How good are we at diagnosing seizures based on semiology?

Deep learning approaches for seizure video analysis: A review

Seizure events can manifest as transient disruptions in the control of movements which may be organized in distinct behavioral sequences, accompanied or not by other observable features such as altered facial expressions. The analysis of these clinical signs, referred to as semiology, is subject to observer variations when specialists evaluate video-recorded events in the clinical setting. To enhance the accuracy and consistency of evaluations, computer-aided video analysis of seizures has emerged as a natural avenue. In the field of medical applications, deep learning and computer vision approaches have driven substantial advancements. Historically, these approaches have been used for disease detection, classification, and prediction using diagnostic data; however, there has been limited exploration of their application in evaluating video-based motion detection in the clinical epileptology setting. While vision-based technologies do not aim to replace clinical expertise, they can significantly contribute to medical decision-making and patient care by providing quantitative evidence and decision support. Behavior monitoring tools offer several advantages such as providing objective information, detecting challenging-to-observe events, reducing documentation efforts, and extending assessment capabilities to areas with limited expertise. The main applications of these could be (1) improved seizure detection methods; (2) refined semiology analysis for predicting seizure type and cerebral localization. In this paper, we detail the foundation technologies used in vision-based systems in the analysis of seizure videos, highlighting their success in semiology detection and analysis, focusing on work published in the last 7 years. We systematically present these methods and indicate how the adoption of deep learning for the analysis of video recordings of seizures could be approached. Additionally, we illustrate how existing technologies can be interconnected through an integrated system for video-based semiology analysis. Each module can be customized and improved by adapting more accurate and robust deep learning approaches as these evolve. Finally, we discuss challenges and research directions for future studies.

A personalized earbud for non-invasive long-term EEG monitoring

Objective. The primary objective of this study was to evaluate the reliability, comfort, and performance of a custom-fit, non-invasive long-term electrophysiologic headphone, known as Aware Hearable, for the ambulatory recording of brain activities. These recordings play a crucial role in diagnosing neurological disorders such as epilepsy and in studying neural dynamics during daily activities.Approach.The study uses commercial manufacturing processes common to the hearing aid industry, such as 3D scanning, computer-aided design modeling, and 3D printing. These processes enable the creation of the Aware Hearable with a personalized, custom-fit, thereby ensuring complete and consistent contact with the inner surfaces of the ear for high-quality data recordings. Additionally, the study employs a machine learning data analysis approach to validate the recordings produced by Aware Hearable, by comparing them to the gold standard intracranial electroencephalography recordings in epilepsy patients.Main results.The results indicate the potential of Aware Hearable to expedite the diagnosis of epilepsy by enabling extended periods of ambulatory recording.Significance.This offers significant reductions in burden to patients and their families. Furthermore, the device's utility may extend to a broader spectrum, making it suitable for other applications involving neurophysiological recordings in real-world settings.

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.

Use of video alone for differentiation of epileptic seizures from non-epileptic spells: A systematic review and meta-analysis

INTRODUCTION

Review of videos (without electroencephalography) to differentiate epileptic seizures (ES) from non-epileptic spells (NES) may be helpful where epilepsy monitoring is not feasible. Previous studies of video-based diagnosis have suffered from variable accuracy, sensitivity, and specificity.

METHODS

We systematically reviewed relevant literature in PubMed, Embase, and Web of Science from inception to September 2022, identifying articles that reported on the video-based diagnosis of ES and NES. In primary analysis, for each study, the most expert group was chosen when different groups of reviewers classified the videos (e.g., epilepsy specialists and general neurologists). In secondary analysis, we compared the diagnostic accuracy of different expertise levels (e.g., epileptologists, general neurologists, residents, medical students). Meta-analysis was performed to obtain pooled estimates of reliability measures.

RESULTS

From 5245 articles identified, 13 met the inclusion criteria, with cumulative data from 683 patients (696 videos) reviewed by 95 independent reviewers in primary analysis. Video alone had a strong ability to differentiate ES from NES as evidenced by the following metrics- area under the curve- 0.9 (considered "outstanding"), sensitivity- 82.2% (95% Confidence Interval [C.I], 80.2%-84.0%), specificity- 84.7% (C.I., 82.8%-86.5%), and diagnostic odds ratio- 24.7 (C.I., 11.5-52.9). The secondary analysis showed reviewer-dependent accuracy with epileptologists showing the highest accuracy (DOR 81.2, C.I., 90.0%-94.6%).

CONCLUSIONS

Video alone has reliable diagnostic performance for differentiating ES from NES. Meta-analysis limitations include inter-study heterogeneity including variable video quality and reviewer expertise. Combined video-EEG remains the gold standard for the diagnosis of epilepsy and NES.

Cognitive profiles in pediatric unilobar vs. multilobar epilepsy

OBJECTIVES

We aimed to determine how cognitive impairment relates to the extent of the presumed epileptogenic zone in pediatric focal epilepsies. We analyzed the cognitive functions in unilobar compared to multilobar focal epilepsy patients that underwent neuropsychological testing at a tertiary epilepsy center.

METHODS

We assessed cognitive functions of pediatric focal epilepsy patients with the German version of the Wechsler Intelligence Scales that measures full-scale IQ and subcategories. We assessed differences in IQ and epilepsy-related variables between unilobar and multilobar epilepsy patients.

RESULTS

We included 62 patients (37 unilobar, 25 multilobar), aged 10.6 ± 3.7 years. Full-scale IQ values were significantly higher in unilobar (93.6 ± 17.7, 95% CI 87.7-99.6) than in multilobar epilepsy patients (77.3 ± 17.2, 95% CI 69.3-85.0; p = 0.001). In all but one IQ subcategory (working memory), significantly higher values were measured in unilobar than in multilobar epilepsy patients. The proportion of unilobar epilepsy patients with severe cognitive impairment (8.3%) and below-average intelligence (30.5%) was lower compared to multilobar epilepsy patients (47.6% and 61.9%; p = 0.002 and p = 0.021, respectively). Epilepsy onset occurred earlier in multilobar (4.0 years, 95% CI 2.6-5.5, SD ± 3.4 years) than in unilobar epilepsy patients (7.0 years, 95% CI 5.5-8.5, SD ± 4.4 years, p = 0.008).

CONCLUSIONS

Pediatric multilobar epilepsy patients face more cognitive issues than unilobar epilepsy patients on average. Our findings should help to identify children and adolescents who are most at risk for impaired cognitive development. A limitation of our study is the simple division into unilobar and multilobar epilepsies, with no specific account being taken of etiology/epilepsy syndrome, which can have a profound effect on cognition.

Treatment of Psychogenic Nonepileptic Seizures

PURPOSE OF REVIEW

Psychogenic nonepileptic seizures (PNES) are the most common Functional Neurological Disorder/Conversion Disorder subtype. Significant advances have been made related to diagnosis, neurobiology, and treatment. In this review, we summarize updates in diagnosis and management over the past 3 years.

RECENT FINDINGS

Although evidence is mixed for the treatment of PNES, psychotherapeutic modalities remain a powerful instrument to empower patients and reduce seizures. A multidisciplinary, holistic approach is beneficial. While seizure freedom in all patients may not be the achieved endpoint in this chronic, paroxysmal disorder, quality of life can be improved with treatment. Additional treatment modalities and further research are needed for patients who are refractory to current treatment. Evidence-based therapies exist for PNES, and recent findings represent an increased understanding of the clinical and neurophysiologic aspects of PNES.

Frontal lobe seizures: overview and update

Frontal lobe seizures (FLS) are debilitating for patients, highly diverse and often challenging for clinicians to evaluate. Frontal lobe epilepsy is the second most common localization for focal epilepsy, and if pharmacoresistant, can be amenable to resective surgery. Detailed study of frontal seizure semiology in conjunction with careful anatomical and electrophysiological correlation based on intracerebral recording with stereoelectroencephalography (SEEG) has allowed demonstration that ictal motor semiology reflects a hierarchical rostro-caudal axis of frontal lobe functional organization, thus helping with presurgical localization. Main semiological features allowing distinction between different frontal sublobar regions include motor signs and emotional signs. Frontal lobe seizure semiology also represents a valuable source of in vivo human behavioral data from a neuroscientific perspective. Advances in defining underlying etiologies of FLE are likely to be crucial for appropriate selection and exploration of potential surgical candidates, which could improve upon current surgical outcomes. Future research on investigating the genetic basis of epilepsies and relation to structural substrate (e.g. focal cortical dysplasia) and seizure organization and expression, could permit a "genotype-phenotype" approach that could be complementary to anatomical electroclinical correlations in better defining the spectrum of FLS. This could help with optimizing patient selection and prognostication with regards to therapeutic choices.

Pitfalls using smartphones videos in diagnosing functional seizures

Expert review of seizure semiology looking at video recordings independent of EEG has been found to be useful for diagnosing functional seizures. Videos recorded outside the hospital containing "spells" have similar sensitivity to EEG when quality recordings are evaluated. Recently, smartphone videos were shown to serve as an adjunct to standard history and physical examination with similar diagnostic yields when compared to diagnostic video-EEG monitoring and reviewed by experts. However, caution must be exercised when interpreting videos of paroxysmal neurological events recorded by caregivers to ensure proper video quality is maintained and recorded event is representative. In this report, we present a case of initial identification of and event falsely suggesting functional seizures in a patient with epilepsy. The smartphone video of a "seizure" was recorded by his wife using her smartphone. Despite a quality recording and a history consistent with epilepsy, the smartphone video reviewed during evaluation in the clinic suggested a functional behavior in contrast to the history that suggested epilepsy manifest as convulsions. Instead of bilateral tonic-clonic motor movements, bizarre, intermittent non-clonic wild flinging movements and vocalization were identified on the smartphone video. The discordance between the clnical history and ideo prompted inpatient video-EEG monitoring. The same nonepileptic semiology was subsequently clarified to represent a physiological nonepileptic event. The event on the smartphone was typical of his agitated post-ictal state following an electroclinical tonic-clonic seizure. With treatment the seizures became controlled with antiseizure medication in long-term follow-up. We highlight the pitfalls using patient-recorded smartphone videos in patients diagnosed with epilepsy. Understanding the utility of smartphones as an adjunct to the clinical history will help in differentiating epileptic from functional seizures.

Predictive semiology of psychogenic non-epileptic seizures in an epilepsy monitoring unit

Introduction

The diagnosis of psychogenic nonepileptic seizures (PNES) is a common clinical dilemma. We sought to assess the diagnostic value of four ictal signs commonly used in differentiating PNES from epileptic seizures (ES).

Methods

We retrospectively reviewed consecutive adult video-electroencephalogram (VEM) studies conducted at a single tertiary epilepsy center between May 2009 and August 2016. Each event was assessed by a blinded rater for the presence of four signs: fluctuating course, head shaking, hip thrusting, and back arching. The final diagnosis of PNES or ES was established for each event based on clinical and VEM characteristics. All ES were pooled regardless of focal or generalized onset. We analyzed the odds ratio of each sign in PNES in comparison to ES with adjustment for repeated measures using logistic regression. Additionally, we calculated the sensitivity, specificity, predictive values, and likelihood ratios of each sign to diagnose PNES.

Results

A total of 742 events from 140 VEM studies were assessed. Fluctuating course (odds ratio (OR) 37.37, 95% confidence interval (CI) 13.56–102.96, P < 0.0001), head shaking (OR 2.95, 95% CI 1.26–6.79, P = 0.012), and hip thrusting (OR 4.28, 95% CI 1.21–15.18, P = 0.02) were each significantly predictive of PNES. Fluctuating course had the highest sensitivity (76.16%). Back arching (OR 1.06, 95% CI 0.35–3.20, P = 0.92) was not significantly associated with PNES.

Conclusion

Fluctuating course, head shaking, and hip thrusting are semiological features significantly more common in PNES than ES. Fluctuating course is the most reliable sign. Back arching does not appear to differentiate PNES from ES.

On seizure semiology

The clinical expression of seizures represents the main symptomatic burden of epilepsy. Neural mechanisms of semiologic production in epilepsy, especially for complex behaviors, remain poorly known. In a framework of epilepsy as a network rather than as a focal disorder, we can think of semiology as being dynamically produced by a set of interconnected structures, in which specific rhythmic interactions, and not just anatomical localization, are likely to play an important part in clinical expression. This requires a paradigm shift in how we think about seizure organization, including from a presurgical evaluation perspective. Semiology is a key data source, albeit with significant methodological challenges for its use in research, including observer bias and choice of semiologic categories. Better understanding of semiologic categorization and pathophysiological correlates is relevant to seizure classification systems. Advances in knowledge of neural mechanisms as well as anatomic correlates of different semiologic patterns could help improve knowledge of epilepsy networks and potentially contribute to therapeutic innovations.

Recognition of Psychogenic Versus Epileptic Seizures Based on Videos

OBJECTIVE

Ictal semiology interpretation for differentiating psychogenic nonepileptic seizures (PNESs) and epileptic seizures (ESs) is important for the institution of appropriate treatment. Our objective was to assess the ability of different health care professionals (HCPs) or students to distinguish PNES from ES based on video-recorded seizure semiology.

METHODS

This study was designed following the Standards for Reporting of Diagnostic Accuracy Studies (STARD) guidelines. We showed in a random mix 36 videos of PNES or ES (18 each) and asked 558 participants to classify each seizure. The diagnostic accuracy of various groups of HCPs or students for PNES versus ES was assessed, as well as the effect of patient age and sex. Measures of diagnostic accuracy included sensitivity, specificity, and area under the curve (AUC).

RESULTS

The descending order of diagnostic accuracy (AUC) was the following (p ≤ 0.001): (1) neurologists and epileptologists; (2) neurology residents; (3) other specialists and nurses with experience in epilepsy; and (4) undergraduate medical students. Although there was a strong trend toward statistical difference, with AUC 95% confidence intervals (CIs) that were not overlapping, between epileptologists (95% CI 93, 97) compared to neurologists (95% CI 88, 91), and neurologists compared to electroencephalography technicians (95% CI 82, 87), multiple pairwise comparisons with the conservative Tukey-Kramer honest significant difference test revealed no statistical difference (p = 0.25 and 0.1, respectively). Patient age and sex did not have an effect on diagnostic accuracy in neurology specialists.

CONCLUSION

Visual recognition of PNES by HCPs or students varies overall proportionately with the level of expertise in the field of neurology/epilepsy.

Long-Term V-EEG in Epilepsy: Chronological Distribution of Recorded Events Focused on the Differential Diagnosis of Epileptic Seizures and Psychogenic Non-Epileptic Seizures

Differential diagnosis in epilepsy is sometimes challenging. Video-electroencephalography (V-EEG) is an essential tool in the diagnosis and management of epilepsy. The prolonged duration of V-EEG recording increases the diagnostic yield of a conventional V-EEG. The right length of monitoring for different indications is still to be established. We present a retrospective descriptive study with a sample of 50 patients with long-term V-EEG monitoring, with a mean age of 36.1 years, monitored from 2013 to 2019 at the Burgos University Hospital. The mean monitoring time was 3.6 days. Events were obtained in 76% of the patients, corresponding to epileptic seizures (ES) in 57.9% of them, with psychogenic non-epileptic seizures (PNES) in 39.5%, and with episodes of both pathologies in 2.6% of the patients. We found that the first event was highly representative, and it correlated with the rest of the events that would be recorded. Moreover, 92% of the first PNES had been captured at the end of the second day, and 89% of the first ES by the end of the third day. V-EEG for differential diagnosis between ES and PNES can be performed in hospitals without specialized epilepsy surgery units. For this indication, the duration of long-term V-EEG can be adjusted individually depending on the nature of the first event.

Discovering themes in medical records of patients with psychogenic non-epileptic seizures

Introduction

Epileptic and psychogenic non-epileptic seizures (PNES) are common diagnostic problems encountered in hospital practice. This study explores the use of unsupervised machine learning in discovering themes in medical records of patients presenting with PNES. We hypothesised that themes generated by machine learning are comparable with the classification by human experts.

Methods

This is a retrospective analysis of the medical records in the emergency department of patients (age >18 years) with PNES who underwent inpatient video-electroencephalography monitoring from May 2009 to June 2014 and received a final diagnosis of PNES. Prior to machine learning of written text, we applied a standardised approach in natural language processing to create a document-term matrix (removal of numbers, stop-words and punctuations, transforming fonts to lower case). The words were separated into tokens and treated as if existing within a bag-of-words. A probability of each word existing within a topic (theme) was modelled on multivariate Dirichlet distribution (R Foundation, V.3.5.0). Next, we asked four experts to independently provide a clinical interpretation of the generated topics. When the majority of (≥3) experts agreed, it was regarded as highly congruent. Interactive data are available on the web at (https://gntem2.github.io/PNES/%23topic=1&lambda=0.6&term=).

Results

There were 39 patients (74.4% women, median age 35 years with range 20-82). A total of 121 documents were converted to text files for text mining. There were 15 generated topics with 12/15 topics rated as highly congruent. The main themes were about descriptors of seizures and medication use.

Conclusions

The findings from machine learning on PNES-related documentation provides evidence for the feasibility of applying machine-learning methodology to analyse large volumes of medical records. The topics generated by machine learning were congruent with interpretations by clinicians indicating this method can be used for screening of medical conditions among large volumes of medical records.

Epilepsy, dissociative seizures, and mixed: Associations with time to video-EEG

PURPOSE

Video-electroencephalographic monitoring (VEM) is a core component to the diagnosis and evaluation of epilepsy and dissociative seizures (DS)-also known as functional or psychogenic seizures-but VEM evaluation often occurs later than recommended. To understand why delays occur, we compared how patient-reported clinical factors were associated with time from first seizure to VEM (TVEM) in patients with epilepsy, DS or mixed.

METHODS

We acquired data from 1245 consecutive patients with epilepsy, VEM-documented DS or mixed epilepsy and DS. We used multivariate log-normal regression with recursive feature elimination (RFE) to evaluate which of 76 clinical factors interacting with patients' diagnoses were associated with TVEM.

RESULTS

The mean and median TVEM were 14.6 years and 10 years, respectively (IQR 3-23 years). In the multivariate RFE model, the factors associated with longer TVEM in all patients included unemployment and not student status, more antiseizure medications (current and past), concussion, and ictal behavior suggestive of temporal lobe epilepsy. Average TVEM was shorter for DS than epilepsy, particularly for patients with depression, anxiety, migraines, and eye closure. Average TVEM was longer specifically for patients with DS taking more medications, more seizure types, non-metastatic cancer, and with other psychiatric comorbidities.

CONCLUSIONS

In all patients with seizures, trials of numerous antiseizure medications, unemployment and non-student status was associated with longer TVEM. These associations highlight a disconnect between International League Against Epilepsy practice parameters and observed referral patterns in epilepsy. In patients with dissociative seizures, some but not all factors classically associated with DS reduced TVEM.

Validation of a predictive calculator to distinguish between patients presenting with dissociative versus epileptic seizures

Dissociative seizures (also known as psychogenic nonepileptic seizures) are a common functional neurological disorder that can be difficult to distinguish from epileptic seizures. Patients with dissociative seizures provide diagnostic challenges, leading to delays in care, inappropriate care, and significant healthcare utilization and associated costs. The dissociative seizure likelihood score (DSLS) was developed by Kerr and colleagues at UCLA to distinguish between patients with epileptic seizures and dissociative seizures based on clinical and medication history as well as features of seizure semiology. We validated this calculator at the University of Colorado, which is a Level 4 National Association of Epilepsy Center. The DSLS accurately predicted the diagnosis in 81% of patients, despite local variability in the factors associated with epileptic versus dissociative seizures between the two populations. The DSLS can be a useful tool to assist with history taking and may have important utility for clinical decision making with these difficult to distinguish patient populations.

Assessment of the Predictive Value of Outpatient Smartphone Videos for Diagnosis of Epileptic Seizures

Importance

Misdiagnosis of epilepsy is common. Video electroencephalogram provides a definitive diagnosis but is impractical for many patients referred for evaluation of epilepsy.

Objective

To evaluate the accuracy of outpatient smartphone videos in epilepsy.

Design, Setting, and Participants

This prospective, masked, diagnostic accuracy study (the OSmartViE study) took place between August 31, 2015, and August 31, 2018, at 8 academic epilepsy centers in the United States and included a convenience sample of 44 nonconsecutive outpatients who volunteered a smartphone video during evaluation and subsequently underwent video electroencephalogram monitoring. Three epileptologists uploaded videos for physicians from the 8 epilepsy centers to review.

Main Outcomes and Measures

Measures of performance (accuracy, sensitivity, specificity, positive predictive value, and negative predictive value) for smartphone video-based diagnosis by experts and trainees (the index test) were compared with those for history and physical examination and video electroencephalogram monitoring (the reference standard).

Results

Forty-four eligible epilepsy clinic outpatients (31 women [70.5%]; mean [range] age, 45.1 [20-82] years) submitted smartphone videos (530 total physician reviews). Final video electroencephalogram diagnoses included 11 epileptic seizures, 30 psychogenic nonepileptic attacks, and 3 physiologic nonepileptic events. Expert interpretation of a smartphone video was accurate in predicting a video electroencephalogram monitoring diagnosis of epileptic seizures 89.1% (95% CI, 84.2%-92.9%) of the time, with a specificity of 93.3% (95% CI, 88.3%-96.6%). Resident responses were less accurate for all metrics involving epileptic seizures and psychogenic nonepileptic attacks, despite greater confidence. Motor signs during events increased accuracy. One-fourth of the smartphone videos were correctly diagnosed by 100% of the reviewing physicians, composed solely of psychogenic attacks. When histories and physical examination results were combined with smartphone videos, correct diagnoses rose from 78.6% to 95.2%. The odds of receiving a correct diagnosis were 5.45 times greater using smartphone video alongside patient history and physical examination results than with history and physical examination alone (95% CI, 1.01-54.3; P = .02).

Conclusions and Relevance

Outpatient smartphone video review by experts has predictive and additive value for diagnosing epileptic seizures. Smartphone videos may reliably aid psychogenic nonepileptic attacks diagnosis for some people.

Video quality using outpatient smartphone videos in epilepsy: Results from the OSmartViE study

BACKGROUND AND PURPOSE

The aim of this study was to evaluate the quality of smartphone videos (SVs) of neurologic events in adult epilepsy outpatients. The use of home video recording in patients with neurological disease states is increasing. Experts interpretation of outpatient smartphone videos of seizures and neurological events has demonstrated similar diagnostic accuracy to inpatient video-electroencephalography (EEG) monitoring.

METHODS

A prospective, multicenter cohort study was conducted to evaluate SV quality in patients with paroxysmal neurologic events from August 15, 2015 through August 31, 2018. Epileptic seizures (ESs), psychogenic nonepileptic attacks (PNEAs), and physiologic nonepileptic events (PhysNEEs) were confirmed by video-EEG monitoring. Experts and senior neurology residents blindly viewed cloud-based SVs without clinical information. Quality ratings with regard to technical and operator-driven metrics were provided in responses to a survey.

RESULTS

Forty-four patients (31 women, age 45.1 years [r = 20-82]) were included and 530 SVs were viewed by a mean of seven experts and six residents; one video per patient was reviewed for a mean of 133.8 s (r = 9-543). In all, 30 patients had PNEAs, 11 had ESs, and three had PhysNEEs. Quality was suitable in 70.8% of SVs (375/530 total views), with 36/44 (81.8%) patient SVs rated as adequate by the majority of reviewers. Accuracy improved with the presence of convulsive features from 72.4% to 98.2% in ESs and from 71.1% to 95.7% in PNEAs. An accurate diagnosis was given by all reviewers (100%) in 11/44 SVs (all PNEAs). Audio was rated as good by 86.2% of reviewers for these SVs compared with 75.4% for the remaining SVs (p = 0.01). Lighting was better in SVs associated with high accuracy (p = 0.06), but clarity was not (p = 0.59). Poor video quality yielded unknown diagnoses in 24.2% of the SVs reviewed. Features hindering diagnosis were limited interactivity, restricted field of view and short video duration.

CONCLUSIONS

Smartphone video quality is adequate for clinical interpretation in the majority of patients with paroxysmal neurologic events. Quality can be optimized by encouraging interactivity with the patient, adequate duration of the SV, and enlarged field of view during videography. Quality limitations were primarily operational though accuracy remained for SV review of ESs and PNEAs.

Objective score from initial interview identifies patients with probable dissociative seizures

OBJECTIVE

To develop a Dissociative Seizures Likelihood Score (DSLS), which is a comprehensive, evidence-based tool using information available during the first outpatient visit to identify patients with "probable" dissociative seizures (DS) to allow early triage to more extensive diagnostic assessment.

METHODS

Based on data from 1616 patients with video-electroencephalography (vEEG) confirmed diagnoses, we compared the clinical history from a single neurology interview of patients in five mutually exclusive groups: epileptic seizures (ES), DS, physiologic nonepileptic seizure-like events (PSLE), mixed DS plus ES, and inconclusive monitoring. We used data-driven methods to determine the diagnostic utility of 76 features from retrospective chart review and applied this model to prospective interviews.

RESULTS

The DSLS using recursive feature elimination (RFE) correctly identified 77% (95% confidence interval (CI), 74-80%) of prospective patients with either ES or DS, with a sensitivity of 74% and specificity of 84%. This accuracy was not significantly inferior than neurologists' impression (84%, 95% CI: 80-88%) and the kappa between neurologists' and the DSLS was 21% (95% CI: 1-41%). Only 3% of patients with DS were missed by both the fellows and our score (95% CI 0-11%).

SIGNIFICANCE

The evidence-based DSLS establishes one method to reliably identify some patients with probable DS using clinical history. The DSLS supports and does not replace clinical decision making. While not all patients with DS can be identified by clinical history alone, these methods combined with clinical judgement could be used to identify patients who warrant further diagnostic assessment at a comprehensive epilepsy center.

Can seizure-related heart rate differentiate epileptic from psychogenic nonepileptic seizures?

OBJECTIVES

We aimed to (1) determine if seizure-related heart rate (HR) differentiates epileptic seizures (ES) from psychogenic nonepileptic seizures (PNES); (2) define the most useful point of the following HR measurements: preictal, ictal-onset, maximal-ictal, or postictal; and (3) delineate the optimal HR cutoff points (absolute HR and relative HR increase) to differentiate ES from PNES.

METHODS

All video-electroencephalography (VEEG) recorded at an Australian tertiary hospital from May 2009 to November 2015 were retrospectively reviewed. Baseline (during rest and wakefulness), 1-min preictal, ictal-onset, maximal-ictal, and 1-min postictal HR were measured for each ES and PNES event. Events lasting <10 s or with uninterpretable electrocardiogram (ECG) due to artifacts were excluded. Receiver operating characteristic curve analysis was performed to assess the diagnostic accuracy of HR reflected by the area under the curve (AUC).

RESULTS

Video-electroencephalography of 341 ES and 265 PNES from 130 patients were analyzed. The AUC for preictal, ictal-onset, maximal-ictal, and postictal HR were found to have poor differentiation between all types of ES and PNES. However, comparing bilateral tonic-clonic ES and PNES, AUC for absolute maximal-ictal HR was 0.84 (95% confidence interval [CI]: 0.73-0.95) and for absolute postictal HR was 0.90 (95% CI: 0.81-1.00) indicating good diagnostic discrimination. Using Youden's index to diagnose tonic-clonic ES, the optimal cutoff point for absolute maximal-ictal HR was 114 bpm (sensitivity: 84%, specificity: 82%) and for absolute postictal HR was 90 bpm (sensitivity: 91%, specificity: 82%).

CONCLUSION

These findings suggest that seizure-related HR is useful in differentiating bilateral tonic-clonic ES from PNES. Based on the AUC, the best diagnostic measurements are maximal-ictal and postictal HR.

Calibration of the Epilepsy Questionnaire for Use in a Low-Resource Setting

Background. Burden of epilepsy in sub-Saharan Africa is huge in the midst of shortage of human resource in its health sector. Using skilled staff to supervise and support lower level healthcare workers providing frontline primary healthcare is a pragmatic coping solution. But, lower level health providers face enormous challenges due to absent clinical algorithms or pragmatic rapid diagnostic tests. Objective. This study aimed to determine if the use of an epilepsy questionnaire in a traditional clinical setting would improve semiological details obtained and diagnostic accuracy. Methods. A prospective study was conducted involving patients diagnosed with epilepsy each with an eye witness who had regularly witnessed the seizures. Routine seizure history from clinical documentation and an interviewer-based questionnaire were compared. The data obtained were assessed for content, accuracy, intermethod and test-retest reliability. Results. Sixty-seven patients with a median age of 24 years were recruited. Routine seizure history had obtained less semiological details with inadequate description of nonmotor manifestations and lateralizing motor details. The questionnaire-obtained history showed higher accuracy for generalized onset seizure (0.83 vs. 0.56) and focal onset seizures (0.79 vs. 0.59). The questionnaire-obtained history also had good test-retest reliability for various semiological domains except automatisms. Conclusions. Routine seizure histories are not standardized. The use of a questionnaire goes a long way in improving semiology description in a low-resource setting and guides the health provider on what details to focus on. The use of epilepsy questionnaires should, therefore, be considered to improve semiology, especially in nonspecialist settings.

Differential diagnosis between epileptic seizures and psychogenic nonepileptic seizures based on semiology

Psychogenic nonepileptic seizures present as paroxysmal symptoms and signs mimicking epileptic seizures. The gold standard test is the synchronous recording by video, electrocardiogram and electroencephalogram. However, video electroencephalogram is not available at many centers and not entirely independent of semiology. Recent studies have focused on semiological characteristics distinguishing these two circumstances. Clinical signs and symptoms provide important clues when making differential diagnosis. The purpose of this review is to help physicians differentiating psychogenic nonepileptic seizures better from epileptic seizures based on semiology, and improve care for those patients.

Reliability of reported peri-ictal behavior to identify psychogenic nonepileptic seizures

PURPOSE

Differentiating psychogenic non-epileptic seizures (PNES) from epileptic seizures (ES) can be difficult, even when expert clinicians have video recordings of seizures. Moreover, witnesses who are not trained observers may provide descriptions that differ from the expert clinicians', which often raises concern about whether the patient has both ES and PNES. As such, quantitative, evidence-based tools to help differentiate ES from PNES based on patients' and witnesses' descriptions of seizures may assist in the early, accurate diagnosis of patients.

METHODS

Based on patient- and observer-reported data from 1372 patients with diagnoses documented by video-elect roencephalography (vEEG), we used logistic regression (LR) to compare specific peri-ictal behaviors and seizure triggers in five mutually exclusive groups: ES, PNES, physiologic non-epileptic seizure-like events, mixed PNES plus ES, and inconclusive monitoring. To differentiate PNES-only from ES-only, we retrospectively trained multivariate LR and a forest of decision trees (DF) to predict the documented diagnoses of 246 prospective patients.

RESULTS

The areas under the receiver operating characteristic curve (AUCs) of the DF and LR were 75% and 74%, respectively (empiric 95% CI of chance 37-62%). The overall accuracy was not significantly higher than the naïve assumption that all patients have ES (accuracy DF 71%, LR 70%, naïve 68%, p > 0.05).

CONCLUSIONS

Quantitative analysis of patient- and observer-reported peri-ictal behaviors objectively changed the likelihood that a patient's seizures were psychogenic, but these reports were not reliable enough to be diagnostic in isolation. Instead, our scores may identify patients with "probable" PNES that, in the right clinical context, may warrant further diagnostic assessment.

Practical Management of Epileptic Seizures and Status Epilepticus in Adult Palliative Care Patients

In terminally ill patients, paroxysmal or episodic changes of consciousness, movements and behavior are frequent. Due to ambiguous appearance, the correct diagnosis of epileptic seizures (ES) and non-epileptic events (NEE) is often difficult. Treatment is frequently complicated by the underlying condition, and an approach indicated in healthier patients may not always be appropriate in the palliative care setting. This article provides recommendations for diagnosis of ES and NEE and treatment options for ES in adult palliative care patients, including aspects of alternative administration routes for antiepileptic drugs such as intranasal, subcutaneous, or rectal application.

An objective score to identify psychogenic seizures based on age of onset and history

OBJECTIVE

Psychogenic nonepileptic seizure (PNES) is a common diagnosis after evaluation of medication resistant or atypical seizures with video-electroencephalographic monitoring (VEM), but usually follows a long delay after the development of seizures, during which patients are treated for epilepsy. Therefore, more readily available diagnostic tools are needed for earlier identification of patients at risk for PNES. A tool based on patient-reported psychosocial history would be especially beneficial because it could be implemented in the outpatient clinic.

METHODS

Based on the data from 1375 patients with VEM-confirmed diagnoses, we used logistic regression to compare the frequency of specific patient-reported historical events, demographic information, age of onset, and delay from first seizure until VEM in five mutually exclusive groups of patients: epileptic seizures (ES), PNES, physiologic nonepileptic seizure-like events (PSLE), mixed PNES plus ES, and inconclusive monitoring. To determine the diagnostic utility of this information to differentiate PNES only from ES only, we used multivariate piecewise-linear logistic regression trained using retrospective data from chart review and validated based on data from 246 prospective standardized interviews.

RESULTS

The prospective area under the curve of our weighted multivariate piecewise-linear by-sex score was 73%, with the threshold that maximized overall retrospective accuracy resulting in a prospective sensitivity of 74% (95% CI: 70-79%) and prospective specificity of 71% (95% CI: 64-82%). The linear model and piecewise linear without an interaction term for sex had very similar performance statistics. In the multivariate piecewise-linear sex-split predictive model, the significant factors positively associated with ES were history of febrile seizures, current employment or active student status, history of traumatic brain injury (TBI), and longer delay from first seizure until VEM. The significant factors associated with PNES were female sex, older age of onset, mild TBI, and significant stressful events with sexual abuse, in particular, increasing the likelihood of PNES. Delays longer than 20years, age of onset after 31years for men, and age of onset after 40years for women had no additional effect on the likelihood of PNES.

DISCUSSION

Our promising results suggest that an objective score has the potential to serve as an early outpatient screening tool to identify patients with greater likelihood of PNES when considered in combination with other factors. In addition, our analysis suggests that sexual abuse, more than other psychological stressors including physical abuse, is more associated with PNES. There was a trend of increasing frequency of PNES for women during childbearing years and plateauing outside those years that was not observed in men.

" Blaming, shaming, humiliation": Stigmatising medical interactions among people with non-epileptic seizures

Background: People with non-epileptic seizures (NES) describe challenging relationships with health professionals, and explain negative interactions as common and expected. Despite these difficulties, little is known about how people with NES experience difficult healthcare encounters. Methods: Using a thematic discourse analysis approach, we analysed the free-text survey responses of 135 people with NES and asked: what kind of challenges do people living with this condition encounter when interacting with health professionals, and how do they experience the consequences of difficult interactions? We explore their experiences by interpreting the latent meaning of participants' texts from a social-constructionist perspective on health and illness. Results: The overarching narrative depicts a fundamental breakdown in patient-provider relationships. According to our data, the negative experiences of study participants emerge from more than practitioners' lack of awareness of NES and access to information about the condition - to the extent that it is available. In examining the challenges people with NES encounter when interacting with health professionals, their main experiences centre on blame and humiliation. When exploring their experiences, theories of stigma serve as a useful theoretical framework. Conclusions: Normative judgements arising from psychogenic understandings of NES are stigmatising and restrict professional displays of respectful (patient-centred) care. Those with the condition depict being negatively stereotyped, illegitimated and held morally culpable by health professionals. Perceived to lack medical, moral and credible status, participants describe practitioners who treat them with disrespect, and some recount conduct that defies all ethical and professional obligations and standards. These encounters can have wide-ranging adverse consequences for patients: emotionally, physically, and for their future healthcare. The quality of healthcare interactions for people with NES requires urgent improvements. In addition to increased awareness of the condition, practitioners need to be conscious of making and acting on adverse moral appraisals when interacting with this patient group.

" Blaming, shaming, humiliation": Stigmatising medical interactions among people with non-epileptic seizures

Background: People with non-epileptic seizures (NES) describe challenging relationships with health professionals, and explain negative interactions as common and expected. Despite these difficulties, little is known about how people with NES experience difficult healthcare encounters. Methods: Using a thematic discourse analysis approach, we analysed the free-text survey responses of 135 people with NES and asked: what kind of challenges do people living with this condition encounter when interacting with health professionals, and how do they experience the consequences of difficult interactions? We explore their experiences by interpreting the latent meaning of participants' texts from a social-constructionist perspective on health and illness. Results: The overarching narrative depicts a fundamental breakdown in patient-provider relationships. According to our data, the negative experiences of study participants emerge from more than practitioners' lack of awareness of NES and access to information about the condition - to the extent that it is available. In examining the challenges people with NES encounter when interacting with health professionals, their main experiences centre on blame and humiliation. When exploring their experiences, theories of stigma serve as a useful theoretical framework. Conclusions: Normative judgements arising from psychogenic understandings of NES are stigmatising and restrict professional displays of respectful (patient-centred) care. Those with the condition depict being negatively stereotyped, illegitimated and held morally culpable by health professionals. Perceived to lack medical, moral and credible status, participants describe practitioners who treat them with disrespect, and some recount conduct that defies all ethical and professional obligations and standards. These encounters can have wide-ranging adverse consequences for patients: emotionally, physically, and for their future healthcare. The quality of healthcare interactions for people with NES requires urgent improvements. In addition to increased awareness of the condition, practitioners need to be conscious of making and acting on adverse moral appraisals when interacting with this patient group.

Proposal for best practice in the use of video-EEG when psychogenic non-epileptic seizures are a possible diagnosis

The gold-standard for the diagnosis of psychogenic non-epileptic seizures (PNES) is capturing an attack with typical semiology and lack of epileptic ictal discharges on video-EEG. Despite the importance of this diagnostic test, lack of standardisation has resulted in a wide variety of protocols and reporting practices. The goal of this review is to provide an overview of research findings on the diagnostic video-EEG procedure, in both the adult and paediatric literature. We discuss how uncertainties about the ethical use of suggestion can be resolved, and consider what constitutes best clinical practice. We stress the importance of ictal observation and assessment and consider how diagnostically useful information is best obtained. We also discuss the optimal format of video-EEG reports; and of highlighting features with high sensitivity and specificity to reduce the risk of miscommunication. We suggest that over-interpretation of the interictal EEG, and the failure to recognise differences between typical epileptic and nonepileptic seizure manifestations are the greatest pitfalls in neurophysiological assessment of patients with PNES. Meanwhile, under-recognition of semiological pointers towards frontal lobe seizures and of the absence of epileptiform ictal EEG patterns during some epileptic seizure types (especially some seizures not associated with loss of awareness), may lead to erroneous PNES diagnoses. We propose that a standardised approach to the video-EEG examination and the subsequent written report will facilitate a clear communication of its import, improving diagnostic certainty and thereby promoting appropriate patient management.

Differential diagnosis of a paroxysmal neurological event: Do neurologists know how to clinically recognize it?

PURPOSE

To investigate ability to recognize paroxysmal neurological events (PNE) based on video-recorded events alone in a group of physicians treating prevalent neurological conditions.

METHODS

Total of 12 patients' videos (6 epileptic seizures (ES), 4 psychogenic nonepileptic seizures (PNES), 2 other nonepileptic seizures (oNES)) were selected. Videos were displayed once to physicians blind to clinical data and final diagnosis. Physicians determined their clinical choice: ES, PNES, oNES, and I don't know (IDK). When ES was chosen, subjects determined type of ES: focal ES, secondary generalized tonic-clonic seizure (GTCS), primary GTCS, and IDK.

RESULTS

In total 145 physicians (62% female, mean age 46.2±9years) (neurologists 58.6%, neuropsychiatrists 25.5%, psychiatrists 5%, and neurology residents 10.3%) were enrolled. Physician's exposure to patients with epilepsy per week was diverse: ≤1 patient (43.7%); 1-7 patients (37.2%); >7 patients (14.5%). Reported frequency of observation of PNE was as follows: frequent (21.4%), sometimes (47.6%); rarely (26.9%); never (2.1%). Majority of subjects were not EEG readers (60.7%). Median percentage (Mdn%) of correct answers (CA) was 75% (range 25-100). Predictor of better PNE recognition was higher frequency of clinical exposure to PNE (OR 1.65; CI95% 1.11-2.45; p=0.013). Mdn% of ES CA was 83.3%, (range 33.3-100), and of PNES CA was 50% (range 0-100). Physicians were more accurate in ES than PNES identification (p<0,001). Mdn% of type of ES CA was 50%, (range 0-100).

CONCLUSIONS

We demonstrate the need for education about clinical features of PNE across subgroups of physicians who deliver neurological service, with emphasis on PNES and ES type classification.

Development and validation of AIIMS modified INCLEN diagnostic instrument for epilepsy in children aged 1 month-18 years

OBJECTIVES

There is shortage of specialists for the diagnosis of children with epilepsy, especially in resource limited settings. Existing INCLEN (International Clinical Epidemiology Network) instrument was validated for children aged 2-9 years. The current study validated modifications of the same including wider symptomatology and age group.

METHODS

The Modified INCLEN tool was validated by a team of experts by modifying the existing tools (2-9 years) to widen the age range from 1 month to 18 years and include broader symptomatology in a tertiary care teaching hospital of North India between January and June 2015. A qualified medical graduate applied the candidate tool which was followed by gold standard evaluation by a Pediatric Neurologist (both blinded to each other).

RESULTS

A total of 197 children {128 boys (65%) and 69 girls (35%)}, with a mean age of 72.08 (±50.96) months, completed the study. The sensitivity, specificity, positive and negative predictive value, positive and negative likelihood ratio of the modified epilepsy tool were 91.5% (84.5-96.1), 88.6% (80.0-93.5), 89.7% (81.9-95.3), 90.8% (83.7-95.7), 8 (6.6-9.8) and 0.09 (0.07-0.12) respectively.

SIGNIFICANCE

The new modified diagnostic instruments for epilepsy is simple, structured and valid instruments covering 1month to 18 years for use in resource limited settings with acceptable diagnostic accuracy. All seizure semiologies as well as common seizure mimics like breath-holding spells are included in the tool. It also provides for identification of acute symptomatic and febrile seizures.

How reliable is ictal duration to differentiate psychogenic nonepileptic seizures from epileptic seizures?

We sought to investigate (1) differences in ictal duration between psychogenic nonepileptic seizures (PNES) and epileptic seizures (ES), (2) the odds of being PNES when seizures last ≥5min, and (3) the value of ictal duration as a diagnostic test to differentiate PNES from ES. We retrospectively reviewed video-EEG recordings and tabulated ictal durations of all PNES and ES. We estimated the mean ictal durations of PNES and ES using linear mixed models. The odds of being PNES when seizures last ≥5min were estimated using logistic regression. We used receiver operating characteristics (ROC) curves to study the overall diagnostic accuracy of ictal duration in differentiating PNES from ES. We studied 441 ES and 341 PNES recorded from 138 patients. The mean ictal duration of PNES (148.7s, 95% CI: 115.2-191.8) was significantly longer (p<0.001) than that of ES (47.7s, 95% CI: 37.6-60.6). The odds of being PNES was about 24 times higher (Odds ratio: 23.8, 95% CI: 7.9-71.3) when the ictal duration was ≥5min. The ROC curve yielded an area under the curve of 0.80 (95% CI 0.73-0.88). Youden's index identified 123.5s as the optimal threshold to diagnose PNES with 65% sensitivity and 93% specificity. Our results indicate that ictal duration is a useful test to raise suspicion of PNES. When a seizure lasts ≥5min, it is 24 times more likely to be PNES with the potential risk of misdiagnosis as status epilepticus.

Improving first responders' psychogenic nonepileptic seizures diagnosis accuracy: Development and validation of a 6-item bedside diagnostic tool

OBJECTIVE

Epileptic seizures (ES) are often seen as a medical emergency, and their immediate and accurate recognition are pivotal in providing acute care. However, a number of clinical situations may mimic ES, potentially leading to misdiagnosis at the emergency room and to inappropriate prescription of antiepileptic drugs (AED) in the acute and chronic settings. Psychogenic nonepileptic seizures (PNES) play a major role in this scenario and often delay the correct diagnosis and increase treatment morbidity and cost. First responders often conduct the initial assessment of these patients, and their impression may be decisive in the prehospital approach to seizures. We sought to investigate and improve the accuracy of PNES diagnosis among professionals involved in the initial assistance to patients with seizures.

METHODS

Fifty-three registered nurses, 34 emergency physicians, 33 senior year medical students, and 12 neurology residents took a short training program consisting of an initial video-based seizure assessment test (pretest), immediately followed by a 30-minute presentation of a 6-item bedside diagnostic tool and then a video-based reassessment (posttest). Baseline status and learning curves were determined.

RESULTS

The distinct professional categories showed no significant differences in their ability to diagnose PNES on both pretests and posttests. All groups improved diagnostic skills after the instructional program.

SIGNIFICANCE

The findings helped determine the best identifiable PNES clinical signs and to provide initial validation to a novel diagnostic instrument. In addition, our results showed that educational measures might help in the identification of PNES by first responders, which may decrease the treatment gap.

How do doctors in training react to seizures?

BACKGROUND AND AIMS

There are scant data on how doctors approach seizures in the acute setting. We sought to study (a) exposure to seizure disorders as well as relevant training and (b) reactions to seizures in the acute setting, among medical residents undergoing physician training.

METHODS

The exposure to and training on seizure disorders were assessed using a structured questionnaire first. Then, they were tested with 20 videos consisting of 10 epileptic seizures (ESs) and 10 psychogenic nonepileptic seizures (PNESs). After each video, we asked three questions to test (a) the diagnosis and the practice of administration of benzodiazepines to terminate the seizure, (b) the estimation of seizure duration, and (c) the practice of intubation. The accuracy of diagnosis was measured by the area under the summary receiver operating characteristics curve (AUC). The difference between true seizure duration and estimated duration was evaluated using paired-sample t-test.

RESULTS

A total of 48 trainees participated in the study. The majority witnessed seizures in movies (37, 77.1%) and television (35, 72.9%). Only 12 (25%) received bedside teaching on seizure disorders. Their diagnostic accuracy of seizures was very poor (AUC=0.54). Participants significantly underestimated the duration of seizures. Thirty-five doctors made an illogical decision to intubate but not to terminate the seizure with intravenous benzodiazepine.

CONCLUSIONS

The diagnostic accuracy of seizures is poor among trainees, and their estimates of seizure duration are unreliable. Our study highlights potential pitfalls in the acute management of seizures and the need for more training on seizure disorders.

New insights into psychogenic nonepileptic seizures 2011-2014

PURPOSE

There has been a rapid increase in the rate of publications about psychogenic nonepileptic seizures (PNES). This review summarises insights from the 50 most important original articles about PNES published since 2011 and describes the advances made in the understanding of PNES over the last 3 years.

METHOD

We carried out a systematic literature search of all English language publications about PNES published between October 2011 and October 2014 on Scopus, Ovid Medline and Web of Knowledge, and inspected all abstracts. Having excluded all review articles, case reports, conference abstracts, articles exploring PNES in children, and articles not actually focussing on PNES, we considered 150 papers for inclusion in this review. We assessed the quality of the identified studies and used expert judgement to identify the 50 most important publications from the review period and composed a narrative review based on these original papers.

RESULTS

Almost one half of the studies initially identified only provided Class 4 evidence. Recent work has provided more support for a biopsychosocial account of PNES. It has illustrated the heterogeneity of PNES, identifying varying and distinct psychological profiles of individuals with this disorder. These findings suggest that intervention needs to be flexible or adaptive if it is appropriately to target the different mechanisms which may give rise to PNES. Several educational and psychotherapeutic interventions for PNES have been described, but sufficiently powered randomised controlled trials are yet to be undertaken. Recent research using social, economic and quality of life indicators has provided further evidence of the societal and individual burden of PNES.

CONCLUSION

The research into PNES published over the last 3 years has deepened our understanding of the condition as a biopsychosocial disorder which is neither a "physical" nor a "psychological" condition. A number of small studies have demonstrated the potential of educational and psychotherapeutic treatments, but rigorous and sufficiently large trials still need to be conducted to determine the effectiveness of these interventions.

Analyzing reliability of seizure diagnosis based on semiology

This study aimed to determine the accuracy of seizure diagnosis by semiological analysis and to assess the factors that affect diagnostic reliability. A total of 150 video clips of seizures from 50 patients (each with three seizures of the same type) were observed by eight epileptologists, 12 neurologists, and 20 physicians (internists). The videos included 37 series of epileptic seizures, eight series of physiologic nonepileptic events (PNEEs), and five series of psychogenic nonepileptic seizures (PNESs). After observing each video, the doctors chose the diagnosis of epileptic seizures or nonepileptic events for the patient; if the latter was chosen, they further chose the diagnosis of PNESs or PNEEs. The overall diagnostic accuracy rate for epileptic seizures and nonepileptic events increased from 0.614 to 0.660 after observations of all three seizures (p < 0.001). The diagnostic sensitivity and specificity of epileptic seizures were 0.770 and 0.808, respectively, for the epileptologists. These values were significantly higher than those for the neurologists (0.660 and 0.699) and physicians (0.588 and 0.658). A wide range of diagnostic accuracy was found across the various seizures types. An accuracy rate of 0.895 for generalized tonic-clonic seizures was the highest, followed by 0.800 for dialeptic seizures and then 0.760 for automotor seizures. The accuracy rates for myoclonic seizures (0.530), hypermotor seizures (0.481), gelastic/dacrystic seizures (0.438), and PNESs (0.430) were poor. The reliability of semiological diagnosis of seizures is greatly affected by the seizure type as well as the doctor's experience. Although the overall reliability is limited, it can be improved by observing more seizures.

Video-based training improves the accuracy of seizure diagnosis

BACKGROUND AND AIM

The difficulties in differentiating epileptic seizures (ES) from psychogenic non-epileptic seizures (PNES) are well known. However, interventions to enhance diagnostic accuracy have not been well studied. We sought to evaluate the accuracy of discrimination between ES and PNES before and after targeted training among medical students.

METHODS

A teaching module incorporating videos of typical ES and PNES was used for training. Typical ES and PNES, 10 each, were shown in a random mix. The participants were asked to make a diagnosis as the baseline test, followed by a detailed discussion on videos. One month later, a 1 h lecture was delivered on the diagnosis and classification of seizures, followed by two more tests 3 and 6 months later, using a similar format, but different videos. A group of emergency medicine trainees also went through the preteaching test for comparison. We used summary receiver operating characteristic curves and area under the curve (AUC) to quantify the discriminating ability and z scores to assess the differences between AUC between different stages of training.

RESULTS

In medical students, the AUC improved significantly from 0.52 (95% CI 0.49 to 0.55) at the baseline to 0.64 (95% CI 0.59 to 0.69, p<0.001) at 3 months and 0.63 (95% CI 0.57 to 0.69, p<0.001) at 6 months. At 3 and 6 months testing, they achieved results similar to that of emergency medicine trainees (p=0.5).

CONCLUSIONS

Targeted video-based training increases the accuracy of visual discrimination of seizures short-term and medium-term.

Common imitators of epilepsy

Many episodic phenomena involving motor, sensory, autonomic, and behavioral functions may imitate epilepsy. The aim of this article is to focus on the various manifestations and the diagnostic and therapeutic challenges of the most common of these disorders, as well as their relationship to emotional aspects. Syncope is caused by reduced global cerebral perfusion. Convulsive movements are sometimes released from subcortical structures, but do not follow the characteristic sequence of tonic-clonic seizures, and postictal symptoms are minimal. Hyperventilation attacks are caused by the metabolic consequences of hypocapnia. Altered blood pH and cerebral vasoconstriction may cause a range of peripheral and central nervous system symptoms. Psychogenic non-epileptic seizures (PNES) are attacks of reduced self-control associated with various behavioral phenomena, usually beyond voluntary control. A detailed clinical history is the most important tool in the differential diagnosis. Various emotional factors may act as immediate triggers in reflex syncope and hyperventilation attacks, whereas in PNES, emotional traumas may be remote and suppressed. Patient education with appropriate explanation of the underlying mechanisms is a fundamental part of the management of these disorders.

Psychiatry versus general physicians: who is better at differentiating epileptic from psychogenic non-epileptic seizures?

OBJECTIVE

To determine how accurately psychiatry and general medical doctors can differentiate epileptic and psychogenic non-epileptic seizures based on videotaped events (closest proxy to witnessed events). This study aims to establish how confidently this distinction can be made, the reasons why a particular diagnosis is reached, and inter-rater agreement.

METHODS

18 videos of patients demonstrating a heterogeneous mixture of epileptic and psychogenic non-epileptic seizures were collected and ordered in a random mix. These videos were shown to groups of general physicians, medical registrars and residents (n=19) as well as to psychiatrists and psychiatry registrars (n=8) who were provided with a questionnaire.

RESULTS

A total of 27 doctors participated in the study. The overall percentage of correct diagnoses was 55.4%. There were no significant differences in correct diagnosis rates between psychiatry and general medical doctors. There was poor inter-rater agreement (Kappa = 0.159). Neither group was particularly confident in reaching a diagnosis, and diverse reasons underpinned the diagnoses given.

CONCLUSION

Among the participants, merely observing an epileptic or non-epileptic event is insufficient to establish a definitive diagnosis. The results indicate poor diagnostic accuracy and agreement among psychiatry and general medical doctors. This may have important implications for both education and clinical practice.