Selim R. Benbadis

Semiological seizure classification

Summary: We propose an epileptic seizure classification based exclusively on ictal semiology. In this semiological seizure classification (SSC), seizures are classified as follows:

An estimate of the prevalence of psychogenic non-epileptic seizures.

The prevalence of psychogenic non-epileptic seizures is difficult to estimate. We propose an estimate based on a calculation. We used the following data, which are known or have been estimated, and are generally accepted. A prevalence of epilepsy of 0.5-1%; a proportion of intractable epilepsy of 20-30%; a percentage of these referred to epilepsy centers of 20-50%; and a percentage of patients referred to epilepsy centers that are psychogenic non-epileptic seizures: 10-20%. Using the low estimates, the prevalence of psychogenic non-epileptic seizures would be 1/50 000. Using the high estimates, the prevalence of psychogenic non-epileptic seizures would be 1/3000. The prevalence of psychogenic non-epileptic seizures is somewhere between 1/50 000 and 1/3000, or 2 to 33 per 100 000, making it a significant neurologic condition.

How many patients with psychogenic nonepileptic seizures also have epilepsy

The proportion of patients with psychogenic nonepileptic seizures (PNES) who also have epilepsy has been reported to vary from 10% to over 50%. The authors reviewed all 32 patients diagnosed with PNES in our EEG–video monitoring unit over a period of 1 year, and only 3 (9.4%) had interictal epileptiform discharges to support a coexisting diagnosis of epilepsy. Thus, the authors believe that only a small proportion of patients with PNES have coexisting epilepsy.

Epileptic seizures and syndromes.

This article describes the main characteristics of the different types of seizures and their classifications. The main types of epilepsies are reviewed, including their main, clinical, and EEG features and an overview of their treatment.

Outcome of Prolonged Video‐EEG Monitoring at a Typical Referral Epilepsy Center

Summary:  Purpose: When seizures do not respond to medications, video‐EEG monitoring is the best available diagnostic tool and is the principal activity of epilepsy centers. The purpose of this study was to analyze the eventual disposition of patients who undergo video‐EEG monitoring at a typical referral epilepsy center.

Association between the Epworth Sleepiness Scale and the Multiple Sleep Latency Test in a Clinical Population

No statistically or clinically significant association was seen between scores on the subjective Epworth Sleepiness Scale and results of the objective mean sleep latency test. These tests may evalu...

Overintepretation of EEGs and misdiagnosis of epilepsy.

&NA; The overinterpretation of EEGs is a known problem that has not been reported specifically. The authors report a series of EEGs on patients who were diagnosed eventually with psychogenic nonepileptic seizures and who had an EEG read as epileptiform. Of the 15 actual records available for review, the overread patterns were wicket spikes (n = 1), hypnagogic hypersynchrony (n = 1), and hyperventilationinduced slowing (n = 1). In the other 12 records, the overread patterns were simple fluctuations of sharply contoured background rhythms or fragmented &agr; activity. Rather than well‐described normal variants, the overinterpreted patterns tend to be normal fluctuations of background activity.

Interrater reliability of EEG-video monitoring

Objective: The diagnosis of psychogenic nonepileptic seizures (PNES) can be challenging. In the absence of a gold standard to verify the reliability of the diagnosis by EEG-video, we sought to assess the interrater reliability of the diagnosis using EEG-video recordings. Methods: Patient samples consisted of 22 unselected consecutive patients who underwent EEG-video monitoring and had at least an episode recorded. Other test results and histories were not provided because the goal was to assess the reliability of the EEG-video. Data were sent to 22 reviewers, who were board-certified neurologists and practicing epileptologists at epilepsy centers. Choices were 1) PNES, 2) epilepsy, and 3) nonepileptic but not psychogenic (“physiologic”) events. Interrater agreement was measured using a κ coefficient for each diagnostic category. We used generalized κ coefficients, which measure the overall level of between-method agreement beyond that which can be ascribed to chance. We also report category-specific κ values. Results: For the diagnosis of PNES, there was moderate agreement (κ = 0.57, 95% confidence interval [CI] 0.39–0.76). For the diagnosis of epilepsy, there was substantial agreement (κ = 0.69, 95% CI 0.51–0.86). For physiologic nonepileptic episodes, the agreement was low (κ = 0.09, 95% CI 0.02–0.27). The overall κ statistic across all 3 diagnostic categories was moderate at 0.56 (95% CI 0.41–0.73). Conclusions: Interrater reliability for the diagnosis of psychogenic nonepileptic seizures by EEG-video monitoring was only moderate. Although this may be related to limitations of the study (diagnosis based on EEG-video alone, artificial nature of the forced choice paradigm, single episode), it highlights the difficulties and subjective components inherent to this diagnosis.

Induction of psychogenic nonepileptic seizures without placebo.

Article abstract The diagnosis of psychogenic nonepileptic seizures (PNES) can only be made with EEG-video monitoring. The authors describe a provocative technique without placebo. Patients with a clinical suspicion for PNES underwent an activation procedure using suggestion, hyperventilation, and photic stimulation. Of 19 inductions performed, 16 (84%) were successful in inducing the habitual episode. The authors’ technique had a sensitivity comparable to those using placebo (e.g., saline injection), but does not have disadvantages.

Outpatient seizure identification : results of 502 patients using computer-assisted ambulatory EEG

Summary Patients with epilepsy may not always be able to identify their seizures. Epilepsy management relies on patient reporting to validate whether seizures occur during treatment. The goal of this study was to assess the frequency of unreported seizures recorded during routine outpatient ambulatory EEG recording. The authors reviewed 552 records from 502 patients who underwent outpatient 16-channel computer-assisted ambulatory EEG monitoring (CAA-EEG). Seizure identification was evaluated by assessing push-button activation. Partial seizures were seen most commonly. A total of 47 of 552 records (8.5%) had partial seizures recorded on CAA-EEG, with 29 of 47 (61.7%) with electroclinical seizures identified by push-button activation. Seizures on EEG without push-button activation were analyzed separately and compared with a self-reported written diary to verify lack of recognition. A total of 18 of 47 records (38.3%) had some partial seizures that were unrecognized by the patient, and 11 of 47 records (23.4%) had seizures recognized only by the computer. The authors conclude that patients frequently have seizures outside of the hospital that go unrecognized. Underreporting of seizure frequency occurs in the outpatient setting and impacts optimal diagnosis and treatment for patients with epilepsy.