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.

Factors associated with delay to video-EEG in dissociative seizures

“…While this analysis focuses specifically on delay to VEM in patients with dissociative seizures, prior to VEM, the certainty of the diagnosis of dissociative seizures was lower. Since these levels of diagnostic certainty were established, other methods have been used to risk-stratify or diagnose patients with dissociative seizures prior to inpatient VEM including, but not limited to ambulatory video-EEG, wearable activity monitors, patient-provided videos, and clinical screening scores [28,[73][74][75][76][77][78]. While none of these have been shown to be equivalent in safety and efficacy to inpatient VEM yet, they may provide alternatives when inpatient VEM is not available.…”

Biomarkers of seizure severity derived from wearable devices

“…17 Algorithms specific for GTCSs have been reported, accurately differentiating between GTCSs and convulsive psychogenic nonepileptic seizures. 18,19 However, a significant number of false alarms occur with these devices (0.2-0.7/d). 2 Therefore, the specificity of the detections needs further confirmation for accurate quantification of the GTCS burden.…”

“…Phase 3 and 4 clinical validation studies showed that wearable devices detect GTCSs with a high sensitivity (90%‐96%), based on accelerometry, 14,15 surface electromyography, 16 or multimodal biosignals 17 . Algorithms specific for GTCSs have been reported, accurately differentiating between GTCSs and convulsive psychogenic nonepileptic seizures 18,19 . However, a significant number of false alarms occur with these devices (0.2‐0.7/d) 2 .…”

A brief update on psychogenic non-epileptic seizures: a challenge to overcome

“…Some authors tried to correlate PNES with neurophysiological data. Naganur et al (2019) showed that the evolutionary pattern of the frequencies of rhythmic movement artifacts on EEG during PNES differs from that of ES. Convulsive PNES were demonstrated to display a characteristic pattern of rhythmic movement artifact that remains stable over time during the event, whereas the EEG activity during convulsive ES tends to evolve throughout.…”