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Summary: Purpose: Approximately 30% of patients admitted for video-EEG monitoring have psychogenic nonepileptic seizures (PNES). Differentiation of “convulsive” PNES from convulsive seizures can be difficult. The EEG often displays rhythmic movement artifact that may resemble seizure activity and confound the interpretation. We sought to determine whether time–frequency mapping of the rhythmic EEG artifact during “convulsive” PNES reveals a pattern that differs from that of epileptic seizures.
Methods: EEGs from 15 consecutive patients with “convulsive” PNESs were studied with time–frequency mapping by using NEUROSCAN and compared with 15 patients with convulsive epileptic seizures. Fast Fourier transforms (FFTs) were performed to determine the dominant frequency for 1- to 2-s windows every 2 s through the seizures.
Results: The dominant frequency remained stable within a narrow range for the duration of the PNES, whereas in the epileptic seizures, it evolved through a wide range. The coefficient of variation of the frequency during the seizures was considerably less for patients without epilepsy (median, 15.0%; range, 7.2–23.7% vs. median, 58.0%; range, 34.8–92.1%; p < 0.001). The median frequency did not differ significantly between groups (4.2 vs. 4.6 Hz; p = 0.290).
Conclusions: “Convulsive” PNES display a characteristic pattern on time–frequency mapping of the EEG artifact, with a stable, nonevolving frequency that is different from the evolving pattern seen during an epileptic seizure.
Individuals with psychogenic nonepileptic seizures (PNES) have recurrent episodes of altered movement, sensation, or experience that resemble epileptic seizures but are not associated with abnormal electrical activity in the brain (1). The etiology of PNES remains unclear; however, they are presumed to relate to underlying psychogenic disturbances, with multiple factors including personality traits playing a role in both etiology and prognosis (2).
PNES represent a common diagnostic and management problem, not just for the neurologist, but also for general practitioners, emergency departments, and other treating physicians. The estimated prevalence of PNES is between 2 to 33 per 100,000 individuals, making PNES one of the more common conversion disorders in the community (3). It has been reported that between 11 and 54% of patients admitted for inpatient video-EEG monitoring (VEM) are diagnosed as having PNES (1,4–7). A significant proportion of patients requiring intubation for status epilepticus are ultimately diagnosed with PNESs, resulting in potentially serious morbidity to the patient, and considerable cost to the community (8).
The differentiation between epileptic and nonepileptic seizures can be difficult. Seizure semiology is important in the diagnostic algorithm, and a plethora of features have been reported as being more likely associated with PNES. These have included a stable ictal heart rate (9), induction of the event with suggestion (10), eyes closed versus open, pelvic thrusting or “no-no” head shaking during the event (11), and even the presence of a teddy bear brought in during EEG monitoring (12). However, none of these features alone is diagnostic of PNES, as all can be seen on occasions during epileptic seizures. VEM is the investigation of choice in confirming the diagnosis of seizure disorders. However, even with EEG monitoring, the diagnosis may not be straightforward. The rhythmic movement artifact often obscures the EEG during a seizure, which can confound interpretation. Rhythmic movement artifact may also resemble spike–wave abnormality on the EEG, making the distinction between epileptic and nonepileptic seizure disorder difficult (13) (Fig. 1). Additionally, VEM monitoring is highly resource and labor intensive and therefore is relatively expensive and limited in availability. More definitive diagnostic methods for PNES are required, particularly those that may be applicable outside of the VEM setting.
Figure 1. Segment of an EEG during a psychogenic nonepileptic seizure, with rhythmic movement artifact resembling spike–wave discharges.
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We have observed that the evolution of the frequency of the movements and rhythmic EEG artifact in PNES differs from that in convulsive epileptic seizures, and this may be a reliable and practical differentiating feature. However, no study has systematically investigated the EEG patterns and rhythmic movement artifact across PNES and epilepsy groups. We have hypothesized that the frequency remains stable over time in PNES, but evolves over time in convulsive epileptic seizures. The aim of this study was to determine whether time–frequency mapping of the rhythmic EEG artifact during “convulsive” PNES revealed a pattern that differed from that seen during epileptic seizures.
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The distinction between epileptic and nonepileptic seizures is an important one with significant implications both for treatment and prognosis. The correct diagnosis allows potentially toxic antiepileptic drugs (AEDs) to be ceased and also saves hospital and medical resources being unnecessarily used in the mistaken belief that the patient is having uncontrolled epileptic seizures. Diagnosis of PNES results in a substantial reduction of direct medical costs (15). The correct diagnosis of PNES also creates the opportunity for referral for appropriate psychiatric assessment and treatment of psychological issues that may underlie the events. Long-term follow-up studies have shown that although the outcome of PNES is variable, poor prognostic features include older age at PNES onset and diagnosis, positive motor features, the presence of tongue biting and incontinence, and more extreme scores on measures of personality pathology in areas of emotional dysregulation, inhibitedness, and compulsivity (2).
Access to limited resources such as video-EEG monitoring is not uniform, and the clinical uncertainty of treating physicians often leads to delay in accurate diagnosis. This delay can further confound the management issues, as ≤80% of patients with PNES have been treated with AEDs before diagnosis, and ≤20% have a history of pseudostatus epilepticus (16).
The concept of evolution of frequency of the rhythmic activity during an epileptic seizure is well described (17,18). Epileptic seizure activity often evolves through many frequencies, from delta-range right through to beta-range frequencies over the course of a single seizure and is evident both from the ictal EEG and direct observation of the patient. This pattern has been demonstrated from scalp electrodes as well as intracranial monitoring and is a useful tool to analyze and categorize seizure disorders further. Two patterns of evolution of frequency of the EEG activity on time–frequency mapping were demonstrated during convulsive epileptic seizures in this study. In the most common pattern, the frequency started low in the delta range, steadily increased into the alpha/beta ranges, and then gradually decreased again back into the delta range before ceasing (Fig. 4B). In the second pattern, the seizure started with a fast frequency discharge in the alpha/beta range, which then gradually slowed into the delta range by the end of the seizure (Fig. 4C).
This study demonstrates that unlike epileptic seizures, convulsive PNES have a stable frequency of rhythmic movements, which produces a stable rhythmic artifact on the EEG. Analysis of this pattern reveals that patients with PNES have a frequency of movement that remains relatively constant throughout the seizure, with little variation or evolution. Our study demonstrated that this feature is 100% sensitive and specific for PNES, although this finding may possibly be related to the sample size. We are currently validating the diagnostic accuracy of time–frequency mapping with a larger prospective study. Whereas a potential confounding variable in this study is the use of the EEG in the diagnosis of the seizure disorder, the diagnosis of PNES was made predominantly by the seizure semiology on video and confirmed by history, epileptologist, and neuropsychiatric opinion. Although the EEG was analyzed for concomitant epileptiform activity, it was not paramount in the diagnostic algorithm, and patients were not diagnosed with PNES based on the EEG. Another characteristic finding among PNES was the presence of brief pauses in rhythmic movement during a seizure, lasting between 5 and 70 s. These pauses were followed by resumption of movement at the same frequency: the “on-off-on” pattern. No such pauses were observed during epileptic events. The explanation for this pattern during PNES may be that an individual has a set fundamental frequency at which subconscious or conscious rhythmic movement preferentially occurs. Therefore when the person resumes movements after a break, the frequency returns to this same frequency. In contrast, during epileptic seizures, the limb movements are driven by the frequency of the evolving seizure discharge, which overrides the normal motor control mechanisms.
Time–frequency mapping can reliably distinguish epileptic from nonepileptic seizures and remove any uncertainty with respect to EEG artifact or epileptiform activity. To our knowledge, this is the first study using time–frequency mapping of the EEG to document this stable frequency of movement in PNES. This pattern is unlike that seen in any convulsive epileptic seizure and may be a useful tool, both electrographically and at the bedside, to distinguish between epileptic and nonepileptic events. Clinically, this characteristic of PNES may be particularly useful in situations in which high-quality EEG services are not immediately available. A careful examination of the frequency of the limb movements, noting nonevolution, and in particular, if the “on-off-on” pattern is present, may allow correct diagnosis of PNES to be made before any potentially dangerous interventions being instituted. If necessary, the clinical observation could be confirmed by examining the frequency of the movement artifact on the rhythm strip of an ECG recording.
In this study we found complete separation of CV values for each group in the range of 23.7 to 34.8. We used the lower of these two values as the test cut-point simply for convenience. For the purpose of validation, we propose that initially a cut-point CV of 30 be used (i.e., approximate mid-point between 23.7 and 34.8). If subsequent studies show some overlap of CV values for each group, the cut-point can be adjusted to maximize sensitivity or specificity or both.
Accurate diagnosis of PNES is essential for both the long-term management of the patient and harm minimization. Unlike other clinical features of convulsive PNES, this study demonstrated that time–frequency mapping of the rhythmic movements has the potential to be used as a reliable diagnostic tool in the accurate classification of seizure disorders. A future application of these findings may be the use of a device to monitor the frequency of movements during events that will allow the outpatient diagnosis of PNES, avoiding the need for expensive inpatient VEM.