Resting-state EEG theta activity reflects degree of genetic determination of the major epilepsy syndromes

Quantitative EEG analysis in typical absence seizures: unveiling spectral dynamics and entropy patterns

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.

Epileptic diathesis: An EEG-LORETA study

OBJECTIVE

Epileptic diathesis is an inherited neurophysiological trait that contributes to the development of all types of epilepsy. The amount of resting-state electroencephalography (EEG) theta activity is proportional to the degree of cortical excitability and epileptic diathesis. Our aim was to explore the amount and topographic distribution of theta activity in epilepsy groups. We hypothesized that the anatomical distribution of increased theta activity is independent of the epilepsy type.

METHODS

Patients with unmedicated idiopathic generalized epilepsy (IGE, n = 92) or focal epilepsy (FE, n = 149) and non-seizure patients with mild to moderate cerebral lesions (NONEP, n = 99) were compared to healthy controls (NC, n = 114). We analysed artifact-free EEG activity and defined multiple distributed sources of theta activity in the source space via low resolution electromagnetic tomography software. Age-corrected and Z-transformed theta values were compared across the groups.

RESULTS

The rank of increased theta activity was IGE > FE > NONEP > NC. Both epilepsy groups showed significantly more theta activity than did the NC group. Maximum theta abnormality occurred in the medial-basal prefrontal and anterior temporal cortex in both epilepsy groups.

CONCLUSIONS

We confirmed the hypothesis outlined above.

SIGNIFICANCE

The common topographical pattern of increased EEG theta activity is correlated with epileptic diathesis, regardless of the epilepsy type.

A Classification Model of EEG Signals Based on RNN-LSTM for Diagnosing Focal and Generalized Epilepsy

Epilepsy is a chronic neurological disorder caused by abnormal neuronal activity that is diagnosed visually by analyzing electroencephalography (EEG) signals.

BACKGROUND

Surgical operations are the only option for epilepsy treatment when patients are refractory to treatment, which highlights the role of classifying focal and generalized epilepsy syndrome. Therefore, developing a model to be used for diagnosing focal and generalized epilepsy automatically is important.

METHODS

A classification model based on longitudinal bipolar montage (LB), discrete wavelet transform (DWT), feature extraction techniques, and statistical analysis in feature selection for RNN combined with long short-term memory (LSTM) is proposed in this work for identifying epilepsy. Initially, normal and epileptic LB channels were decomposed into three levels, and 15 various features were extracted. The selected features were extracted from each segment of the signals and fed into LSTM for the classification approach.

RESULTS

The proposed algorithm achieved a 96.1% accuracy, a 96.8% sensitivity, and a 97.4% specificity in distinguishing normal subjects from subjects with epilepsy. This optimal model was used to analyze the channels of subjects with focal and generalized epilepsy for diagnosing purposes, relying on statistical parameters.

CONCLUSIONS

The proposed approach is promising, as it can be used to detect epilepsy with satisfactory classification performance and diagnose focal and generalized epilepsy.