ArticleCureus2026
Differential Semiology in Video-EEG Monitoring: A Clinical Approach to Distinguishing Psychogenic Nonepileptic and Epileptic Seizures.
Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
Introduction Distinguishing psychogenic nonepileptic seizures (PNES) from epileptic seizures (ES) remains a major diagnostic challenge in clinical neurology. Misdiagnosis is common and may result in inappropriate treatment, including unnecessary use of antiepileptic drugs and delayed psychiatric care. Video-electroencephalographic (video-EEG) monitoring is considered the gold standard for diagnosis, but it is not universally available. Semiological features, therefore, remain an essential diagnostic tool, particularly in resource-limited settings. Methods This retrospective observational study included 43 patients who underwent video-EEG monitoring at the Clinic of Neurology, University Clinical Hospital Mostar, between May 2022 and May 2025. Patients were classified into ES and PNES groups based on video-EEG findings. Multiple clinical and semiological features were analyzed. Statistical significance was defined as p < 0.05. Results PNES were significantly associated with seizure duration >2 minutes (71.4% vs. 10.3%, p < 0.001), gradual onset, preserved responsiveness, eye closure with resistance to passive opening, and fluctuating course. ES were characterized by sudden onset, shorter duration, impaired responsiveness, and head deviation. The combination of these semiological features showed clear discriminatory value in differentiating PNES from ES. Conclusion Semiological analysis represents a valuable and accessible diagnostic tool in differentiating PNES from ES. Its systematic application may improve early diagnostic accuracy, reduce diagnostic delay, and optimize patient management, particularly in settings with limited access to video-EEG monitoring.
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