Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Naganur VD

PROVIDER: S-EPMC6546070 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Publications

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

Naganur Vaidehi D VD Kusmakar Shitanshu S Chen Zhibin Z Palaniswami Marimuthu S MS Kwan Patrick P O'Brien Terence J TJ

Epilepsia open 20190513 2

<h4>Objective</h4>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 ...[more]

PMID: 31168498

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Project description:ObjectiveTo study the outcome of patients with psychogenic non-epileptic seizures (PNES) after their diagnosis in the epilepsy monitoring unit (EMU).MethodsPatients diagnosed in our EMU with definite PNES between January 2009 and May 2023 were contacted by phone, and those who agreed to participate were asked a set of predetermined questions. Comparative analyses were carried out on several variables before and after diagnosis: number of participants with daily PNES, number of visits to the emergency department, number of participants who consulted their general practitioner or a neurologist outside of a scheduled follow-up, number of participants who took antiseizure medications (ASMs) or psychotropic drugs, and employment status.ResultsOut of the 103 patients with a definite diagnosis of PNES, 61 patients (79% female) accepted to participate in our study. The median age at PNES onset was 35 years, and the median delay to diagnosis was 3 years. Almost two-thirds (62%) were receiving ASMs and 40% psychotropic drugs. The mean stay at the EMU was 5 days. PNES diagnosis was explained to almost all patients (97%) by the end of their EMU stay and was well-accepted by most (89%). When contacted, 46% of participants no longer had PNES; 32% mentioned that their PNES had ceased immediately upon communication of the diagnosis. The median follow-up duration was 51 months. Fewer patients had daily seizures after the diagnosis (18 vs. 38%; p < 0.0455). Similarly, the median number of emergency department visits was significantly lower (0 vs. 2; p < 0.001). Only 17 patients consulted their general practitioner (vs. 40, p < 0.001) and 20 a neurologist (vs. 55, p < 0.001) after a PNES attack outside of a scheduled follow-up. The use of ASMs was also significantly reduced from 70 to 33% (p < 0.01), with only one still taking an ASM for its antiseizure properties. Significantly more participants were working at last follow-up than at PNES diagnosis (49 vs. 25%; p < 0.001).ConclusionOur study revealed a relatively favorable long-term outcome of definite PNES diagnosed in the EMU that translated in significant reductions in PNES frequency, health care utilization and ASM use, as well as a significant increase in employment rate.

Project description:ObjectiveEarly and accurate diagnosis of patients with psychogenic nonepileptic seizures (PNES) leads to appropriate treatment and improves long-term seizure prognosis. However, this is complicated by the need to record seizures to make a definitive diagnosis. Suspicion for PNES can be raised through knowledge that patients with PNES have increased somatic sensitivity and report more positive complaints on review-of-systems questionnaires (RoSQs) than patients with epileptic seizures. If the responses on the RoSQ can differentiate PNES from other seizure types, then these forms could be an early screening tool.MethodsOur dataset included all patients admitted from January 2006 to June 2016 for video-electroencephalography at UCLA. RoSQs prior to May 2015 were acquired through retrospective chart review (n=405), whereas RoSQs from subsequent patients were acquired prospectively (n=190). Controlling for sex and number of comorbidities, we used binomial regression to compare the total number of symptoms and the frequency of specific symptoms between 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 RoSQs to differentiate PNES only from ES only, we used multivariate logistic regression, controlling for sex and the number of medical comorbidities.ResultsOn average, patients with PNES or mixed PNES and ES reported more than twice as many symptoms than patients with isolated ES or PSLE (p<0.001). The prospective accuracy to differentiate PNES from ES was not significantly higher than naïve assumption that all patients had ES (76% vs 70%, p>0.1).DiscussionThis analysis of RoSQs confirms that patients with PNES with and without comorbid ES report more symptoms on a population level than patients with epilepsy or PSLE. While these differences help describe the population of patients with PNES, the consistency of RoSQ responses was neither accurate nor specific enough to be used solely as an early screening tool for PNES. Our results suggest that the RoSQ may help differentiate PNES from ES only when, based on other information, the pre-test probability of PNES is at least 50%.

Dataset Information

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

Objective

Methods

Results

Significance

Publications

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

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