The diagnosis of epilepsy and the identification of seizures are subject to multiple challenges. Hence, it is relevant to identify EEG features that allow for differentiation between seizure intervals and between patients and healthy subjects. Several studies have explored the search for biomarkers by utilizing signal processing techniques, although some of these studies have been applied to limited datasets. In this study, a set of seven univariate features in the time and frequency domains was calculated for scalp EEG recordings. These recordings were collected from four EEG datasets (patients=180, controls=100, seizures=613), and two types of experiments were performed: a comparison between healthy subjects and the pre-ictal (one min before seizure onset) and ictal intervals, and a comparison between seizure stages (pre-ictal, ictal and post-ictal). Variations in the normalized power spectral density were the most reliable indicator of seizure activity (p-value<0.05). Mobility, complexity, and approximate entropy also changed significantly, with entropy-based measurements decreasing during seizures, indicating a reduction in EEG irregularity (p-value<0.05). The results highlighted the importance of combining spectral, statistical, and entropy-based features for a more comprehensive understanding of seizures. Although some common patterns were identified, distinct behaviors were observed between datasets. Future work will benefit from a diverse and curated dataset; therefore, causes of dissimilarities can be unequivocally identified.
Sánchez-Hernández et al. (Mon,) studied this question.