INTERICTAL EEG FINDINGS IN PATIENTS WITH PSYCHOGENIC NON EPILEPTIC SEIZURES

Abstract

Background: Psychogenic non-epileptic seizures (PNES) are a major diagnostic challenge as they are clinically indistinguishable from epilepsy, leading to a high rate of incorrect diagnosis and inappropriate treatment protocols. Interictal electroencephalography (EEG) has been postulated as a possible non-invasive discriminator, and yet comparative data in resource limited settings are yet to be available. The present study attempted to compare interictal EEG results in PNES and epileptic patients for enhanced diagnostic precision.

Methods: The Department of Neurology, Pak Emirates Military Hospital, Rawalpindi, Pakistan, was carried out a comparative cross-sectional study between January 2025 and June 2025. There were 100 adult patients (50 PNES and 50 epileptic patients) who were consecutively sampled. Diagnostics were verified by video-EEG monitoring according to International League Against Epilepsy guidelines. Interictal EEG (at least 30 minutes duration) was evaluated for abnormalities (epileptiform abnormalities, slowing, asymmetry) by two blinded neurophysiologists. IBM SPSS version 23 was used for data analysis, and categorical comparisons were done by chi-square tests and diagnostic performance was determined by receiver operating characteristic (ROC) analysis.

Results: Abnormal interictal EEG results were significantly higher in epilepsy (66%) compared to PNES (30%; p < 0.001). Epileptiform patterns differed considerably: epileptiform discharges were the leading pattern in epilepsy (81.8% of abnormal recordings), whereas slowing was most common in PNES (80%). ROC curve provided an area under the curve of 0.68 (95% CI: 0.57-0.79), 66% sensitivity and 70% specificity for differentiating between epilepsy and PNES.

Conclusion: Interictal EEG presents characteristic differences between PNES and epilepsy, justifying its use as a complementary diagnostic tool in resource-constrained environments where video-EEG may be lacking. These findings advocate for integrated EEG evaluation to reduce misdiagnosis and optimize patient outcomes.