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

• Published in: Epilepsia open 2019-06, Vol.4 (2), p.309-317
• Main Authors: Naganur, Vaidehi D., Kusmakar, Shitanshu, Chen, Zhibin, Palaniswami, Marimuthu S., Kwan, Patrick, O'Brien, Terence J.
• Format: Article
• Language: English
• Subjects: Accelerometers; accelerometry; Algorithms; ambulatory; Automation; Convulsions & seizures; Epilepsy; False alarms; FDA approval; Full‐length Original Research; Microelectromechanical systems; Patients; psychogenic non‐epileptic seizures; Sensors; Studies; Wrist
• ISSN: 2470-9239; 2470-9239
• DOI: 10.1002/epi4.12327

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.