"Convulsive" nonepileptic seizures have a characteristic pattern of rhythmic artifact distinguishing them from convulsive epileptic seizures

Psychogenic non-epileptic seizures (PNES) in the context of concurrent epilepsy – making the right diagnosis

Epilepsy is a risk factor for the development of psychogenic non-epileptic seizures (PNES) and comorbid epilepsy is recognized as a comorbidity in about 10–30% of patients with PNES. The combination of epileptic and nonepileptic seizures poses a particular diagnostic challenge. In patients with epilepsy, additional PNES may be suspected on the basis of their typical semiology. The possibility of additional PNES should also be considered if seizures fail to respond to antiepileptic drug treatment, in patients with frequent emergency admissions with seizures and in those who develop new types of seizures. The description of semiological details by patients and witnesses can suggest additional PNES. Home video recordings can support an initial diagnosis, however, especially in patients with mixed seizure disorders it is advisable to seek further diagnostic confirmation by capturing all habitual seizure types with video-EEG. The clinical features of PNES associated with epilepsy are similar to those in isolated PNES disorders and include longer duration, fluctuating course, asynchronous movements, pelvic thrusting, side-to-side head or body movement, persistently closed eyes and mouth, ictal crying, recall of ictal experiences and absence of postictal confusion. PNES can also present as syncope-like episodes with unresponsiveness and reduced muscle tone. There is no unique epileptological or brain pathology profile putting patients with epilepsy at risk of additional PNES. However, patients with epilepsy and PNES typically have lower educational achievements and higher levels of psychiatric comorbidities than patients with epilepsy alone. Psychological trauma, including sexual abuse, appears to be a less relevant aetiological factor in patients with mixed seizure disorders than those with isolated PNES, and the gender imbalance (i.e. the greater prevalence in women) is less marked in patients with PNES and additional epilepsy than those with PNES alone. PNES sometimes develop after epilepsy surgery. A diagnosis of ‘known epilepsy’ should never be accepted without (at least brief) critical review. This narrative review summarises clinical, electrophysiological and historical features that can help identify patients with epilepsy and additional PNES.

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.

Psychogenic non-epileptic seizures in children

Psychogenic Non-Epileptic Seizures (PNES) are a relatively common condition in children. While their clinical presentation resembles epileptic seizures, the underlying cause for PNES involves a multitude of bio-psychosocial factors. Patients may be misdiagnosed with epilepsy and subjected to unnecessary treatments, often delaying the diagnosis for years. A strong understanding of its symptomatology is essential for diagnosis of PNES. Successful management depends on effective teamwork that involves the neurologist as well as mental health professionals. This paper reviews the various aspects of PNES in children with emphasis on the clinical presentation, diagnosis as well as the underlying psychological basis and treatment.

Long-term Home Video EEG for Recording Clinical Events

SUMMARY

Around 50 years after the first EEG acquisition by Hans Berger, its use in ambulatory setting was demonstrated. Ever since, ambulatory EEG has been widely available and routinely used in the United States (and to a lesser extent in Europe) for diagnosis and management of patients with epilepsy. This technology alone cannot help with semiological characterization, and absence of video is one of its main drawbacks. Addition of video to ambulatory EEG potentially improves diagnostic yield and opens new aspects of utility for better characterization of patient's events, including differential diagnosis, classification, and quantification of seizure burden. Studies evaluating quality of ambulatory video EEG (aVEEG) suggest good quality recordings are feasible. In the utilization of aVEEG, to maximize yield, it is important to consider pretest probability. Having clear pretest questions and a strong index of suspicion for focal, generalized convulsive or non-epileptic seizures further increases the usefulness of aVEEG. In this article, which is part of the topical issue "Ambulatory EEG," the authors compare long-term home aVEEG to inpatient video EEG monitoring, discuss aVEEG's use in diagnosis and follow-up of patients, and present the authors' own experience of the utility of aVEEG in a teaching hospital setting.

Epidemiology of Functional Seizures Among Adults Treated at a University Hospital

Importance

Functional seizures (formerly psychogenic nonepileptic seizures), paroxysmal episodes that are often similar to epileptic seizures in their clinical presentation and display no aberrant brain electrical patterns, are understudied. Patients experience a long diagnostic delay, few treatment modalities, a high rate of comorbidities, and significant stigma due to the lack of knowledge about functional seizures.

Objective

To characterize the clinical epidemiology of a population of patients with functional seizures observed at Vanderbilt University Medical Center (VUMC).

Design, Setting, and Participants

This case-control study included patients with functional seizures identified in the VUMC electronic health record (VUMC-EHR) system from October 1989 to October 2018. Patients with epilepsy were excluded from the study and all remaining patients in the VUMC medical center system were used as controls. In total, the study included 1431 patients diagnosed with functional seizures, 2251 with epilepsy and functional seizures, 4715 with epilepsy without functional seizures, and 502 200 control patients who received treatment at VUMC for a minimum of a 3 years. Data were analyzed from November 2018 to March 2020.

Exposure

Diagnosis of functional seizures, as identified from the VUMC-EHR system by an automated phenotyping algorithm that incorporated International Classification of Diseases, Ninth Revision (ICD-9) codes, International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes, Current Procedural Terminology codes, and natural language processing.

Main Outcomes and Measures

Associations of functional seizures with comorbidities and risk factors, measured in odds ratios (ORs).

Results

Of 2 346 808 total patients in the VUMC-EHR aged 18 years or older, 3341 patients with functional seizures were identified (period prevalence, 0.14%), 1062 (74.2%) of whom were women and for which the median (interquartile range) age was 49.3 (39.4-59.9) years. This assessment replicated previously reported associations with psychiatric disorders including posttraumatic stress disorder (PTSD) (OR, 1.22; 95% CI, 1.21-1.24; P < 3.02 × 10-5), anxiety (OR, 1.14; 95% CI, 1.13-1.15; P < 3.02 × 10-5), and depression (OR, 1.14; 95% CI, 1.13-1.15; P < 3.02 × 10-5), and identified novel associations with cerebrovascular disease (OR, 1.08; 95% CI, 1.06-1.09; P < 3.02 × 10-5). An association was found between functional seizures and the known risk factor sexual assault trauma (OR, 10.26; 95% CI, 10.09-10.44; P < 3.02 × 10-5), and sexual assault trauma was found to mediate nearly a quarter of the association between female sex and functional seizures in the VUMC-EHR.

Conclusions and Relevance

This case-control study found evidence to support previously reported associations, discovered new associations between functional seizures and PTSD, anxiety, and depression. An association between cerebrovascular disease and functional seizures was also found. Results suggested that sexual trauma may be a mediating factor in the association between female sex and functional seizures.

A New Method Based on CEEMD Combined With Iterative Feature Reduction for Aided Diagnosis of Epileptic EEG

In the clinical diagnosis of epileptic diseases, the intelligent diagnosis of epileptic electroencephalogram (EEG) signals has become a research focus in the field of brain diseases. In order to solve the problem of time-consuming and easily influenced by human subjective factors, artificial intelligence pattern recognition algorithm has been applied to EEG signals recognition. However, at present, the common empirical mode decomposition (EMD) signal decomposition algorithm does not consider the problem of mode aliasing. The EEG features obtained by feature extraction may be mixed with some unimportant features that affect the classification accuracy. In this paper, we proposed a new method based on complementary ensemble empirical mode decomposition (CEEMD) combined with iterative feature reduction for aided diagnosis of epileptic EEG. First of all, the evaluation indexes of decomposing and reconstructing signals by several methods were compared. The CEEMD was selected as the decomposition method of the signals. Then, the support vector machine recursive elimination (SVM-RFE) was used to reduce 9 features extracted from EEG data. The support vector classification of the gray wolf optimizer (GWO-SVC) recognition model was established for different feature subsets. By comparing the classification accuracy of training set and test set of different feature subsets, and considering the complexity of the model reflected by the number of features selected by SVM-RFE, the analysis showed that the 6 feature subsets with fewer features and higher classification accuracy could reflect the key information of epileptic EEG. The accuracy of the training set classification was 99.38% and the test set was as high as 100%. The recognition time was only 1.6551 s. Finally, in order to verify the reliability of the algorithm proposed in this paper, the proposed algorithm compared with the classification model established by the raw EEG signals and the optimization model established by other intelligent optimization algorithms. It is found that the algorithm used in this paper has higher classification accuracy and faster recognition time than other processing methods. The experimental results show that CEEMD combined with SVM-RFE is feasible for rapid and accurate recognition of EEG signals, which provides a theoretical basis for the aided diagnosis of epilepsy.

EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features

Epilepsy and psychogenic non-epileptic seizures (PNES) often show over-lap in symptoms, especially at an early disease stage. During a PNES, the electrical activity of the brain remains normal but in case of an epileptic seizure the brain will show epileptiform discharges on the electroencephalogram (EEG). In many cases an accurate diagnosis can only be achieved after a long-term video monitoring combined with EEG recording which is quite expensive and time-consuming. In this paper using short-term EEG data, the classification of epilepsy and PNES subjects is analyzed based on signal, functional network and EEG microstate features. Our results showed that the beta-band is the most useful EEG frequency sub-band as it performs best for classifying subjects. Also the results depicted that when the coverage feature of the EEG microstate analysis is calculated in beta-band, the classification shows fairly high accuracy and precision. Hence, the beta-band and the coverage are the most important features for classification of epilepsy and PNES patients.

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.

The utility of an automated and ambulatory device for detecting and differentiating epileptic and psychogenic non-epileptic seizures

OBJECTIVE

Accurate differentiation between epileptic seizures (ES) and psychogenic non-epileptic seizures (PNES) can be challenging based on history alone. Inpatient video EEG monitoring (VEM) is often needed for a definitive diagnosis. However, VEM is highly resource intensive, is of limited availability, and cannot be undertaken over long periods. Previous research has shown that time-frequency analysis of accelerometer data could be utilized to differentiate between ES and PNES. Using a seizure detection and classification algorithm, we sought to examine the diagnostic utility of an automated analysis with an ambulatory accelerometer.

METHODS

A wrist-worn device was used to collect accelerometer data from patients during VEM admission, for diagnostic evaluation of convulsive seizures. An automated process, that involved the use of K-means clustering and support vector machines, was used to detect and classify each seizure as ES or PNES. The results were compared with VEM diagnoses determined by epileptologists blinded to the accelerometer data.

RESULTS

Twenty-four convulsive seizures, consisting of at least 20 seconds of sustained continuous activity, recorded from 11 patients during inpatient VEM (13 PNES from five patients and 11 ES from six patients) were included for analysis. The automated system detected all convulsive seizures (ES, PNES) from >661 hours of recording with 67 false alarms (2.4 per 24 hours). The sensitivity and specificity for classifying ES from PNES were 72.7% and 100%, respectively. The positive and negative predictive values for classifying PNES were 81.3% and 100%, respectively. There was no significant difference between the classification results obtained from the automation process and the VEM diagnoses.

SIGNIFICANCE

This automated system can potentially provide a wearable out-of-hospital seizure diagnostic monitoring system.

Predicting psychogenic non-epileptic seizures from serum levels of neuropeptide Y and adrenocorticotropic hormone

OBJECTIVE

Patients with psychogenic non-epileptic seizures (PNES) may present with convulsive events that are not accompanied by epileptiform brain activity. Video-electroencephalography (EEG) monitoring is the gold standard for diagnosis, yet not all patients experience convulsive episodes during video-EEG sessions. Hence, we aimed to construct a predictive model in order to detect PNES from serum hormone levels, detached from an evaluation of patients' convulsive episodes.

METHODS

Fifteen female patients with PNES and 60 healthy female controls participated in the study, providing blood samples for hormone analysis. A binomial logistic regression model and the leave-one-out cross-validation were employed.

RESULTS

We found that levels of neuropeptide Y and adrenocorticotropic hormone were the optimal combination of predictors, with over 90% accuracy (area under the curve=0.980).

CONCLUSIONS

The ability to diagnose PNES irrespective of convulsive events would represent an important step considering its feasibility and affordability in daily clinical practice.

A systematic study of stereotypy in epileptic seizures versus psychogenic seizure-like events

OBJECTIVE

The objective of this study was to quantify the features of stereotypy in epileptic seizures and compare it with that of stereotypy in psychogenic nonepileptic seizure-like events (PNES) confirmed by video-electroencephalography (VEEG) monitoring.

METHODS

Video-electroencephalography monitoring records of 20 patients with temporal lobe seizures (TLS) and 20 with PNES were retrospectively reviewed (n = 138 seizures, 48 TLS and 90 PNES). We analyzed the semiology of 59 behaviors of interest for their presence, duration, sequence, and continuity using quantified measures that were entered into statistical analysis.

RESULTS

We identified discontinuity as the parameter that was clearly distinct between PNES and epileptic TLS events: there were significantly more frequent pauses of behavior (i.e., "on-off" pattern) in PNES compared with TLS (P = 0.012). The frequency of pauses during an event was diagnostic of PNES events. For instance, the presence of 2 "pauses" during an episode determines a 69% probability of the seizure being nonepileptic. Moreover, PNES events had significantly greater duration (143 s) than TLS events (68 s) (excluding outliers, P = 0.002) and greater duration variability from one event to another in the same subject (P = 0.005).

SIGNIFICANCE

Our work provides the first quantified measure of behavioral semiology during epileptic and nonepileptic seizures and offers novel behavioral measures to differentiate them from each other.

Novel features for capturing temporal variations of rhythmic limb movement to distinguish convulsive epileptic and psychogenic nonepileptic seizures

OBJECTIVE

To investigate the characteristics of motor manifestation during convulsive epileptic and psychogenic nonepileptic seizures (PNES), captured using a wrist-worn accelerometer (ACM) device. The main goal was to find quantitative ACM features that can differentiate between convulsive epileptic and convulsive PNES.

METHODS

In this study, motor data were recorded using wrist-worn ACM-based devices. A total of 83 clinical events were recorded: 39 generalized tonic-clonic seizures (GTCS) from 12 patients with epilepsy, and 44 convulsive PNES from 7 patients (one patient had both GTCS and PNES). The temporal variations in the ACM traces corresponding to 39 GTCS and 44 convulsive PNES events were extracted using Poincaré maps. Two new indices-tonic index (TI) and dispersion decay index (DDI)-were used to quantify the Poincaré-derived temporal variations for every GTCS and convulsive PNES event.

RESULTS

The TI and DDI of Poincaré-derived temporal variations for GTCS events were higher in comparison to convulsive PNES events (P < 0.001). The onset and the subsiding patterns captured by TI and DDI differentiated between epileptic and convulsive nonepileptic seizures. An automated classifier built using TI and DDI of Poincaré-derived temporal variations could correctly differentiate 42 (sensitivity: 95.45%) of 44 convulsive PNES events and 37 (specificity: 94.87%) of 39 GTCS events. A blinded review of the Poincaré-derived temporal variations in GTCS and convulsive PNES by epileptologists differentiated 26 (sensitivity: 70.27%) of 44 PNES events and 33 (specificity: 86.84%) of 39 GTCS events correctly.

SIGNIFICANCE

In addition to quantifying the motor manifestation mechanism of GTCS and convulsive PNES, the proposed approach also has diagnostic significance. The new ACM features incorporate clinical characteristics of GTCS and PNES, thus providing an accurate, low-cost, and practical alternative to differential diagnosis of PNES.

[Psychogenic non epileptic seizures : Differential diagnostic features]

Psychogenic nonepileptic seizures (PNES) are to be considered in the differential diagnosis of a transient loss of consciousness. Their discrimination from syncope, epileptic seizures or vascular events can be difficult and requires profound knowledge about the semiology and clinical presentation of PNES and their differential diagnoses. Erroneous diagnoses and the resulting therapies lead to elevated morbidity, elevated costs and a poorer outcome. The aim of the present article is to provide an overview on PNES and their delineation from the clinical pictures of epilepsy and syncope.

Psychogenic non-epileptic seizures: aetiology, diagnosis and management

Psychogenic non-epileptic seizures (PNES) have a significant impact on most patients in terms of distress, disability, loss of income and iatrogenic harm. Three-quarters of patients with PNES are initially misdiagnosed and treated for epilepsy. Misdiagnosis exposes patients to multiple iatrogenic harms and prevents them from accessing psychological treatment. In most cases, the patient's history (and witness accounts) should alert clinicians to the likely diagnosis of PNES. Since this diagnosis may be resisted by patients and may involve ‘un-diagnosing’ epilepsy, video-electroencephalogram recording of typical seizures is often helpful. The underlying causes of PNES are diverse: a model combining predisposing, precipitating and perpetuating factors is a useful way of conceptualising their aetiology. The initial step of treatment should be to limit iatrogenic harm. There is some evidence for the effectiveness of psychotherapy.

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.

Stereotypy of psychogenic nonepileptic seizures

Psychogenic nonepileptic seizures (PNES) are defined as paroxysmal episodes in which epileptic semiology features are manifested, without the characteristic concomitant electrical discharges seen in epileptic seizures. Although many studies have dealt with semiologic classification of PNES, most of the studies did not raise the question of consistency of PNES in the same patient. The aim of this study was to measure the degree of consistency of PNES among individual patients. We retrospectively reviewed medical records and video- EEG records of all adult patients who underwent monitoring in our center from August 1st 2013 to May 31st 2015. Those who were diagnosed with PNES with or without a background of epilepsy were selected for this study. In order to check consistency between seizures, we analyzed patients who had more than one recorded seizure during monitoring. In case of more than 2 recorded seizures, the first two seizures were analyzed. We found 53 patients who had PNES during this period, 29 of them had more than one seizure. All seizures in the same patient were in the same semiology category. In patients with either motor rhythmic or complex motor seizures, we found a main anatomical region involved. The main anatomical region involved was the same in 13 out of 14 patients. Movement frequency was highly similar between the seizures of the same patient, while duration of seizures was significantly different. Despite significant differences in duration between the first and second seizure in patients with PNES, all other aspects tested were highly similar. This shows that recurrent PNES in the same patient are stereotypic. This supports the hypothesis that PNES is probably a dissociative disorder.

Neurologic diagnostic criteria for functional neurologic disorders

The diagnosis of functional neurologic disorders can be challenging. In this chapter we review the diagnostic criteria and rating scales reported for functional/psychogenic sensorimotor disturbances, psychogenic nonepileptic seizures (PNES) and functional movement disorders (FMD). A recently published scale for sensorimotor signs has some limitations, but may help in the diagnosis, and four motor and two sensory signs have been reported as highly reliable. There is good evidence using eight specific signs for the differentiation of PNES from seizures. Recently, diagnostic criteria were developed for PNES; their sensitivity and specificity need to be evaluated. The definitive diagnosis of PNES can be made by recording typical positive features during the spells, and in a low proportion of cases, where the distinction with an organic etiology cannot easily be done, a normal electroencephalogram suggests the diagnosis. FMD diagnosis relies on diagnostic criteria, which have been refined over time and may be supplemented by laboratory tests in some phenotypes. Rating scales for PNES and FMD could be useful for severity measures, but several limitations remain to be addressed.

Dissociative symptoms and epilepsy

This article considers the relationship between various types of dissociative symptoms, including symptoms of depersonalization, derealization, and conversion disorders, and epilepsy. After introductory remarks concerning dissociation, this relationship is discussed through two main themes: firstly, the phenomenology and mechanisms of so-called 'dreamy states' in epilepsy and their closest analogs in psychiatric disorders, and secondly, the similarities and differences between epileptic seizures and psychogenic nonepileptic attacks. Although epileptic and dissociative symptoms may appear similar to observers, they arise through different mechanisms and have different experiential qualities.

Psychogenic nonepileptic seizures

Treatment for PNES must be individualized. A combination of approaches is probably the most beneficial for improvement. Treatment should not simply emphasize removing maladaptive PNES behaviour, but should also focus on learning new coping skills and removing secondary gains. If PNES persist, therapy should be re-evaluated.

Resetting of brain dynamics: epileptic versus psychogenic nonepileptic seizures

We investigated the possibility of differential diagnosis of patients with epileptic seizures (ES) and patients with psychogenic nonepileptic seizures (PNES) through an advanced analysis of the dynamics of the patients' scalp EEGs. The underlying principle was the presence of resetting of brain's preictal spatiotemporal entrainment following onset of ES and the absence of resetting following PNES. Long-term (days) scalp EEGs recorded from five patients with ES and six patients with PNES were analyzed. It was found that: (1) Preictal entrainment of brain sites was reset at ES (P<0.05) in four of the five patients with ES, and not reset (P=0.28) in the fifth patient. (2) Resetting did not occur (p>0.1) in any of the six patients with PNES. These preliminary results in patients with ES are in agreement with our previous findings from intracranial EEG recordings on resetting of brain dynamics by ES and are expected to constitute the basis for the development of a reliable and supporting tool in the differential diagnosis between ES and PNES. Finally, we believe that these results shed light on the electrophysiology of PNES by showing that occurrence of PNES does not assist patients in overcoming a pathological entrainment of brain dynamics. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.

Psychogenic non-epileptic seizures: a challenging entity

Psychogenic non-epileptic seizures (PNES) are commonly encountered in neurologic practice. They are often misdiagnosed as epileptic seizures and treated as such for several years before a correct diagnosis is established. Such a misdiagnosis has the potential to expose patients to undue risk through several anti-epileptic drugs (AEDs). Patients are also affected in other ways, such as by financial consequences and the limitation of certain daily activities. In this review, we present the contemporary opinion of PNES with attention to clinically relevant salient features and management strategies.

Psychogenic nonepileptic seizures: answers and questions

Psychogenic nonepileptic seizures (PNES) superficially resemble epileptic seizures, but are not associated with ictal electrical discharges in the brain. PNES constitute one of the most important differential diagnoses of epilepsy. However, despite the fact they have been recognized as a distinctive clinical phenomenon for centuries and that access to video/EEG monitoring has allowed clinicians to make near-certain diagnoses for several decades, our understanding of the etiology, underlying mental processes, and, subsequently, subdifferentiation, nosology, and treatment remains seriously deficient. Emphasizing the clinical picture throughout, the first part of this article is intended to "look and look again" at what we know about the epidemiology, semiology, clinical context, treatment, and prognosis of PNES. The second part is dedicated to the questions that remain to be answered. It argues that the most important reason our understanding of PNES remains limited is the focus on the visible manifestations of PNES or the seizures themselves. In contrast, subjective seizure manifestations and the biographic or clinical context in which they occur have been relatively neglected.

Generalized tonic-clonic seizures detected by implantable loop recorder devices: diagnosing more than cardiac arrhythmias

BACKGROUND

Both syncope and seizures are important causes of recurrent, unexplained episodes of loss of consciousness. Implantable loop recorders have identified serious arrhythmias in patients with repeated syncope; however, implantable loop recorder detection of seizures is less well established.

OBJECTIVES

The purpose of this study was to provide in-depth analysis of a characteristic myopotential pattern recorded by implantable loop recorders during generalized tonic-clonic seizures.

METHODS

Fourteen patients with refractory, video-EEG-documented epilepsy (complex partial, atonic, tonic, or generalized tonic-clonic seizures) underwent implantable loop recorder placement as part of a study protocol evaluating cardiac rhythm abnormalities in patients at high risk for sudden unexpected death in epilepsy.

RESULTS

Twelve generalized tonic-clonic seizure episodes were detected by the implantable loop recorder in six patients. Implantable loop recorder and EEG recordings of generalized tonic-clonic seizures were identical and revealed a tonic phase (sustained, rapid, high-frequency myopotentials) transitioning to a clonic phase (periodic bursts of high-frequency myopotentials with a decelerating burst frequency from 3-6 Hz to 1-2 Hz) prior to seizure termination. With the nonprogrammable bandpass filter of 0.85 to 32 Hz in the implantable loop recorder, all generalized tonic-clonic seizure episodes had escaped automatic detection and required activation by family members. None of the 76 nongeneralized tonic-clonic seizure episodes recorded on the implantable loop recorder in the 14 patients exhibited the stereotypical tonic-clonic pattern that defines generalized seizures.

CONCLUSION

Recognizing this specific myopotential pattern on an implantable loop recorder might help diagnose generalized tonic-clonic seizures as a cause of recurrent, unexplained episodes of loss of consciousness. Having a programmable bandpass filter in the implantable loop recorder might increase its diagnostic yield for such patients.