DOI: 10.1093/braincomms/fcad330 ISSN: 2632-1297

Limited clinical validity of univariate resting-state EEG markers for classifying seizure disorders

Irene Faiman, Rachel Sparks, Joel S Winston, Franz Brunnhuber, Naima Ciulini, Allan H Young, Paul Shotbolt
  • Neurology
  • Cellular and Molecular Neuroscience
  • Biological Psychiatry
  • Psychiatry and Mental health


Differentiating between epilepsy and psychogenic nonepileptic seizures (PNES) presents a considerable challenge in clinical practice, resulting in frequent misdiagnosis, unnecessary treatment, and long diagnostic delays. Quantitative markers extracted from resting-state electroencephalogram (EEG) may reveal subtle neurophysiological differences that are diagnostically relevant.

Two observational, retrospective diagnostic accuracy studies were performed to test the clinical validity of univariate resting-state EEG markers for the differential diagnosis of epilepsy and PNES. Clinical EEG data were collected for 179 quasi-consecutive patients (age >18) with a suspected diagnosis of epilepsy or PNES who were medication-naïve at the time of EEG; 148 age- and gender-matched patients subsequently received a diagnosis from specialist clinicians and were included in the analyses. Study 1 is a hypothesis-driven study testing the ability of theta power and peak alpha frequency to classify people with epilepsy and people with PNES, with an advanced machine learning pipeline. The next study (Study 2) is data-driven; a high number of quantitative EEG features are extracted and a similar machine learning approach as Study 1 assesses whether previously unexplored univariate EEG measures show promise as diagnostic markers.

The results of Study 1 suggest that EEG markers that were previously identified as promising diagnostic indicators (i.e., theta power and peak alpha frequency) have limited clinical validity for the classification of epilepsy and PNES (mean accuracy: 48%). The results of Study 2 indicate that identifying univariate markers that show good correlation with a categorical diagnostic label is challenging (mean accuracy: 45-60%). This is due to a considerable overlap in neurophysiological features between the diagnostic classes considered in this study, and to the presence of more dominant EEG dynamics such as alterations due to temporal proximity to epileptiform discharges.

Markers that were identified in the context of previous epilepsy research using visually normal resting-state EEG were found to have limited clinical validity for the classification task of distinguishing between people with epilepsy and people with PNES. A search for alternative diagnostic markers uncovered the challenges involved and generated recommendations for further research.

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