Aims. To evaluate the sensitivity and specificity of quantitative EEG (QEEG) spectrograms in order to distinguish epileptic from non-epileptic events. Methods. Seventeen patients with paroxysmal non-epileptic events, captured during EEG monitoring, were retrospectively assessed using QEEG spectrograms. These patients were compared to a control group of 13 consecutive patients (ages 25-60 years) with epileptic seizures of similar semiology. Assessment of raw EEG was employed as the gold standard against which epileptic and non-epileptic events were validated. QEEG spectrograms, available using Persyst 12 EEG system integration software, were each assessed with respect to their usefulness to distinguish epileptic from non-epileptic seizures. The given spectrogram was interpreted as indicating a seizure if, at the time of the clinically identified event, it showed a visually significant change from baseline. Results. Eighty-two clinically identified paroxysmal events were analysed (46 non-epileptic and 36 epileptic). The “seizure detector trend analysis” spectrogram correctly classified 33/46 (71%) non-epileptic events (no seizure indicated during a clinically identified event) vs. 29/36 (81%) epileptic seizures (seizure indicated during a clinically identified event) (p=0.013). Similarly, “rhythmicity spectrogram”, FFT spectrogram, “asymmetry relative spectrogram”, and integrated-amplitude EEG spectrogram detected 28/46 (61%), 30/46 (65%), 22/46 (48%) and 27/46 (59%) non-epileptic events vs. 27/36 (75%), 25/36 (69%), 25/36 (69%) and 27/36 (75%) epileptic events, respectively. Conclusions. High sensitivities and specificities for QEEG seizure detection analyses suggest that QEEG may have a role at the bedside to facilitate early differentiation between epileptic seizures and non-epileptic events in order to avoid unnecessary administration of antiepileptic drugs and possible iatrogenic consequences.
- psychogenic non-epileptic seizures
- quantitative EEG
- seizure detection trend
ASJC Scopus subject areas
- Clinical Neurology