AUTHOR=Guerrero-Aranda Alioth , Ramírez-Ponce Evelin , Ramos-Quezada Oscar , Paredes Omar , Guzmán-Quezada Erick , Genel-Espinoza Alejandra , Romo-Vazquez Rebeca , Vélez-Pérez Hugo TITLE=Quantitative EEG analysis in typical absence seizures: unveiling spectral dynamics and entropy patterns JOURNAL=Frontiers in Human Neuroscience VOLUME=17 YEAR=2023 URL=https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2023.1274834 DOI=10.3389/fnhum.2023.1274834 ISSN=1662-5161 ABSTRACT=

A typical absence seizure is a generalized epileptic event characterized by a sudden, brief alteration of consciousness that serves as a hallmark for various generalized epilepsy syndromes. Distinguishing between similar interictal and ictal electroencephalographic (EEG) epileptiform patterns poses a challenge. However, quantitative EEG, particularly spectral analysis focused on EEG rhythms, shows potential for differentiation. This study was designed to investigate discernible differences in EEG spectral dynamics and entropy patterns during the pre-ictal and post-ictal periods compared to the interictal state. We analyzed 20 EEG ictal patterns from 11 patients with confirmed typical absence seizures, and assessed recordings made during the pre-ictal, post-ictal, and interictal intervals. Power spectral density (PSD) was used for the quantitative analysis that focused on the delta, theta, alpha, and beta bands. In addition, we measured EEG signal regularity using approximate (ApEn) and multi-scale sample entropy (MSE). Findings demonstrate a significant increase in delta and theta power in the pre-ictal and post-ictal intervals compared to the interictal interval, especially in the posterior brain region. We also observed a notable decrease in entropy in the pre-ictal and post-ictal intervals, with a more pronounced effect in anterior brain regions. These results provide valuable information that can potentially aid in differentiating epileptiform patterns in typical absence seizures. The implications of our findings are promising for precision medicine approaches to epilepsy diagnoses and patient management. In conclusion, our quantitative analysis of EEG data suggests that PSD and entropy measures hold promise as potential biomarkers for distinguishing ictal from interictal epileptiform patterns in patients with confirmed or suspected typical absence seizures.