Abstract:
Differentiating psychogenic nonepileptic seizures from epileptic seizures is a difficult task that requires timely recording of psychogenic events using video electroencephalography (EEG). Interpretation of video EEG to distinguish epileptic features from signal artifacts is error prone and can lead to misdiagnosis of psychogenic seizures as epileptic seizures resulting in undue stress and ineffective treatment with antiepileptic drugs. In this study, an automated surface EEG analysis was implemented to investigate differences between patients classified as having psychogenic or epileptic seizures. Surface EEG signals were grouped corresponding to the anatomical lobes of the brain (frontal, parietal, temporal, and occipital) and central coronal plane of the skull. To determine if differences were present between psychogenic and epileptic groups, magnitude squared coherence (MSC) and cross approximate entropy (C-ApEn) were used as measures of neural connectivity. MSC was computed within each neural frequency band (delta:
0.5Hz-4Hz, theta: 4-8Hz, alpha: 8-13Hz, beta: 13-30Hz, and gamma: 30-100Hz) between all brain regions. C-ApEn was computed bidirectionally between all brain regions. Independent samples t-tests were used to compare groups. The statistical analysis revealed significant differences between psychogenic and epileptic groups for both connectivity measures with the psychogenic group showing higher average connectivity. Average MSC was found to be lower for the epileptic group between the frontal/central, parietal/central, and temporal/occipital regions in the delta band and between the temporal/occipital regions in the theta band. Average C-ApEn was found to be greater for the epileptic group between the frontal/parietal, parietal/frontal, parietal/occipital, and parietal/central region pairs. These results suggest that differences in neural connectivity exist between psychogenic and epileptic patient groups.
