinical practice. We conducted a comprehensive PubMed search looking for evidence on PDs in people with epilepsy (PwE). Out of over 600 results obtained without applying any time restriction, we selected only relevant studies (both analytical and descriptive) limited to English, Italian, French and Spanish languages, with a specific focus on PDs, rather than traits or symptoms, thus narrowing our search down to 23 eligible studies. PDs have been investigated in focal epilepsy (predominantly temporal lobe epilepsy - TLE), juvenile myoclonic epilepsy (JME) and psychogenic non-epileptic seizures (PNES), with heterogeneous methodology. Prevalence rates of PDs in focal epilepsy ranged from 18 to 42% in surgical candidates or post-surgical individuals, with Cluster C personality disorders or related traits and symptoms being most common. In JME, prevalence rates ranged from 8 to 23%, with no strong correlation with any specific PDs subtype. In PNES, prevalence rates ranged from 30 to 60%, with a notable association with Cluster B personality disorders, particularly borderline personality disorder. The presence of a PD in PwE, irrespective of subtype, complicates treatment management. However, substantial gaps of knowledge exist concerning the neurobiological substrate, effects of antiseizure medications and epilepsy surgery on concomitant PDs, all of which are indeed potential paths for future research.

Improving epilepsy diagnosis across the lifespan: approaches and innovations

Epilepsy diagnosis is often delayed or inaccurate, exposing people to ongoing seizures and their substantial consequences until effective treatment is initiated. Important factors contributing to this problem include delayed recognition of seizure symptoms by patients and eyewitnesses; cultural, geographical, and financial barriers to seeking health care; and missed or delayed diagnosis by health-care providers. Epilepsy diagnosis involves several steps. The first step is recognition of epileptic seizures; next is classification of epilepsy type and whether an epilepsy syndrome is present; finally, the underlying epilepsy-associated comorbidities and potential causes must be identified, which differ across the lifespan. Clinical history, elicited from patients and eyewitnesses, is a fundamental component of the diagnostic pathway. Recent technological advances, including smartphone videography and genetic testing, are increasingly used in routine practice. Innovations in technology, such as artificial intelligence, could provide new possibilities for directly and indirectly detecting epilepsy and might make valuable contributions to diagnostic algorithms in the future.

Design and validation of a diagnostic suspicion checklist to differentiate epileptic from psychogenic nonepileptic seizures (PNES-DSC)

OBJECTIVE

Psychogenic non-epileptic seizures (PNES) are complex clinical manifestations and misdiagnosis as status epilepticus remains high, entailing deleterious consequences for patients. Video-electroencephalography (vEEG) remains the gold-standard method for diagnosing PNES. However, time and economic constraints limit access to vEEG, and clinicians lack fast and reliable screening tools to assist in the differential diagnosis with epileptic seizures (ES). This study aimed to design and validate the PNES-DSC, a clinically based PNES diagnostic suspicion checklist with adequate sensitivity (Se) and specificity (Sp) to discriminate PNES from ES.

METHODS

A cross-sectional study with 125 patients (n = 104 drug-resistant epilepsy; n = 21 PNES) admitted for a vEEG protocolised study of seizures. A preliminary PNES-DSC (16-item) was designed and used by expert raters blinded to the definitive diagnosis to evaluate the seizure video recordings for each patient. Cohen's kappa coefficient, leave-one-out cross-validation (LOOCV) and balance accuracy (BAC) comprised the main validation analysis.

RESULTS

The final PNES-DSC is a 6-item checklist that requires only two to be present to confirm the suspicion of PNES. The LOOCV showed 71.4% BAC (Se = 45.2%; Sp = 97.6%) when the expert rater watched one seizure video recording and 83.4% BAC (Se = 69.6%; Sp = 97.2%) when the expert rater watched two seizure video recordings.

CONCLUSION

The PNES-DSC is a straightforward checklist with adequate psychometric properties. With an integrative approach and appropriate patient history, the PNES-DSC can assist clinicians in expediting the final diagnosis of PNES when vEEG is limited. The PNES-DSC can also be used in the absence of patients, allowing clinicians to assess seizure recordings from smartphones.

Dissociative seizures in the emergency room: room for improvement

BACKGROUND

Dissociative seizures, also known as functional or psychogenic non-epileptic seizures, account for 11%-27% of all emergency seizure presentations. Misdiagnosis as epileptic seizures is common and leads to ineffective and potentially harmful treatment escalations. We assess the potential for diagnostic improvement at different stages of emergency workup and estimate the utility of benzodiazepines.

METHODS

A retrospective study of all emergency presentations with a discharge diagnosis of acute dissociative seizures seen at a university hospital 2010-2022 was performed to assess clinical characteristics and emergency decision-making.

RESULTS

Among 156 patients (73% female, median 29 years), 15% presented more than once for a total of 203 presentations. Half of seizures were ongoing at first medical contact; prolonged seizures and clusters were common (23% and 24%). Diagnostic accuracy differed between on-site emergency physicians and emergency department neurologists (12% vs 52%). Typical features such as eye closure, discontinuous course and asynchronous movements were common. Benzodiazepines were given in two-thirds of ongoing seizures, often in high doses and preferentially for major hyperkinetic semiology. Clinical response to benzodiazepines was mixed, with a minority of patients remaining either unaffected (16%) or becoming critically sedated (13%). A quarter of patients given benzodiazepines by emergency medical services were admitted to a monitoring unit, 9% were intubated.

CONCLUSIONS

Improved semiological assessment could reduce early misdiagnosis of dissociative seizures. Although some seizures seem to respond to benzodiazepines, critical sedation is common, and further studies are needed to assess the therapeutic ratio.

Expert accuracy and inter-rater agreement of "must-know" EEG findings for adult and child neurology residents

OBJECTIVE

We published a list of "must-know" routine EEG (rEEG) findings for trainees based on expert opinion. Here, we studied the accuracy and inter-rater agreement (IRA) of these "must-know" rEEG findings among international experts.

METHODS

A previously validated online rEEG examination was disseminated to EEG experts. It consisted of a survey and 30 multiple-choice questions predicated on the previously published "must-know" rEEG findings divided into four domains: normal, abnormal, normal variants, and artifacts. Questions contained de-identified 10-20-s epochs of EEG that were considered unequivocal examples by five EEG experts.

RESULTS

The examination was completed by 258 international EEG experts. Overall mean accuracy and IRA (AC1) were 81% and substantial (0.632), respectively. The domain-specific mean accuracies and IRA were: 76%, moderate (0.558) (normal); 78%, moderate (0.575) (abnormal); 85%, substantial (0.678) (normal variants); 85%, substantial (0.740) (artifacts). Academic experts had a higher accuracy than private practice experts (82% vs. 77%; p = .035). Country-specific overall mean accuracies and IRA were: 92%, almost perfect (0.836) (U.S.); 86%, substantial (0.762) (Brazil); 79%, substantial (0.646) (Italy); and 72%, moderate (0.496) (India). In conclusion, collective expert accuracy and IRA of "must-know" rEEG findings are suboptimal and heterogeneous.

SIGNIFICANCE

We recommend the development and implementation of pragmatic, accessible, country-specific ways to measure and improve the expert accuracy and IRA.

The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives

Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.

A Case of Focal Seizures Presented With Recurrent Sweating and Chills

Focal seizures, characterized by excessive electrical excitation in a brain region, present diagnostic challenges due to diverse manifestations, particularly with non-motor symptoms. Here, we present a 69-year-old Japanese woman experiencing unexplained recurrent episodes of sweating, chills, and shivering. Despite exhaustive investigations that identified no abnormalities, her symptoms remained unalleviated by symptomatic treatments. The episodic nature of her presentations subsequently prompted a clinical suspicion of seizures, leading to further neurological evaluations. Magnetic resonance imaging (MRI) of the brain and electroencephalography (EEG) revealed chronic ischemic changes in the cerebral white matter and intermittent sharp and slow wave bursts in the frontal regions. These findings led to a diagnosis of focal seizures manifesting as autonomic symptoms. The patient's symptoms were successfully treated with carbamazepine. This case illustrates the importance of considering non-motor focal seizures in patients with episodic symptoms, even when routine tests show no abnormalities.

Models of care and relevance of territorial management in assisting persons with epilepsy

Epilepsy is a widespread social disease that affects people of all ages and often involves both diagnostic and therapeutic difficulties. Beyond seizure control, it is necessary to ensure people with epilepsy a good quality of life and respect for human rights, seeking to increase self-management capacity and break down stigma. People with epilepsy should have privileged access to specialized epilepsy centers, where multidisciplinary care is possible. These centers, organized by different levels of complexity, should be uniformly distributed throughout the country and networked together. The scientific community and health care organizations must therefore design all necessary strategies so that knowledge about epilepsy improves among the general population and the most effective pathways of care are effectively implemented.

Education Research: Competency-Based EEG Education: An Online Routine EEG Examination for Adult and Child Neurology Residents

Background and Objectives

We recently published expert consensus-based curricular objectives for routine EEG (rEEG) interpretation for adult and child neurology residents. In this study, we used this curriculum framework to develop and validate an online, competency-based, formative and summative rEEG examination for neurology residents.

Methods

We developed an online rEEG examination consisting of a brief survey and 30 multiple-choice questions covering EEG learning objectives for neurology residents in 4 domains: normal, abnormal, normal variants, and artifacts. Each question contained a deidentified EEG image, displayed in 2 montages (bipolar and average), reviewed and optimized by the authors to address the learning objectives. Respondents reported their level of confidence (LOC, 5-point Likert scale) with identifying 4 categories of EEG findings independently: states of wakefulness/sleep, sleep structures, normal variants, and artifacts. Accuracy and item discrimination were calculated for each question and LOC for each category. The test was disseminated by the International League Against Epilepsy and shared on social media.

Results

Of 2,080 responses, 922 were complete. Respondents comprised clinical neurophysiologists/experts (n = 41), EEG/epilepsy clinical fellows (n = 211), EEG technologists (n = 128), attending neurologists (n = 111), adult neurology residents (n = 227), child neurology residents (n = 108), medical students (n = 24), attending non-neurologists (n = 18), and others (n = 54). Mean overall scores (95% CI) were 82% (77-86) (clinical neurophysiologists), 81% (79-83) (clinical fellows), and 72% (70-73) (adult and child neurology residents). Experts were more confident than clinical fellows in all categories but sleep structures. Experts and clinical fellows were more confident than residents in all 4 categories. Among residents, accuracy and LOC increased as a function of prior EEG weeks of training. Accuracy improved from 67% (baseline/no prior EEG training) to 77% (>12 prior EEG weeks). More than 8 weeks of EEG training was needed to reach accuracy comparable with clinical neurophysiologists on this rEEG examination. Increase in LOC was slower and less robust than increase in accuracy. All but 3 questions had a high discrimination index (>0.25).

Discussion

This online, competency-based rEEG examination, mapped to a published EEG curriculum, has excellent psychometrics and differentiates experienced EEG readers from adult and child neurology residents. This online tool has the potential to improve resident EEG education worldwide.

Epilepsy classification using artificial intelligence: A web-based application

OBJECTIVE

The purpose of the current endeavor was to evaluate the feasibility of using easily accessible and applicable clinical information (based on history taking and physical examination) in order to make a reliable differentiation between idiopathic generalized epilepsy (IGE) versus focal epilepsy using machine learning (ML) methods.

METHODS

The first phase of the study was a retrospective study of a prospectively developed and maintained database. All patients with an electro-clinical diagnosis of IGE or focal epilepsy, at the outpatient epilepsy clinic at Shiraz University of Medical Sciences, Shiraz, Iran, from 2008 until 2022, were included. The first author selected a set of clinical features. Using the stratified random portioning method, the dataset was divided into the train (70%) and test (30%) subsets. Different types of classifiers were assessed and the final classification was made based on their best results using the stacking method.

RESULTS

A total number of 1445 patients were studied; 964 with focal epilepsy and 481 with IGE. The stacking classifier led to better results than the base classifiers in general. This algorithm has the following characteristics: precision: 0.81, sensitivity: 0.81, and specificity: 0.77.

SIGNIFICANCE

We developed a pragmatic algorithm aimed at facilitating epilepsy classification for individuals whose epilepsy begins at age 10 years and older. Also, in order to enable and facilitate future external validation studies by other peers and professionals, the developed and trained ML model was implemented and published via an online web-based application that is freely available at http://www.epiclass.ir/f-ige.

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.

Mapping of Neuro-Cardiac Electrophysiology: Interlinking Epilepsy and Arrhythmia

The interplay between neurology and cardiology has gained significant attention in recent years, particularly regarding the shared pathophysiological mechanisms and clinical comorbidities observed in epilepsy and arrhythmias. Neuro-cardiac electrophysiology mapping involves the comprehensive assessment of both neural and cardiac electrical activity, aiming to unravel the intricate connections and potential cross-talk between the brain and the heart. The emergence of artificial intelligence (AI) has revolutionized the field by enabling the analysis of large-scale data sets, complex signal processing, and predictive modeling. AI algorithms have been applied to neuroimaging, electroencephalography (EEG), electrocardiography (ECG), and other diagnostic modalities to identify subtle patterns, classify disease subtypes, predict outcomes, and guide personalized treatment strategies. In this review, we highlight the potential clinical implications of neuro-cardiac mapping and AI in the management of epilepsy and arrhythmias. We address the challenges and limitations associated with these approaches, including data quality, interpretability, and ethical considerations. Further research and collaboration between neurologists, cardiologists, and AI experts are needed to fully unlock the potential of this interdisciplinary field.

Estimating Patient-Level Uncertainty in Seizure Detection Using Group-Specific Out-of-Distribution Detection Technique

Epilepsy is a chronic neurological disorder affecting around 1% of the global population, characterized by recurrent epileptic seizures. Accurate diagnosis and treatment are crucial for reducing mortality rates. Recent advancements in machine learning (ML) algorithms have shown potential in aiding clinicians with seizure detection in electroencephalography (EEG) data. However, these algorithms face significant challenges due to the patient-specific variability in seizure patterns and the limited availability of high-quality EEG data for training, causing erratic predictions. These erratic predictions are harmful, especially for high-stake domains in healthcare, negatively affecting patients. Therefore, ensuring safety in AI is of the utmost importance. In this study, we propose a novel ensemble method for uncertainty quantification to identify patients with low-confidence predictions in ML-based seizure detection algorithms. Our approach aims to mitigate high-risk predictions in previously unseen seizure patients, thereby enhancing the robustness of existing seizure detection algorithms. Additionally, our method can be implemented with most of the deep learning (DL) models. We evaluated the proposed method against established uncertainty detection techniques, demonstrating its effectiveness in identifying patients for whom the model's predictions are less certain. Our proposed method managed to achieve 87%, 89% and 75% in accuracy, specificity and sensitivity, respectively. This study represents a novel attempt to improve the reliability and robustness of DL algorithms in the domain of seizure detection. This study underscores the value of integrating uncertainty quantification into ML algorithms for seizure detection, offering clinicians a practical tool to gauge the applicability of ML models for individual patients.

"Resistance Is Futile": A Pilot Study into Pseudoresistance in Canine Epilepsy

Epilepsy is a common neurological disorder in veterinary practice, complicated by frequent occurrence of medication-resistant epilepsy. In human medicine, it has been noted that some patients with medication-resistant epilepsy have in fact other reasons for their apparent medication-resistance. The aim of this retrospective study was to describe the issue of pseudoresistance using as an example a population of dogs presented with presumed medication-resistant epilepsy and provide an in-depth review of what is known in human medicine about pseudoresistant epilepsy. One-hundred fifty-two cases were identified with medication-resistant epilepsy, of which 73% had true medication-resistant epilepsy and 27% patients had pseudoresistance. Low serum anti-seizure medication levels were the most common cause of pseudoresistance, present in almost half of the cases (42%), followed by inadequate choice of drugs or dosages (22%), misclassification (22%) or misdiagnosis (9%) of epilepsy and poor compliance (9%). All cases of pseudoresistance, except for one, responded to a modification of the initial therapy protocol. Pseudoresistance can bias clinical trials, misinform the clinical decision-making process, delay diagnosis and treatment, and misinform owners about their pets' prognosis. A substantial proportion of these cases can have improvement of their seizure frequency or achieve seizure freedom upon modification of their therapeutic protocol.

Brain cell-specific origin of circulating microRNA biomarkers in experimental temporal lobe epilepsy

The diagnosis of epilepsy is complex and challenging and would benefit from the availability of molecular biomarkers, ideally measurable in a biofluid such as blood. Experimental and human epilepsy are associated with altered brain and blood levels of various microRNAs (miRNAs). Evidence is lacking, however, as to whether any of the circulating pool of miRNAs originates from the brain. To explore the link between circulating miRNAs and the pathophysiology of epilepsy, we first sequenced argonaute 2 (Ago2)-bound miRNAs in plasma samples collected from mice subject to status epilepticus induced by intraamygdala microinjection of kainic acid. This identified time-dependent changes in plasma levels of miRNAs with known neuronal and microglial-cell origins. To explore whether the circulating miRNAs had originated from the brain, we generated mice expressing FLAG-Ago2 in neurons or microglia using tamoxifen-inducible Thy1 or Cx3cr1 promoters, respectively. FLAG immunoprecipitates from the plasma of these mice after seizures contained miRNAs, including let-7i-5p and miR-19b-3p. Taken together, these studies confirm that a portion of the circulating pool of miRNAs in experimental epilepsy originates from the brain, increasing support for miRNAs as mechanistic biomarkers of epilepsy.

Psychiatric and psychological assessment of patients with drug-resistant epilepsy and psychogenic nonepileptic seizures (PNES) with no response to previous treatments

OBJECTIVE

Psychogenic nonepileptic seizures (PNES) are common imitators of epileptic seizures. Refractoriness to antiseizure medication hinders the differential diagnosis between ES and PNES, carrying deleterious consequences in patients with PNES. Psychiatric and psychological characteristics may assist in the differential diagnosis between drug-resistant epilepsy (DRE) and PNES. Nevertheless, current comprehensive psychiatric and psychological descriptive studies on both patient groups are scarce and with several study limitations. This study provides a comprehensive psychiatric and psychological characterization of Spanish patients with DRE and PNES.

METHOD

A cross-sectional and comparative study was completed with 104 patients with DRE and 21 with PNES. Psychiatric and psychological characteristics were assessed with the HADS, SCL-90-R, NEO-FFI-R, PDQ-4+, COPE, and QOLIE-31 tests. Parametric and non-parametric tests were used, and regression models were fit to further explore factors affecting patients' life quality.

RESULTS

Patients with PNES had greater levels of somatization and extraversion and were associated with benzodiazepine intake. Patients with DRE showed greater narcissistic personality disorder symptoms than those with PNES. In patients with DRE, difficulty in performing basic needs-related tasks and greater psychological distress severity and seizure frequency were associated with poorer life quality. In contrast, being a woman, having a psychiatric disorder history, and greater psychiatric symptoms' intensity were associated with poorer life quality in patients with PNES.

CONCLUSION

Patients with DRE and PNES share similar psychiatric and psychological characteristics, with only very few being significantly different.

Wearable Electromyography Classification of Epileptic Seizures: A Feasibility Study

Accurate diagnosis and classification of epileptic seizures can greatly support patient treatments. As many epileptic seizures are convulsive and have a motor component, the analysis of muscle activity can provide valuable information for seizure classification. Therefore, this paper present a feasibility study conducted on healthy volunteers, focusing on tracking epileptic seizures movements using surface electromyography signals (sEMG) measured on human limb muscles. For the experimental studies, first, compact wireless sensor nodes were developed for real-time measurement of sEMG on the gastrocnemius, flexor carpi ulnaris, biceps brachii, and quadriceps muscles on the right side and the left side. For the classification of the seizure, a machine learning model has been elaborated. The 16 common sEMG time-domain features were first extracted and examined with respect to discrimination and redundancy. This allowed the features to be classified into irrelevant features, important features, and redundant features. Redundant features were examined with the Big-O notation method and with the average execution time method to select the feature that leads to lower complexity and reduced processing time. The finally selected six features were explored using different machine learning classifiers to compare the resulting classification accuracy. The results show that the artificial neural network (ANN) model with the six features: IEMG, WAMP, MYOP, SE, SKEW, and WL, had the highest classification accuracy (99.95%). A further study confirms that all the chosen eight sensors are necessary to reach this high classification accuracy.

Seizure detection with reduced electroencephalogram channels: research trends and outlook

Epilepsy is a prevalent condition characterized by recurrent, unpredictable seizures. Monitoring with surface electroencephalography (EEG) is the gold standard for diagnosing epilepsy, but a time-consuming, uncomfortable and sometimes ineffective process for patients. Further, using EEG over a brief monitoring period has variable success, dependent on patient tolerance and seizure frequency. The availability of hospital resources and hardware and software specifications inherently restrict the options for comfortable, long-term data collection, resulting in limited data for training machine-learning models. This mini-review examines the current patient journey, providing an overview of the current state of EEG monitoring with reduced electrodes and automated channel reduction methods. Opportunities for improving data reliability through multi-modal data fusion are suggested. We assert the need for further research in electrode reduction to advance brain monitoring solutions towards portable, reliable devices that simultaneously offer patient comfort, perform ultra-long-term monitoring and expedite the diagnosis process.

Epilepsy diagnosis using a clinical decision tool and artificially intelligent electroencephalography

OBJECTIVE

To construct a tool for non-experts to calculate the probability of epilepsy based on easily obtained clinical information combined with an artificial intelligence readout of the electroencephalogram (AI-EEG).

MATERIALS AND METHODS

We performed a chart review of 205 consecutive patients aged 18 years or older who underwent routine EEG. We created a point system to calculate the pre-EEG probability of epilepsy in a pilot study cohort. We also computed a post-test probability based on AI-EEG results.

RESULTS

One hundred and four (50.7%) patients were female, the mean age was 46 years, and 110 (53.7%) were diagnosed with epilepsy. Findings favoring epilepsy included developmental delay (12.6% vs 1.1%), prior neurological injury (51.4% vs 30.9%), childhood febrile seizures (4.6% vs 0.0%), postictal confusion (43.6% vs 20.0%), and witnessed convulsions (63.6% vs 21.1%); findings favoring alternative diagnoses were lightheadedness (3.6% vs 15.8%) or onset after prolonged sitting or standing (0.9% vs 7.4%). The final point system included 6 predictors: Presyncope (-3 points), cardiac history (-1), convulsion or forced head turn (+3), neurological disease history (+2), multiple prior spells (+1), postictal confusion (+2). Total scores of ≤1 point predicted <5% probability of epilepsy, while cumulative scores ≥7 predicted >95%. The model showed excellent discrimination (AUROC: 0.86). A positive AI-EEG substantially increases the probability of epilepsy. The impact is greatest when the pre-EEG probability is near 30%.

SIGNIFICANCE

A decision tool using a small number of historical clinical features accurately predicts the probability of epilepsy. In indeterminate cases, AI-assisted EEG helps resolve uncertainty. This tool holds promise for use by healthcare workers without specialty epilepsy training if validated in an independent cohort.

Benefits of the Neurogenic Potential of Melatonin for Treating Neurological and Neuropsychiatric Disorders

There are several neurological diseases under which processes related to adult brain neurogenesis, such cell proliferation, neural differentiation and neuronal maturation, are affected. Melatonin can exert a relevant benefit for treating neurological disorders, given its well-known antioxidant and anti-inflammatory properties as well as its pro-survival effects. In addition, melatonin is able to modulate cell proliferation and neural differentiation processes in neural stem/progenitor cells while improving neuronal maturation of neural precursor cells and newly created postmitotic neurons. Thus, melatonin shows relevant pro-neurogenic properties that may have benefits for neurological conditions associated with impairments in adult brain neurogenesis. For instance, the anti-aging properties of melatonin seem to be linked to its neurogenic properties. Modulation of neurogenesis by melatonin is beneficial under conditions of stress, anxiety and depression as well as for the ischemic brain or after a brain stroke. Pro-neurogenic actions of melatonin may also be beneficial for treating dementias, after a traumatic brain injury, and under conditions of epilepsy, schizophrenia and amyotrophic lateral sclerosis. Melatonin may represent a pro-neurogenic treatment effective for retarding the progression of neuropathology associated with Down syndrome. Finally, more studies are necessary to elucidate the benefits of melatonin treatments under brain disorders related to impairments in glucose and insulin homeostasis.

Optimism and pessimism as predictors of seizure group among patients with intractable seizure disorders

The purpose of this study was to investigate the validity of the Revised Optimism-Pessimism Scale (PSM-R) as a measure of attributional style, and the incremental utility of optimism and pessimism as predictors of seizure group, in an intractable seizure disorder sample. Participants included adult patients with epileptic seizures (ES; n = 151) and psychogenic nonepileptic seizures (PNES; n = 173) whose diagnoses were confirmed by prolonged video/EEG monitoring (PVEM). Optimism and pessimism scores were computed from abbreviated versions of the MMPI for all participants. Analyses were conducted to examine the relationships between optimism, pessimism, and MMPI clinical scale scores. Logistic regression analyses were conducted to generate a model for the prediction of seizure group. Results supported the validity of the PSM-R as a measure of attributional style in an intractable seizure disorder sample. Both optimism and pessimism provided significant incremental predictive utility over and above other predictors of seizure group. There are advantages of using the proposed prediction model over other alternative differential diagnostic procedures, including lower cost, greater availability, and increased standardization. Overall, results indicated that attributional style is a clinically relevant index of personality and cognitive response to stress among an intractable seizure disorder sample.

Seizures in the Emergency Department: clinical and diagnostic data from a series of 153 patients

INTRODUCTION

Seizures are a frequent reason for admission to emergency departments and require early, precise diagnosis and treatment. The objective of this study was to describe the clinical and prognostic characteristics of a series of patients with seizures attended at our hospital's emergency department.

METHODS

We performed a retrospective, observational study of all patients with seizures who were admitted to our hospital's emergency department and attended by the on-call neurology service between February and August 2017.

RESULTS

We included 153 patients, representing 9.9% of all neurological emergency department admissions. The median age was 58 years, 52.3% of patients were women, and 51% had history of epilepsy. Onset was focal in 82.4% of cases, and the most frequent aetiology was cerebrovascular disease (24.2%). Twelve patients (7.8%) developed status epilepticus, which was associated with higher scores on the ADAN scale (P < .001) and with history of refractory epilepsy (P = .002). The in-hospital mortality rate was 3.7%, and in-hospital mortality was associated with older age (P = .049) and status epilepticus (P = .018). Eighty percent of patients with no history of epilepsy were diagnosed with epilepsy at the emergency department; all started treatment. The kappa coefficient for epilepsy diagnosis in the emergency department compared to diagnosis after one year of follow-up by the epilepsy unit was 0.45 (diagnosis was modified in 20% of patients).

CONCLUSIONS

Seizures are a frequent neurological emergency with potential complications and considerable morbidity and mortality rates. In patients with no known history of epilepsy, the condition may be diagnosed in the emergency department, but follow-up at specialised epilepsy units is recommended.

Electroencephalogram utilization and psychiatric comorbidities among children and adolescents with epilepsy in rural Southwestern Uganda

OBJECTIVE

This study aimed at describing routine electroencephalogram (EEG) findings among children and adolescents with a clinical diagnosis of epilepsy and determines how interictal EEG abnormalities vary with the psychiatric comorbidities.

METHODS

We conducted a cross-sectional study among children and adolescents with epilepsy aged 5-18 years receiving care from a regional referral hospital in Southwestern Uganda. Psychiatric comorbidities were assessed using an adapted parent version of Child and Adolescent Symptom Inventory-5. Thirty-minute EEG samples were taken from routine EEG recordings that were locally performed and remotely interpreted for all participants.

RESULTS

Of the 140 participants, 71 (50.7%) had normal EEG findings and 51 (36.4%) had epileptiform abnormalities while 18 (12.9%) had non-epileptiform. Of those who had epileptiform abnormalities on EEG, 23 (45.1%) were focal, 26 (51.0%) were generalized, and 2 (3.9%) were focal with bilateral spread. There was no significant association between the different psychiatric comorbidities and the interictal EEG abnormalities.

CONCLUSIONS

Among children and adolescents with a clinical diagnosis of epilepsy in Southwestern Uganda, only 36% showed epileptiform abnormalities on their EEG recordings. There was no association between the interictal EEG abnormalities and psychiatric comorbidities.

The utility of mobile telephone-recorded videos as adjuncts to the diagnosis of seizures and paroxysmal events in children with suspected epileptic seizures

BACKGROUND

Epilepsy is often diagnosed through clinical description, but inter-observer interpretations can be diverse and misleading.

OBJECTIVE

To assess the utility of smartphone videos in the diagnosis of paediatric epilepsy.

METHODS

The literature was reviewed for evidence to support the use of smartphone videos, inclusive of advantages, ethical practice and potential disadvantages. An existing adult-based quality of video (QOV) scoring tool was adapted for use in children. A pilot study used convenience sampling of videos from 25 patients, which were reviewed to assess the viability of the adapted QOV tool against the subsequent diagnosis for the patients with videos. The referral mechanism of the videos was reviewed for the source and consent processes followed.

RESULTS

A total of 14 studies were identified. Methodologies varied; only three focused on videos of children, and QOV was formally scored in three. Studies found that smartphone videos of good quality assisted the differentiation of epilepsy from non-epileptic events, especially with accompanying history and with more experienced clinicians. The ethics and risks of circulation of smartphone videos were briefly considered in a minority of the reports. The pilot study found that the adapted QOV tool correlated with videos of moderate and high quality and subsequent diagnostic closure.

CONCLUSIONS

Data relating to the role of smartphone video of events in children is lacking, especially from low- and middle-income settings. Guidelines for caregivers to acquire good-quality videos are not part of routine practice. The ethical implications of transfer of sensitive material have not been adequately addressed for this group. Prospective multicentre studies are needed to formally assess the viability of the adapted QOV tool for paediatric videos.

EEG biomarker candidates for the identification of epilepsy

Electroencephalography (EEG) is one of the main pillars used for the diagnosis and study of epilepsy, readily employed after a possible first seizure has occurred. The most established biomarker of epilepsy, in case seizures are not recorded, are interictal epileptiform discharges (IEDs). In clinical practice, however, IEDs are not always present and the EEG may appear completely normal despite an underlying epileptic disorder, often leading to difficulties in the diagnosis of the disease. Thus, finding other biomarkers that reliably predict whether an individual suffers from epilepsy even in the absence of evident epileptic activity would be extremely helpful, since they could allow shortening the period of diagnostic uncertainty and consequently decreasing the risk of seizure. To date only a few EEG features other than IEDs seem to be promising candidates able to distinguish between epilepsy, i.e. > 60 % risk of recurrent seizures, or other (pathological) conditions. The aim of this narrative review is to provide an overview of the EEG-based biomarker candidates for epilepsy and the techniques employed for their identification.

Degenerative cervical myelopathy education in UK medical schools: a national cross-sectional survey of medical students

INTRODUCTION

Degenerative cervical myelopathy (DCM) is a common and progressive neurological condition caused by injury of the cervical spinal cord by degenerative spinal pathology. Delayed diagnosis leading to avoidable and irreversible disability is a major current problem limiting patient outcomes. Lack of sufficient representation of DCM in undergraduate and postgraduate medical curricula may contribute to poor recognition of DCM by non-specialist doctors. The objective of this study was to assess the DCM teaching provision in UK medical schools and the DCM knowledge of UK medical students.

METHODS

UK medical students completed a web-based survey distributed nationally through university social media pages, university email bulletins and the national student network of Myelopathy.org. The survey comprised a 19-item questionnaire capturing data on student demographics, myelopathy teaching and myelopathy knowledge. Advertisements were repeated monthly over a 12-month recruitment period and participation was incentivised by entry into an Amazon voucher prize draw. Ethical approval for the study was granted by the Psychology Research Ethics Committee, University of Cambridge (PRE.2018.099).

RESULTS

A total of 751 medical students from 32 British medical schools completed the survey. Medical students from all year groups participated. Most students (520; 72%) had not received any medical school teaching about DCM. When students had received DCM teaching, the duration of teaching was minimal (75% < 1 h). A total of 350 students (47%) reported conducting private study on DCM. Modal student self-rating of their own knowledge of DCM was 'terrible' (356; 47%). There was no correlation between a student's subjective rating of their knowledge and their answers to objective questions. A total of 723 (96%) of students expressed interest in learning more about DCM, with lectures the preferred format.

CONCLUSIONS

DCM appears to be a neglected condition in medical education which has implications for clinical practice. However, student enthusiasm to undertake private study suggests future teaching interventions will be well-received. Future work is necessary to characterise the format of DCM teaching that is most effective and to subsequently measure how educational interventions translate into clinical benefits.

Teaching distinguishing semiological features improves diagnostic accuracy of seizure-like events by emergency physicians

Background

Misdiagnosis of seizure-like events (SLE) in emergency situations is common. Here, we evaluate whether a single, video-based lesson highlighting distinguishing semiological features can improve the diagnostic accuracy of emergency physicians for epileptic seizures (ES), psychogenic non-epileptic seizures (PNES) and syncopes (SY).

Methods

40 emergency physicians (24 anesthetists, nine surgeons and seven internal medicine specialists by primary specialty) participated in a prospective trial on the diagnostic accuracy of SLE. They assessed video-displayed SLE at two time points: before and after a lecture on distinguishing semiological features. In the lecture, semiological features were demonstrated using patient videos, some were acted by the instructor in addition. The increase in correct diagnoses and recognition of distinguishing semiological features were analyzed.

Results

Before the lesson, 45% of 200 SLE-ratings were correct: 15% of SY (n = 40), 30% of PNES (n = 40), 59% of ES (n = 120, focal to bilateral tonic–clonic seizures (FBTCS) 87.5% (n = 40), focal impaired aware seizures (FIAS) 45% (n = 80)). Semiology teaching increased both the rate of correct diagnoses of SLE to overall 79% (p < 0.001) (ES 91% (p < 0.001), FBCTS 98% (n.s.), FIAS 88% (p < 0.001), PNES 88% (p < 0.001), SY 35% (p < 0.001)), and the number of recognized distinguishing semiological features. We identified several semiological features with high entity specific positive predictive values (> 0.8).

Conclusions

A single 45-min video-based lesson highlighting distinguishing semiological features improves the diagnostic accuracy of ES, PNES and SY by emergency physicians. We expect that including this aspect into the curriculum of emergency physicians will lead to better individual patient treatment in pre-hospital medicine and more appropriate subsequent use of clinical resources.

Diagnosis of Epilepsy with Functional Connectivity in EEG after a Suspected First Seizure

Epilepsy is regarded as a structural and functional network disorder, affecting around 50 million people worldwide. A correct disease diagnosis can lead to quicker medical action, preventing adverse effects. This paper reports the design of a classifier for epilepsy diagnosis in patients after a first ictal episode, using electroencephalogram (EEG) recordings. The dataset consists of resting-state EEG from 629 patients, of which 504 were retained for the study. The patient's cohort exists out of 291 patients with epilepsy and 213 patients with other pathologies. The data were split into two sets: 80% training set and 20% test set. The extracted features from EEG included functional connectivity measures, graph measures, band powers and brain asymmetry ratios. Feature reduction was performed, and the models were trained using Machine Learning (ML) techniques. The models' evaluation was performed with the area under the receiver operating characteristic curve (AUC). When focusing specifically on focal lesional epileptic patients, better results were obtained. This classification task was optimized using a 5-fold cross-validation, where SVM using PCA for feature reduction achieved an AUC of 0.730 ± 0.030. In the test set, the same model achieved 0.649 of AUC. The verified decrease is justified by the considerable diversity of pathologies in the cohort. An analysis of the selected features across tested models shows that functional connectivity and its graph measures have the most considerable predictive power, along with full-spectrum frequency-based features. To conclude, the proposed algorithms, with some refinement, can be of added value for doctors diagnosing epilepsy from EEG recordings after a suspected first seizure.

Epilepsy, intellectual disability and the epilepsy care pathway: improving outcomes

The strong, life-long association between epilepsy and intellectual disability means that psychiatric teams, and the services they exist in, have a need for significant competencies in the field of epilepsy. This article addresses these competencies through the pathway of care. It will focus on those areas most relevant to psychiatric care and, when possible, explore where technology has begun to influence practice. The pathway leads from diagnosis through, in some cases, to mortality and support of the bereaved in psychiatric care. We will approach the topic through showing how the intertwining themes of information, empowerment, access to care, assessment of risk and psychological support are important. Technological advances are supporting changes in most of these areas, and psychological support, a knowledge of the needs of people with epilepsy and intellectual disability and epilepsy skills remain the foundation in the application of these advances.

Blood biomarkers in epilepsy

Robust and accessible biomarkers are greatly needed in epilepsy. Diagnostic and prognostic precision in the clinic needs to improve, and there is a need for objective quantification of seizure burden. In recent years, there have been advances in the development of accessible and cost-effective blood-based biomarkers in neurology, and these are increasingly studied in epilepsy. However, the field is in its infancy and specificity and sensitivity for most biomarkers in most clinical situations are not known. This review describes advancements regarding human blood biomarkers in epilepsy. Examples of biochemical markers that have been shown to have higher blood concentrations in study subjects with epilepsy include brain proteins like S100B or neuronal specific enolase, and neuroinflammatory proteins like interleukins, and tumor necrosis factor-alpha. Some of the blood biomarkers also seem to reflect seizure duration or frequency, and levels decrease in response to treatment with antiseizure medication. For most biomarkers, the literature contains seemingly conflicting results. This is to be expected in an emerging field and could reflect different study populations, sampling or analysis techniques, and epilepsy classification. More studies are needed with emphasis put on the classification of epilepsy and seizure types. More standardized reporting could perhaps decrease result heterogeneity and increase the potential for data sharing and subgroup analyses.

Use of Health-Related Apps and Telehealth in Adults with Epilepsy in Germany: A Multicenter Cohort Study

Background: Telehealth can improve the treatment of chronic disorders, such as epilepsy. Telehealth prevalence and use increased during the coronavirus disease 2019 (COVID-19) pandemic. However, familiarity with and use of telehealth and health-related mobile applications (apps) by persons with epilepsy remain unknown. Methods: We investigated telehealth use, demographics, and clinical variables within the multicenter Epi2020 cross-sectional study. Between October and December 2020, adults with epilepsy completed a validated questionnaire, including individual questions regarding knowledge and use of apps and telehealth. Results: Of 476 included individuals (58.2% women; mean age 40.2 ± 15.4 years), 41.6% reported using health-related apps. Health apps were used more frequently (pedometer 32.1%, exercise app 17.6%) than medical apps (health insurance 15.1%, menstrual apps 12.2%) or apps designed for epilepsy (medication reminders 10.3%, seizure calendars 4.6%). Few used seizure detectors (i.e., apps as medical devices 1.9%) or mobile health devices (fitness bracelet 11.3%). A majority (60.9%) had heard the term telehealth, 78.6% of whom had a positive view. However, only 28.6% had a concrete idea of telehealth, and only 16.6% reported personal experience with telehealth. A majority (55%) would attend a teleconsultation follow-up, and 41.2% would in a medical emergency. Data privacy and availability were considered equally important by 50.8%, 21.8% considered data privacy more important, and 20.2% considered data availability more important. Current health-related app use was independently associated with younger age (p = 0.003), higher education (p < 0.001), and subjective COVID-19-related challenges (p = 0.002). Persistent seizure occurrence (vs. seizure freedom ≥12 months) did not affect willingness to use teleconsultations on multivariable logistic regression analysis. Conclusions: Despite positive telehealth views, few persons with epilepsy in Germany are familiar with specific apps or services. Socioeconomic factors influence telehealth use more than baseline epilepsy characteristics. Telehealth education and services should target socioeconomically disadvantaged individuals to reduce the digital care gap. German Clinical Trials Register (DRKS00022024; Universal Trial Number: U1111-1252-5331).

MicroRNAs miR-629-3p, miR-1202 and miR-1225-5p as potential diagnostic and surgery outcome biomarkers for mesial temporal lobe epilepsy with hippocampal sclerosis

BACKGROUND

Mesial temporal lobe epilepsy (MTLE) is a symptomatic epilepsy syndrome clinically characterized by high prevalence, pharmacoresistance, good surgical prognosis and hippocampal sclerosis (HS); however, no singular criteria can be considered sufficient for the MTLE-HS diagnosis. MicroRNAs (miRNAs) are small non-coding molecules that act as important gene-expression regulators at post-transcriptional level. Evidences on the involvement of miRNAs in epilepsy pathogenesis as well as their potential to be employed as biomarkers claim for investigations on miRNAs' applicability as epilepsy diagnosis and prognosis biomarkers. Consequently, the present study aimed to evaluate the applicability of three specific miRNAs as biomarkers of diagnosis and surgical outcomes in adult patients with MTLE-HS.

METHOD

Hippocampus, amygdala and blood samples from 20 patients with MTLE-HS were analyzed, 10 with favorable surgical prognosis (Engel I) and 10 with unfavorable surgical prognosis (Engel III-IV). For the control groups, hippocampus and amygdala from necropsy and blood samples from healthy individuals were adopted. The miRNAs expression analysis was performed using Real-Time Quantitative Polymerase Chain Reaction for miRNAs highlighted from microarray as being involved in GABAergic neurotransmission.

RESULTS

The miRNAs miR-629-3p, miR-1202 and miR-1225-5p were found to be hyperexpressed in MTLE-HS patients' blood.

CONCLUSIONS

Our data suggest the existence of three circulating miRNAs (miR-629-3p, miR-1202 and miR-1225-5p) that could possibly act as additional tools in the set of factors that contribute to MTLE-HS diagnose.

Paroxysmal Nonepileptic Events in Children: A Video Gallery and a Guide for Differential Diagnosis

Purpose

of review Paroxysmal Non-Epileptic Events (PNEEs) are a heterogeneous group of time-limited events, characterized by changes in motor or behavioral activity beginning abruptly and ending in a short time. Due to their manifestation, these conditions can clinically simulate seizures.

Recent findings:

These episodes belong to different categories including syncopal events, psychiatric disorders, movement disorders, and many others. PNEEs are a common cause of diagnostic mistakes and families’ concerns and the risk of useless and sometimes even injurious treatment is considerable. The high frequency of these manifestations in clinical practice makes PNEEs a diagnostic challenge for clinicians.

Learning from the comorbidities of epilepsy

PURPOSE OF REVIEW

Comorbidities are a common feature in epilepsy, but neither the entire spectrum nor the significance of such comorbidities has been fully explored. We review comorbidities associated with epilepsy and their associated burden, provide an overview of relationships, and discuss a new conceptualization of the comorbidities.

RECENT FINDINGS

The epidemiology of the comorbidities of epilepsy and effects on health outcomes, healthcare use, and healthcare expenditures have been partly delineated. Distinct mechanisms of the associations have been suggested but not entirely ascertained. Movement from conceptualizing epilepsy as a condition to a symptom-complex has occurred.

SUMMARY

Comorbidities are common among people with epilepsy and are associated with poorer clinical outcomes and quality of life, greater use of health resources, and increased expenditure. Becoming aware of the associated mechanisms and their uncertainty is central to understanding the relationships between epilepsy and comorbid health conditions, which have implications for diagnosis and screening, medical management, and surgical management. Conceptualizing comorbidities of epilepsy as precipitating factors and epilepsy as the symptom will improve the understanding of epilepsy and catalyze research and improvements in clinical practice.

A multimodal AI system for out-of-distribution generalization of seizure identification

Artificial intelligence (AI) and health sensory data-fusion hold the potential to automate many laborious and time-consuming processes in hospitals or ambulatory settings, e.g. home monitoring and telehealth. One such unmet challenge is rapid and accurate epileptic seizure annotation. An accurate and automatic approach can provide an alternative way to label seizures in epilepsy or deliver a substitute for inaccurate patient self-reports. Multimodal sensory fusion is believed to provide an avenue to improve the performance of AI systems in seizure identification. We propose a state-of-the-art performing AI system that combines electroencephalogram (EEG) and electrocardiogram (ECG) for seizure identification, tested on clinical data with early evidence demonstrating generalization across hospitals. The model was trained and validated on the publicly available Temple University Hospital (TUH) dataset. To evaluate performance in a clinical setting, we conducted non-patient-specific pseudo-prospective inference tests on three out-of-distribution datasets, including EPILEPSIAE (30 patients) and the Royal Prince Alfred Hospital (RPAH) in Sydney, Australia (31 neurologists-shortlisted patients and 30 randomly selected). Our multimodal approach improves the area under the receiver operating characteristic curve (AUC-ROC) by an average margin of 6.71% and 14.42% for deep learning techniques using EEG-only and ECG-only, respectively. Our model's state-of-the-art performance and robustness to out-of-distribution datasets show the accuracy and efficiency necessary to improve epilepsy diagnoses. To the best of our knowledge, this is the first pseudo-prospective study of an AI system combining EEG and ECG modalities for automatic seizure annotation achieved with fusion of two deep learning networks.

Enhancing patients' experiences of living with epilepsy

Epilepsy is a multifaceted neurological condition that has many causes. Living with epilepsy can have significant physical, psychological and social effects on an individual and their family. A patient's experience of living with epilepsy can be influenced by multiple factors, such as pre-existing comorbidities or underlying risk factors for developing comorbidities. This article explores the experiences of patients with epilepsy at the point of diagnosis, in hospital and when taking anti-epileptic drugs. It also details various evidence-based interventions that can improve these patients' experiences and the quality of care that they receive.

Approach to Loss of Consciousness: Distinguishing Epileptic Seizures, Psychogenic Nonepileptic Seizures, and Syncope

Transient loss of consciousness (TLOC) is a common emergent neurological issue, which can be attributed to syncope, epileptic seizures, and psychogenic nonepileptic seizures. The purpose of this article is to outline an approach to diagnosing the most common etiologies of TLOC by focusing on the importance of the history and physical examination, as well as targeted diagnostic tests.

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

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

Recurrence risk after a first remote symptomatic seizure in adults: Epilepsy or not?

The ILAE practical definition of epilepsy has a one seizure possibility to diagnose epilepsy after a first seizure if the recurrence risk is very high. The recurrence risk after a first seizure in brain disorders (first remote seizure) is often high, but varies with etiology, so more specific information is needed for clinical practice. This review describes etiology-specific recurrence risks in adults with a first remote seizure in stroke, traumatic brain injury, infections, dementia, multiple sclerosis, and tumors. Most studies are short, single center, and retrospective. Inclusion criteria, outcome ascertainment, and results vary. Few patient categories are clearly above the epilepsy threshold of recurrence risk, and there are surprisingly little data for important etiologies like brain infections. Beside stroke, severe TBI could have a sufficiently high recurrence risk for early epilepsy diagnosis, but more studies are needed, preferably prospective ones. The literature is uninformative regarding which seizures qualify as remote. The clinical implication of the low level of available evidence is that for other etiologies than stroke, seizure recurrence remains the most appropriate indicator of epilepsy for most patients with a first remote seizure. Nonetheless, there are worrying indications of a diagnostic drift, which puts patients with a preexisting brain disorder at risk of misdiagnosis. Although there are drawbacks to an intermediate term like "possible epilepsy," it could perhaps be useful in cases when the recurrence risk is high, but epilepsy criteria are not definitely met after a first remote seizure.